Degradation Science: Mesoscopic Evolution and Temporal Analytics of PhotovoltaicEnergy Materials

Roger H. Frencha, Rudolf Podgornikb, Timothy J. Pesheka, Laura S. Bruckmana, Yifan Xuc, Jiayang Sunc, G. Q. Zhangd, NicholasR. Wheelera, Abdulkerim Goka, Yang Hua, Mohammed A. Hossaina, Devin A. Gordona, Pei Zhaoa

aSolar Durability and Lifetime Extension Center, Materials Science, Case Western Reserve University, Cleveland OH, 44106bRudi’s Affiliation

cEpidemiology and Biostatistics Department, Case Western Reserve University, Cleveland OH, 44106dG.Q. Affiliation

Abstract

Keywords: Photovoltaics, Degradation, Mesoscopic Evolution, Real-world, Data Science, Epidemiology

1. Introduction

Energy materials comprise a significant component of ourmodern world; these systems have useful lifetimes that extendfrom 25 to greater than 50 years. These long lifetimes and thelarge investments in energy producing technologies are barriersthat novel energy materials must surmount to provide a sub-stantial proportion of global energy. These challenges were ap-parent from the Li-ion batteries in the Boeing 787 which werepredicted to vent smoke without fire due to internal short circuitonly once per 10 million flight hours, [1, 2] yet the real-worldexperience was 3 orders of magnitude greater than this estimatewith two adverse events grounding the whole fleet within fourmonths of introduction. We do not know how to address thefundamental science of real energy materials under real-worldconditions and over real-world time spans, unlike the materialscience of synthesis, properties and function of energy materi-als.

Photovoltaics (PV), first developed thirty five years ago, arenow seeing widespread global adoption due in part to 25 yearwarranties on commercial products. The first 5 MW PV powerplant, developed in the 1980’s as part of the DOE Block GrantProgram, was predicted to have a 20 year lifetime. The sitepower decreased 10X faster than the predicted rate and thefailed plant was decommissioned after 5 years. [3] This historyof degradation-induced failures has been an ongoing character-istic of new and promising PV cells.

The science of degradation of energy materials over timeframes longer than >1 gigasecond (31.7 years) is a fundamentalchallenge of mesoscale science, [4] and a transformational op-portunity for energy science. It has similarities to the data andmodeling challenges in medicine, sociology or climate science,where models and observational data together form an insep-arable basis for scientific understanding and prediction. [5, 6]Degradation of energy materials evolves over long timeframesdue to many distinct mechanisms which leads to a variety ofboth slow and rare events that eventually cause degradation andfailure. The challenge is to identify, model, and predict the

mesoscopic evolution over time scales that produce degrada-tion, while monitoring the dynamic processes that arise in theirfunctional use and typically occur on daily time frames. It isessential to connect the mechanistic degradation pathways, andtheir time evolution, with the mesoscopic science that wouldenable the identification of improved and longer lived energymaterials.

1.1. Advances in nanoscience and computation

Since the National Nanotechnology Initiative in 2000, [7]there have been tremendous advances including detailed under-standing of science at the nanometer scale and at the femto- toattosecond scale. Fundamental advances in glasses, nanoscalematerials, interactions,[8] fabrication and assembly, [9, 10], andsystems [11, 12, 13, 14] have ensued. We now have more de-tailed scientific understanding of the basic nanoscience of en-ergy materials that underpin their beneficial function and pro-vide a insights into their degradation.

An important part of nanoscience has been the research oncomputational materials science [15] and multi-scale modelswhich connects microscopic/atomistic mechanisms with higherlevel, coarse grained, mesoscopic and even macroscopic mod-els [16, 17, 18, 19, 20] so as to understand the fundamentalorigins of the physical properties of materials. This multi-scalemodeling research has focused on spanning length scales un-derpinning the effective parameter passing approach, to link theatomic scale behavior to the experimentally determined macro-scopic properties. The Materials Genome Initiative [21, 22] isan example of this focus on accelerating materials discovery bycomputational modeling at the micro- and mesoscopic levels soas to guide the experimental synthesis of new materials. [23]

Still, even with the advances to date in multi-scale modeling,we are not able to span from femto- to gigaseconds in order toprovide fundamental guidance on the microscopic mechanismsthat control the mesoscopic evolution of materials and resultin the long term degradation in function and properties. Forthis challenge, we focus on a different approach which starts

Preprint submitted to Current Opinion in Solid State and Materials Science November 19, 2014

with the availability of data from large and diverse experiments.This varied datasets provide access to enough information tofit appropriate physical and statistical models and identify thefundamental mechanisms of energy material degradation whichare then incorporated into a network model of their mesoscaleevolution over lifetime. As these two fields advance, they willcontinue to provide mutual benefit, illuminating fundamentalsand identifying critical contributors and effects.

The tremendous advances in computation and communica-tions, [24] and open access, code and data [25, 26, 27] over thepast ten years are a second building block for the opportunitiesin degradation science. With the advent of distributed comput-ing [28, 29, 30], advances in internet connectivity and mobility,coupled to the ubiquity of sensors, there are unprecedented bigdata streams, which can be utilized for experimental studies ofenergy materials both in the laboratory or in real-world condi-tions. These advances have driven the field of data science awayfrom the mere computational advances, which have been moretypically the focus of science (e.g. high performance comput-ing). With the increase of data volumes and variety, and theassociated advances in informatics for petabyte scale analysis,it is now possible to study large populations of real (as opposedto idealized or simplified) energy materials under real-worldconditions and over very long time frames. These epidemio-logical or population-based studies as used in public health andsociology, can complement our traditional small sample size,laboratory-based experiments, providing additional statisticallysound information to bridge the 24 orders of magnitude in timerequired for femto- to gigasecond science.

1.2. Temporal evolution of mesoscopic energy materialsThe Materials Genome Initiative and advances in

nanoscience have allowed nascent energy materials to bedeveloped. However a predictive framework for materialsproperties over time in their real world applications is lacking.For example there is a vast amount of research on new batteriesand improved storage capacity for applications in electronics,transportation and grid.[31] Yet we still don’t know the fun-damental science of degradation, the contributing factors thatlimit the number of charge cycles, nor the basic mechanismsand pathways that lead battery end-of-life.[32] Similar resultsare seen in XXX and YYY, where we lack scientific funda-mentals of the degradation mechanism pathways, leading topremature failure in the real-world conditions.

Functional energy materials are complex materials withhomo- and heterogeneous interfaces, and with substantialvariances among samples in a population. They are non-equilibrium systems, with cyclic operating conditions and stres-sors by virtue of their energy function; and are thus have highspatio-temporal complexity. For energy applications spanningthe time domain from femto- to gigaseconds the span of scalesrequires a new approach for information passing between thescales, that can distinguish the large dynamics of function fromthe slow/rare events of degradation, as well as between differentregimes of both, the material and in the environment. For exam-ple damage initiation, accumulation and growth, will eventuallylead to a transition such as a sudden precipitation or bifurcation

into a new regime. Similarly environmental conditions, as en-countered in permafrost and/or desert or given by daily and/orannual cycles of the seasons, can all produce results quantita-tively different than a well controlled laboratory-based study.To understand their degradation significance, all of these het-erogeneous aspects of materials and devices, across populationand across characteristic time scales, need to be accessible toscientific inquiry. The focus of this research is the develop-ment of mesoscopic-evolution network models which integratephysical and statistical models of mechanisms and phenomena.These network model link micro- and mesoscopic degradationin order to understand the stress/mechanisms/responses of thePVs in real-world use over their lifetime.

Figure 1: Caption.

2. Current photovoltaic energy materials research ap-proaches

Three distinct communities of scientists and engineers haveworked in research and development of energy materials fordistinct goals. Scientists typically pursue laboratory-based re-search topics related to material performance. Engineers seekstandards to guide product development and safety of holisticsystems. Finally, owners and operators are most concerned withthe real-world performance and return on investment. Thesecommunities overlap and interact, but their fundamental ap-proaches have hindered the development and permeation of atrue scientific foundation and culture across the new energy ma-terials community.

2.1. Laboratory-based studiesLaboratory-based studies of degradation in PV energy mate-

rials have typically focused on a specific observed failure modeand sought to accelerate the presentation of that mode. Thismethodology has been used in potential-induced degradation[33], however, no physical modeling for insights to causes areprovided. Novoa et al. [34] examined the bonding strengthof PV module backsheets to comparable materials in a sim-ulated PV module in laboratory accelerated aging conditions.Peike et al. [35] studied the effect of the system interactions onthe power performance of a solar cell when laminated in var-ious structures. These studies tended to study distinct modesobserved in the accelerated aging of PV energy material fromindustry testing standards; however, there is little correlationof this data with real-world observations and few insights thatare extrapolated to use conditions. Koehl et al. [36] does tiereal-world climatic data in data-driven modeling of accelerated

2

aging conditions based upon moisture content and hydrolysisreaction rates. Researchers have employed physical models ofmoisture ingress and diffusion into real materials and systemsto estimate water concentration [? ].

2.2. Standards-based testing

Internationally-recognized standards for certifying the per-formance and safety of commercially available photovoltaicproducts is in existence. However, several high-profile firesand other non-conformances have been well-documented [? ].Standardized testing is laboratory-based testing of short du-ration, usually 3-4 months, with binary (pass/fail) outcomes.These tests are designed to assure a uniformity of quality atmanufacture, although the tests are widely believed to providerapid means of detection the known failures or degradationmodes for a products in its intended environment. This state-ment is highly relevant to the discussion here because the fail-ure and degradation modes must be known fully, i.e. there isno predictive quality to these tests and there is an implicit link-age to the end-use environment. An example of these standardsbased test procedures for crystalline silicon PV is IEC 61215[37]. Due to the binary nature of the results problems identi-fied by tests can not include corrective action insights and aresubsequently learning limited.

2.3. Real-world studies

An epidemiological approach to data collection and analyt-ics have only recently started being applied in real-world stud-ies of PV systems.[38, 39, 40]. Typically the published real-world studies were used to monitor and model performance atcoarse time scales or determine average degradation rates overlong periods of time. In these longitudinal studies as little as 1data point per month was collected over time and simple lineardegradation trend was fitted and compiled for sites around theglobe. Jordan and Kurtz [41] measured real-world performanceof over 2000 power plants to obtain a mean linear model ofPV module degradation rate equal to 0.5 %/year and comparedthese results to other studies. However a set of modules ex-posed to hot/dry conditions underwent mean degradation ratesof 1.5 %/year [42]. These approaches are quite different fromthe approaches used in climate science, where statistical analy-sis is applied to all variables of the system [43] and statisticallysignificant relationships are identified. [44]

3. Degradation science: formulating mesoscopic evolutionmodels for energy materials

The challenge for adoption of degradation science as thefoundation of energy materials from the laboratory to the realworld, is not cultural, but scientific. The basic science hasyet to be developed that would span the time domains, frommechanisms to lifetime, and provide an integrated mesoscopicevolution modeling methodology which encompasses the ac-tive mechanisms and determines the performance, safety andfunction of these energy materials over diverse real-world con-ditions. Integrating laboratory-based and real-world studies of

energy materials is both a prerequisite for developing a fullmesoscopic evolution science and will bring deeper understand-ing of the research findings integrating the scientists, engineersand operators of our essential energy materials and infrastruc-ture. Bringing these approaches, cultures and communities to-gether will ease the invention, implementation and penetrationof new energy materials in the real world.

The data acquisition strategies must also balance the relevantscales and volumes of the datasets to be acquired and used in thephysical and statistical modeling. Approaches for extraction ofthe necessary information, while disregarding spurious infor-mation, so as to develop a network of models for each of theactive mechanisms related to each degradation pathway on themesoscopic physical level and the data-driven statistical modellevel are essential. To capture the temporal evolution of theenergy material over long time frames, appropriate informaticsmethods are needed to balance data volume (e.g., simple uni-variate time-series data streams with large volumetric imagingdatasets) while considering their respective information con-tents. [45]. In addition, the data and extracted information mustbe accessible for query and modeling. Similarly the modelingapproaches used to understand and parameterize active mech-anisms and phenomena over lifetime, fall into the broad cate-gories of micro-, meso- and macroscopic approaches. All ofthese aspects of laboratory-based and real-world experimenta-tion, informatics, analytics and development of network modelsfor mesoscopic evolution of energy materials over lifetime con-stitute the field of degradation science.

3.1. Challenge: integrate laboratory-based and real-worldstudies and data

Laboratory-based experiments include the effects of multi-ple stressors, experimentally controlled multiple mechanisms,expected active mechanisms and multiple responses criticalto lifetime performance for the deterministic modeling ofmesoscale evolution. The stressors of the real world are not ex-perimentally controlled, must be actively monitored and theircumulative effect over time must be explicitly integrated. Inthese observational real-world studies, the active degradationmechanisms may include unexpected yet significant mecha-nisms. The systems under study typically have strong hetero-geneity, with large variances that increase over time among di-verse evolutionary paths and modeled often using stochastic as-sumptions.

In the planning phase, the number and types of repetition canbe determined or estimated using results from a pilot study orhistorical similar studies to reduce over- or under-sampling andensure sufficient statistical power. The makeup of a real-worldpopulation-based study follows similar principles, but due to itsobservational nature and the existence of uncontrolled environ-mental stressors, special attention is needed to faithfully recordall related variables for follow up studies.

3.1.1. Stress/Mechanism/Response FrameworkTo target a full library of degradation mechanisms and path-

ways and ensure the sufficient sampling and data acquisition

3

Figure 2: Caption.

for both real-world and Laboratory-based experiments, thor-ough planning of the sample populations, evaluations and datas-treams to maximize the information yield is required. An effi-cient statistical planning has attributes that allow the study toavoid bias and ensure reproducibility. Thus laboratory-basedexperiments are designed to utilize analytical tooling to uncovermicroscopic mechanisms of response to an applied stressor orstressors.

Experiments are prospective randomized comparative studiesthat evaluate effects of different stressors conditions on differ-ent material types. Based on the objectives of the experiments,the nature of measurement method and prior knowledge of theanalytic form of the degradation processes, the laboratory ex-periments can utilize repeated measurement design or endpointdesign. To elucidate the library of possible degradation mecha-nisms, a wealth of datatypes and variables are selected from thedatastreams, and these variables, which we denote as stress ormechanism or response, are modeled (e.g., pairwise using do-main knowledge or purely data-driven exploratory fitting). Ul-timately the material degradation library comprises a networkof sub-models, both physical and statistical, and ultimately asystem of multivariate equations for response prediction givenapplied multifactor, sequential or cyclic stressors. [46]

RFNotes: These perspectives impact both laboratory-basedand real-world studies. For example in the laboratory, multi-ple samples studied through time under accelerated conditions,with a retained sample library to enable retrospective study af-ter the fact, when new insights arise. The retained sample li-brary samples can be the basis of future, previously undefined,studies.

3.1.2. Informatics and ontology for data integration, assemblyand query - [GQ]

RFNotes: An informatics and analytics environment to en-able data assembly, data re-use, query, exploratory data anal-ysis for development of physical and statistical models, initialregression of these mesoscopic evolution models, along withtheir ongoing refinement as new data is acquired over time. En-abling pilot studies, and full studies as new data comes in. Within-place analytics

Storing, Archiving and Retrieving Large Materials Datasets,[47]

RFNotes: Data science and analytics. Applicable to labora-tory and real-world studies. CRADLE = Common Research

Figure 3: Caption.

Analytics and Data Lifecycle Environment. Informatics andontology. Data acquisition and ingestion. Annotation, clean-ing, munging. Data assembly. Utilize existing data in futurestudies. RFNotes: An informatics and analytics environment toenable data assembly, data re-use, query, exploratory data anal-ysis for development of physical and statistical models, initialregression of these mesoscopic evolution models, along withtheir ongoing refinement as new data is acquired over time. En-abling pilot studies, and full studies as new data comes in. Within-place analytics RFnotes: Diverse source of data. Datasets ac-quired can be utilized for many different studies, if real-worldsystems produce deep data RFnotes: Use self-reporting tech-nologies. Add De Novo data sources. Such as PV Electric TimeSeries. To Get Power Degradation Rates RD

3.2. Physical and statistical sub-models

3.2.1. Physical modeling - [Rudi]Physical models usually assume upwards progression either

in the space-time scales and/or in the nature of implied coarsegraining. The latter is unavoidable since a complete atom-istic/quantum description on a microscopic level is usually notrealistic, and mesoscopic or even macroscopic physical mod-els are unavoidable. The range of applicability of these mod-els is limited by the extent of coarse graining that is impliedand they depend on experimentally determinable parametersand relations to greater extent, as in: - microscopic physicalmodels specifically refer to atomic/molecular aggregate mech-anisms describable by quantum-electrodynamic Hamiltoniansas functions of quantum variables solved at a finite tempera-ture, and have in principle no implied coarse graining and nofree parameters. Examples: quantum theory of the photovoltaiceffect starting from a microstructural lattice-electron descrip-tion of a semiconductor device or density functional theory ofoptical spectra of materials [48]. - mesoscopic physical mod-els imply first level of coarse graining described with Hamil-tonians that do not depend anymore on quantum variables buton coarse grained order parameters with assumed symmetries,coupled with phenomenological constants that only in principledepend on microstructural parameters [49]. Example: contin-uum description of various ordering phenomena (liquid-solid

4

phase transition) with Landau-type order parameter theories[50] or the continuum description of structural defects (dis-locations and disclinations in crystals) in mesoscopic order-ing [51]. These models can be seldom reduced to some fea-tures of the micro-world description. - macroscopic physicalmodels depend on macroscopic variables and their connectionwith micro-world of quantum variables or even meso-worldof order parameters is very tenuous. They imply even morecoarse graining, losing completely the connection with micro-or meso-structural variables and are more based on experimentsand statistical data then on micro- or mesoscopic considera-tions. Example: Navier-Stokes hydrodynamics [52], reaction-diffusion systems [53], flocking phenomena in birds [54] or epi-demiological models in population dynamics [55]. These mod-els are not even in principle reducible to meso- or microscopicunderlying descriptions.

Defects and disorder are concepts that arise naturally withgrowing coarse graining of the meso- and/or macrostructuralmodel description [56]. While thermal disorder leading to ther-mal fluctuations and entropy variation is ever present, structuraldefects and disorder are usually imposed by external constraintsor non-equilibrium driving mechanisms. Specifically, in thecontext of aging and degradation mechanisms of energy ma-terials, externally controlled or uncontrolled stressors introducestructural defects that can either couple to the thermal fluctu-ations (annealed disorder) or remain independent of the ther-mal noise (quenched disorder) [57]. Both, however, introducedegradation mechanisms that lead to a progressive disorderingof the material eventually completely destroying its function-ality. Examples of quenched disorder model include photo-oxidation yellowing of the PV energy materials, where defectsare chemical and essentially immobile in nature, progressivelyaccumulating under stressor conditions leading to degradationirrespective of the thermal fluctuations. Hazing of polymericmaterials would be an example of annealed disorder, wherestressor generated microscopic defects interact with the matrixtaking advantage of the underlying thermal fluctuations to mi-grate and nucleate into macroscopic disorder degrading the ma-terials functionality.

3.2.2. Statistical modeling - [Jiayang]Longitudinal studies utilizing repeated measure design with

retainment can be used to test and model univariate degrada-tion. In such studies subjects are followed over time with re-peated monitoring of outcome measure. In addition, one ormore subjects are retained from further exposure at each timeepoch within each subgroup for future investigation. Two goalsof such studies are: 1) to model patterns of changes in outcomemeasure over time under certain exposure types, such as de-termining the shapes of degradation curves (linear, quadratic,exponential, change-point, etc) and estimate corresponding pa-rameters. 2) To compare effects of either exposure type ormaterial type on the outcome measure. Here the error struc-ture is best characterized with a mixed effect model, wherefixed effects being exposure types, material types and exposuretime, and random effects capture potential subject differences.Mixed effect models are also ideal to handle missing values

due to retainment. Parameters in mixed effect models are fittedby restricted maximum likelihood estimation. Model selection(functional forms) can be done by comparing AIC, aided withphysical interpretation.

Furthermore, semi-gSEM can be used to model pathwaysfrom exposure time to system response via intermediate vari-ables under different exposure types. Data from an real worldexperiment involve many uncontrolled environmental stressors(independent variables, covariates, predictors). Careful and ex-tensive data cleaning and segmentation during EDA are essen-tial before modeling attempt. Clustering techniques and visual-ization of data are helpful tools in this step. Time series regres-sion models can be used as a parametric model in each subgroupof data that show coherent pattern. Given the large number ofobservations, a nonparametric modeling approach such as ran-dom forest is also appropriate. Random forest also gives a nat-ural ranking of the importance of stressors to system responses.

3.3. Mesoscopic evolution models: networks of sub-models

Linking physical and statistical models using torrential real-time data streams is among the major theoretical challenges fac-ing predictive capabilities of the degradation science. In thisrespect there exist definite similarities with modelling of cli-mate processes (A Vast Machine [5]), modelling of social pro-cesses (The Living Earth Simulator [58]) or indeed any othertype of stochastic, disordered, complex, multilevel phenomenathat do not yield easily to prevalent reductionist paradigms inscience. Even multi-scale modelling, including recent coarse-grained simulations of biophysical and chemical systems [18][[[[we should have more cites.]]]], can not be seen as a guid-ing paradigm for material degradation because it is driven ex-clusively by physical models with well defined and controlledstressors, leading to effective parameter passing approach tolink the microscale with macroscale properties.

Mesoscopic generalized Structural Equation Models, semi-supervised by domain knowledge (semi-gSEM), could con-nect the mesoscopic evolution models, based on SEM theoryof statistics, epidemiology and mathematical sociology, to thephysical and chemical mechanistic models. In this way wetake simultaneous advantage of the SEMs exploratory and con-firmatory statistical models, elucidating statistically significantrelationships in complex systems with both measured and la-tent variables, as well as of the intermittent micro-, meso-and macroscopic models with internal mechanistic variables,whenever they are applicable or indeed existent, capturing re-lationships/couplings among all the variables available fromlaboratory-based and real-world experiments.

The black swan: the impact of the highly improbable [59]Anti-fragile [60] Mechanochemical strengthening of a syntheticpolymer in response to typically destructive shear forces [61]

Stochastic Simulation of Chemical Kinetics [62]

5

4. Degradation science approach exemplified

4.1. Stress, mechanism, response framework

4.2. Informatics and analytics- Yang,Pei

In order to enable the studies of mesoscopic evolution us-ing temporal analytics, an infrastructure encompassing thelaboratory-based controlled stressors and the real-world uncon-trolled stressors, allowing the researchers in different field fordata sharing and analyzing is critical. Common Research Ana-lytics and Data Lifecycle Environment (CRADLE) serves as theplatform of data acquisition, data storage, data processing anddata presentation, enabled physical and statistical model devel-opment and application with a focus on mesoscopic evolutionand temporal analytics.

4.2.1. Energy-CRADLE data lifecycle environmentEnergy-CRADLE as the first example of CRADLE infras-

tructure focused on handling the PV system and environmentalmonitoring data time series data stream from the SunFarm net-work and laboratory-based PV material testing data.

All the SunFarms are equipped with minute-by-minute PVpower data and environmental data monitoring instruments,and live data stream feed beack to SDLE center. Laboratory-based data are typically spectrum, and images. There are about200GB of data being generated each year. The nature of thedata indicate it is a typical big data problem: high volume, ve-locity, and variety.

Compared with traditional way of using MySQL database tostore and retrieve data, the way that Energy CRADLE leveragesHadoop and HBase, is showing significantly better performanceon data processing and data extraction. (Illustration of figurewill be added).

E-CRADLE 2, as a upgrade to E-CRADLE 1, is currentlyunder development, and will provide more powerful features toresearchers and developers. E-CRADLE 2 is based on Cloud-era Express, as a combination of CDH and Cloudera Manager.By leveraging Cloudera Express, E-CRADLE would providebatch processing, interactive SQL, interactive search, and sup-port of multiple programming language, such as Hive, Pig toprocess data. Beyond Cloudera Express, RHadoop, a collec-tion of R packages that allow researchers to manage and ana-lyze data on a Hadoop cluster, which is called in-place analysis.(Citation/links will be added)

processing.

4.2.2. Temporal analytics for physical and statistical modeling:semi-gSEM- Laura/Yifan

4.3. laboratory-based experiments with controlled stressors

4.3.1. Photodegradation of acrylic-Nick/AbdulAcrylic polymer, or poly(methyl methacrylate), is widely

used in photovoltaic technologies that reflect and concentratelight such as back-surface mirrors and fresnel lenses. Althoughthese roles require the material to withstand harsh outdoor con-ditions including harmful UV radiation and other stressors,acrylic is susceptible to photo-, thermal and chemical degra-dation which can manifest as unwanted changes in optical and

mechanical properties. To ward against these effects, acrylicmaterials are often compounded with additives that stabilize thepolymer temporarily, but these protective chemicals are them-selves vulnerable to depletion over time. In this way, successivedegradative stages in acrylic photovoltaic energy materials canhappen slowly over the course of many years, making acceler-ated laboratory-based studies the key to their timely elucidation.

Laboratory-based photodegradation studies of both unstabi-lized acrylic materials and those compounded with Tinuvin UVabsorber have shown reciprocity in response to identical dosesacross a range of irradiance levels (1X, 5X, 50X), confirmingthat the active degradation mechanisms remain constant inde-pendent of irradiance intensity. However, spectral effects areobserved when contrasting full spectrum and UV only irradi-ance sources - similar active degradation mechanisms are ob-served in the UV region response (changes in the fundamen-tal absorption edge due to chain scission, and UV absorberbleaching), but yellowing progresses much faster in response tothe full spectral exposure due to additional photodarkening ofthe yellowing compounds themselves. Data-based semi-gSEMmodeling of these study results give insights into the interre-lated mechanisms at play - changes in the fundamental absorp-tion edge due to chain scission are strongly associated with theyellowing response. This effect decreases 10X with the ad-dition of the Tinuvin stabilizer present in MP grade, with thefundamental mechanism of photobleaching of the Tinuvin UVabsorber determining the Tinuvin consumption rate.

Figure 4: Caption.

4.3.2. Photodegradation and hydrolysis of polyester-Abdul/Devin

Polyethylene-terephthalate (PET) is one of the most criticalcomponents in photovoltaic backsheets due to its high dielec-tric strength but its very susceptible to moisture and ultraviolet(UV) light. Failures such as cracking and delamination causedby weather degradation may result not just in performance lossof modules but also in electrical insulation breakdown. PETdegradation mainly occurs via photolytic and hydrolytic cleav-age of an ester bond and results in discoloration and/or haz-ing, decreased molecular weight, increased crystallinity, lossof mechanical and hence dielectric properties. Photodegrada-tion and/or photo-oxidation mainly proceeds via Norrish type-Ior Norrish type-II reactions that determine further degradation

6

routes. Hydrolysis mechanisms are more complex when chem-ical reactions like autocatalysis due to active carboxylic acidend groups are taken into account. Various kinetic models suchas non-autocatalytic random chain scission, half-order autocat-alytic, first order and second order kinetics on the moisture con-tent have been proposed. [63, 64, 65, 66, 67]

For demonstration of photodegradation, i.e., yellowing un-der UV irradiance, and hydrolysis i.e., moisture induced hazingand embrittlement, unstabilized PET samples were exposed tofour different laboratory-based accelerated conditions: contin-uous UVA at 1.55 W/m2 at 340 nm at 70 C, cyclic UVA at1.55 W/m2 at 340 nm at 70 C plus condensing humidity at 50C with no irradiance (ASTM G154 Cycle 4), IEC61215 dampheat (85 C and 85 %RH), and modified IEC61215 humidityfreeze (70 C/85 %RH and -40 C). Figure 5 summarizes resultsas yellowing is predominant with exposures consisting of UVAlight irradiance (a) while hazing is mostly caused by high levelof moisture content (b). Nanoindentation & hardness measure-ments show that as haze development increases embrittlementresults in loss of mechanical properties and physical integrityof the material, including its dielectric strength (c).

Figure 5: Caption.

4.3.3. Photo- and hydrolytic degradation of PV cells-TimPolycrystalline silicon solar cells, fabricated by Q-Cells were

exposed to 4 hours condensing humidity, and 8 hours of UVA340 5X irradiance, and 70C temperature, with a periodicity of12 hours in accordance with ASTM G154. These cells are acomponent of the conventional 60-cell solar modules that arecommercially available, and were exposed directly with no en-capsulation protection.

The observed response was power loss as evidenced by theI-V characteristics. These data can be mapped onto a physicalmodel of the solar cell device using an equivalent circuit de-piction containing a diode to represent the photoactive pn junc-tion and parallel and series resistors to represent losses. Thissimplistic model mathematically predicts the I-V characteristicswell, and allows for parametrization of the curve for enhancedinformation extraction and mesoscopic insights to performanceloss. In this way the results indicated an increase in the solarcell series resistance that suggests degradation is caused by in-creased losses at the semiconductor-metal junction.

Optical analysis of the silver grid that constitutes the frontcontact of the cell indicated corrosion related oxide nanopar-ticle formation as evidenced by a decay in the optical secondharmonic generation signal.

Figure 6: Caption.

4.3.4. Photo, thermal and hydrolytic degradation of PVmodules-Nick/Laura

Crystalline silicon PV modules were exposed to two stan-dard accelerated stress conditions: IEC?? UV precondition andIEC61215 damp heat. The models exposed to the UV pre-conditioning did not have a measurable change in the perfor-mance metric over the time span of the laboratory-based expo-sure which suggests that the stress level was inadequate. Thismodel included UV irradiance and heat? as stressors.

Two degradation pathways were activated: PET embrittle-ment and EVA Hydrolysis based the measured data that relatesto a mechanistic variable. Degradation Pathway 1- PET Hydrol-ysis leading to embrittlement. Degradation Pathway 2- EVAHydrolysis leading to Acetic Acid production and power loss.Pathway two has a number of steps to it, including EVA sidechain scission, Acetic acid formation, metal corrosion (not di-rectly measured) ending in the response of power loss. Didntnecessarily scope how these 2 pathways relate to one another,further development.

Crystalline silicon (c-si) PV modules contain many PV mate-rials that degrade differently in response to environmental stres-sors over long time scales in a highly interrelated manner. Realworld use conditions for these PV systems feature a varietyof significantly impactful stressors, like harmful wavelengthsof solar radiation, widely fluctuating moisture and temperatureconditions, exposure to corrosive chemicals, mechanical im-pacts and vibrations, and more. Understanding the interplayof mechanistic degradation processes that unfold in responseto these environmental stressors is pivotal in the continued im-provement of these systems, but even modern qualification testssay little about how c-si PV modules respond to the stressors ofthe real world.

Two such modern qualification tests are IEC61215 Damp

7

Heat testing, and IEC?? UV Preconditioning. [list test con-ditions?] Studies are underway to investigate the mechanisticdegradation pathways activated in c-si PV modules by theselaboratory-based exposure stressors, in order to compare themto what is observed in the field. Initial results from full-sizedc-si PV modules show that UV Preconditioning stressors arenot sufficient to induce a significant response in the measurablecharacteristics of the modules within 1500 hours. Conversely,c-si PV modules exposed to Damp Heat show significant degra-dation within 1500 hours, with a dominant mechanistic degra-dation pathway evident involving several component PV ma-terials. Semi-gSEM modeling of the experimental data indi-cates that moisture and thermal stressors activate PET hydroly-sis in the c-si PV backsheet, resulting in a loss of mechanical,moisture barrier, and dielectric properties. This PET hydrolyticresponse is strongly correlated to EVA hydrolysis (likely dueto an increase in moisture ingress through the compromisedbacksheet barrier). EVA hydrolysis results in the appearanceof acetic acid within the interior of the c-si PV module sys-tems which is strongly correlated to loss of electrical properties(likely due to metallization corrosion).

Figure 7: Caption.

4.4. Real world studies under uncontrolled conditions

4.4.1. Cross-sectional study of real-world PV modules-Yang/Tim

In pursuit of insights into field-relevant degradation mech-anisms a study of real-world performance of PV systems wasundertaken for six months at the SDLE SunFarm. These 60PV modules comprised of 20 distinct brands (3 samples ofeach brand ). The uncontrolled nature of the real-world ne-cessitates high granularity and high-fidelity time series datas-treams; Minute-by-minute irradiance and environmental weremonitored with Kipp & Zonen CMP6 pyranometer and VaisalaWXT520 weather stations. Power data including DC current,voltage and AC power were monitored by grid connected mi-croinverters.

Clustering types such as hierarchical and k-means to dis-cover naturally arising self similarity grouping and relation-ships. Parametric and nonparametric data driven statisticalmodeling, showing PV module brand differences, which arethen mapped to physical models, such as 60 cell diode mod-els of PV Modules, so that changes in variances and divergencein performance across a study population as time passes can be

used to highlight the nature of damage accumulation, degrada-tion and failure. The population members who fail first can beobserved to diverge from the mean population behavior earlierin the study, for a truly non-stochastic degradation pathway.

In order to predict PV system performance under any givenenvironmental conditions, i.e. insolation, ambient temperature.The data of ”good tracker group” (pointed out with red arrow) ischosen for modeling. The response variable Y AC is the mea-sured AC power normalized to each modules nameplate poweroutput. Plane of array irradiance (POA), ambient temperatureand modules brands are the main contributing covariates. Inmodel selection, each individual predictor and the interaction oftwo or three predictors were taken into consider. Based on min-imization of AIC (Akaike Information Criterion), and furthervalidated with domain knowledge, the best fit for solar noontime AC power production is given in [68]

Figure 8: Caption.

4.4.2. Prediction of microinverter operating temperature forlifetime stress-Mohammad/Tim

A microinverter is typically connected to one PV module toconvert the DC output of the PV module to utility AC. In a sim-ilar way to the PV modules, reported data on the microinverterperformance naturally allows for extended power electronicsinformatics and analytics. The analysis showed that the criticalfactors are a mixture of application stressors: PV module tem-perature, AC power output of the microinverter, and environ-mental stressors: irradiance, and ambient temperature []. Fig.shows the correlation coefficient between different applicationand operation stressors. Furthermore, the impact of each stres-sors on thermal performance are also evaluated and a multiplelinear regression model was developed to predict the microin-verter temperature at real world operation. Fig. shows com-parison between actual and predicted microinverter temperatureduring noon time in a particular cloudy day.

Even in this complex device, a switch-mode power converter,the microstructural evolution of the power components resultsin performance degradation at the mesoscale, but the stres-sors and responses are largely unknown. Application stressors,

8

such as high speed grid disturbances, high frequency switchingpower cycling, combined with slower events such as modulepower cycling and heat/humidity give a rich set of variables thatimpact inverter mesostructural evolution. By acquiring time-series datasets, typically on the minute-by-minute level, one canidentify factors through which some population members di-verge from the mean behavior and become a subset of extremalor even failing devices. One seeks out the relationships betweenthese variables and again mapping the data onto physical mod-els allows close examination of what is essentially a closed de-vice. For example in MOSFETs utilized in microinverters phys-ical models of efficiency loss indicate that switching losses havetwo contributors: output capacitance loss, PCoss = CossV2 fsand conduction losses Pcl =ID2Ron, regression fitting deter-mine the physical parameters of the device and allow for mon-itoring in time to infer degradation mechanisms, such a Ronincrease signifying thermal runaway of the FET die. Mecha-nisms can then be confirmed by laboratory-based analytic in-vestigation. In many ways this is easier and more productiveway to determine the critical factors on mesoscopic evolutionover lifetime. Difference can arise from climatic, applicationstressors, design, construction, etc.

Figure 9: Caption.

5. Discussion

5.1. Making the stochastic, deterministic

YXNotes: Either from physical approach with measurementerrors or from statistical approach, the resulting models are nei-ther completely stochastic nor completely deterministic. Bystudying the rich data, we statistically extract meaningful de-terministic portion from a stochastic degradation phenomenon.

Such deterministic parts include, for example, mean degrada-tion curver / rate (shape and function parameter estimations)and relations of various observable and latent variables in thedegradation network.

RFNotes: Mescopic evolution models that encompass thetemporal evolution of mechanisms, interactions at a level,among levels, over time. Features such as interfaces, disloca-tions, defects and also materials, components and functionaldevices. Predictive models of full system, sub-system, compo-nent, material, feature levels

5.2. The age of materials informatics

RFNotes: this is about bringing the modern distributed com-puting advances that appeared in 2006 into research on energymaterials, so as to enable real-world studies, along with merg-ing laboratory-based and real-world studies into larger models.

5.2.1. Changing ways for doing scienceRFNotes: Complementing traditional research approaches

with new epidemiological, analytical and modeling approaches,including harnessing diverse and de novo data types for re-search, and conducting experiments under broad and uncon-trolled conditions.

5.2.2. Hadoop and NoSQLRFNotes: Distributed Computing, communications and the

internet of things, to benefit science and enable new classes ofcomplex and stochastic problems to be studied.

5.2.3. Challenges of data handling and opportunities of open-ness

RFNotes: Data Assembly, annotation and curation. Ontolog-ical foundations for energy materials as is done in medical fieldfor different communities in the medical field. Efficient query,and metadata Development. Open sources software has accel-erated science and introduced new forms of collaboration. Theadvent and explosion of open data reduces the effective cost ofmaking data needed for many experiments and opens new ar-eas of scientific inquiry. And the open sharing and assembly ofdatasets and analytics enables new scientific inquiries.

Open data, code [69, 70, 71]

5.2.4. Challenges of Data Variety, Volume Velocity, VeracityRFNotes: Laboratory-based degradation science data types.

Single value points in time, spectral points in time, images intime. RFNotes: Real-world degradation science data types.Time series. Measure all stresses/responses. RFNotes: Dimen-sion reduction is a major focus of Materials Informatics efforts,for both enabling data handling and transport, and for effec-tive information extraction. Very large datasets, as for exampleproduced at DOE central facilities can approach 100 of Ter-abytes for an experiment, where therefore just taking data tothe users home institution is a challenge. With the large volu-metric or image, and hyper-dimensional, datasets many timesthey are pre-processed in a limited manner to extract some cur-rent information based on initial or current physical theories.

9

This data reduction can lose critical information, and also re-strict the utility of the reduced dataset if the reduction has beendone in-artfully. At the same time there is a critical need to bal-ance data data volumes coming from diverse sources, such astime series, spectra and images. And to enable the extractionof a diverse set of single-valued metrics to enable informationcontent assessment using approaches of information entropy. Inthese cases, thankfully the original dataset is not destroyed, butis still available for subsequent analysis using better models,and at the same time can be used for other research projects inthe open data sense. RFNotes: Single valued metrics. Informa-tion content assessment and extraction. Shannon’s informationentropy. Feynman on Computation. Developing useful mea-sures and metrics, based on model’s parameterization, based onobservations, and on visualization.

5.3. Hypothesis-driven and epidemiological research: Labora-tory and real world

RFNotes: Study protocols, defining experimental design forpopulation size,statistical power and significance. Study types,retrospective, longitudinal. RFNotes: Experiments, laboratory-based, hypothesis driven, mechanistic. On small populationsizes. Proven contributions and significance? Real-world, ob-servational, uncontrolled Studies, therefore must measure allvariables Studies

5.4. Mesostructure evolution modeling: making the stochastic,deterministic

RFNotes: Micro-, Meso-, and Macro-scopic physical mod-els. Microscopic: Reaction Diffusion, Nucleation and Growth.Mesoscopic: Fluctuation and Disorder, thermal, entropic. an-nealed vs. quenched disorder. Macroscopic: From macroscopicvariables such as current, diode models. Big challenges in re-gression of full mesoscopic evolution SEM models, but here in-formatics and systems of equations help. RFNotes: Data-drivenstatistical models. Used for bridging across gaps of approx-imation. Capturing accumulation and transitions. RFNotes:semi-gSEM for merging physical and statistical models. Map-ping from physical/mechanistic models and data-driven statis-tical models onto SEM models. Identifying accumulation andtransitions

5.4.1. Traversing temporal and spatial domainsRFNotes: Discovering rare and slow events against a back-

ground of periodic and aperiodic yet common and fast events.This is an information extraction from data problem. With theopportunity to identify signatures of stress and response degra-dation events. Discover degradation modes and mechanisms,instead of just noting failure. From micro to meso to macro.From temporal through volumetric

5.4.2. Advancing new paradigms for mesoscopic evolutionstudies

RFNotes: Introducing query and retrospective studies to con-trolled laboratory-based studies.

6. Conclusions

7. Acknowledgements

DOE-BAPVC, OTF-SDLE, OTF-MAPV, UL, SG,

8. Tables

9. Figures

RFNotes: There are 6 examples, one figure each. laboratory:pmma, pet, modules, bare cells. Real- world : pr modules, mi-croinverters. So we have 9 figures. 1 figure per page. For theexamples, each fig can have an a) and a b) to illustrate the ex-ample Maybe PMMA, Pet have a,b,c?

9.1. Degradation science approach9.1.1. 1. Degradation science triangle

RFNotes: Sample Stressors, Mechanisms, Responses. Meso-scopic evolution models. Study protocols. Laboratory-basedand real-world. Ffigure 1 is DS triangle with laboratory-based,and real-world experiments in bottom left and Right corners.The Top say Degradation Science Mesoscopic Evolution. Andthe middle of the triangle has Dear Sci Model( semi-gSEMwith a network of Mechanisms, colored as physical or statis-tical models, and the Stressors on the left side, the responses onright.

9.1.2. 2. SunFarm networkThe global SF network, we need a new fig of this, with Cleve-

land (5 farms) inset to Ohio inset, to US and Taiwan, India3. Energy-CRADLE

9.2. Laboratory-based studies9.2.1. 4. PMMA9.2.2. 5. PET9.2.3. 6. PV Mod UL-sem9.2.4. 7. PV Cells9.3. Real-world studies9.3.1. 8. PV Mod TSA9.3.2. 9. Microinverter TSA10. References

References

[1] N. Williard, Wei He, C. Hendricks, M. Pecht, Lessons learnedfrom the 787 dreamliner issue on lithium-ion battery reliability, Energies(19961073) 6 (9) (2013) 4682–4695, 00008. doi:10.3390/en6094682.URL http://search.ebscohost.com/login.aspx?direct=

true&db=a9h&AN=90506546&site=ehost-live

[2] M. P. H. Huerta, Certification testing of lithium-ion batteries to beused on commercial airplanes - ntsb safety reccomendation, Tech. Rep.A-14-032 through -036, 00000 (May 2014).URL http://www.ntsb.gov/doclib/recletters/2014/

A-14-032-036.pdf

[3] R. G. Ross, PV reliability development lessons from JPL’s flat plate so-lar array project, IEEE Journal of Photovoltaics 4 (1) (2014) 291–298,00000. doi:10.1109/JPHOTOV.2013.2281102.URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?

arnumber=6616584

10

[4] J. Hemminger, G. Crabtree, J. Sarrao, From quanta to the continuum: Op-portunities for mesoscale science, Tech. rep., U.S Department of EnergyBasic Energy Sciences Advisory Committee (Sep. 2012).

[5] P. N. Edwards, A Vast Machine: Computer Models, Climate Data, and thePolitics of Global Warming, first edition Edition, The MIT Press, Cam-bridge, Mass, 2010, 00270.

[6] R. S. Chen, A vast machine: Computer models, climate data, and thepolitics of global warming, Environmental Health Perspectives 119 (4)(2011) A182, 00000 PMID: null.URL http://www.ncbi.nlm.nih.gov/pmc/articles/

PMC3080963/

[7] M. C. Roco, The long view of nanotechnology development: Thenational nanotechnology initiative at 10 years, in: NanotechnologyResearch Directions for Societal Needs in 2020, no. 1 in Science PolicyReports, Springer Netherlands, 2011, pp. 1–28, 00116.URL http://link.springer.com/chapter/10.1007/

978-94-007-1168-6_1

[8] R. French, V. Parsegian, R. Podgornik, R. Rajter, A. Jagota, J. Luo,D. Asthagiri, M. Chaudhury, Y. Chiang, S. Granick, S. Kalinin, M. Kar-dar, R. Kjellander, D. Langreth, J. Lewis, S. Lustig, D. Wesolowski,J. Wettlaufer, W. Ching, M. Finnis, F. Houlihan, O. von Lilien-feld, C. van Oss, T. Zemb, Long range interactions in nanoscalescience, Reviews of Modern Physics 82 (2) (2010) 1887–1944.doi:10.1103/RevModPhys.82.1887.URL http://apps.isiknowledge.com/full_record.do?

product=WOS&search_mode=GeneralSearch&qid=1&SID=

1Ah3Ejhdn8kKF83ia@F&page=1&doc=2

[9] R. French, H. Tran, Immersion lithography: Photomask and wafer-levelmaterials, ANNUAL REVIEW OF MATERIALS RESEARCH 39(2009) 93–126. doi:10.1146/annurev-matsci-082908-145350.URL http://apps.isiknowledge.com/full_record.do?

product=WOS&search_mode=GeneralSearch&qid=1&SID=

1Ah3Ejhdn8kKF83ia@F&page=1&doc=7

[10] S. Karmakar, C. Dasgupta, S. Sastry, Growing length scales andtheir relation to timescales in glass-forming liquids, Annual Re-view of Condensed Matter Physics 5 (1) (2014) 255–284, 00009.doi:10.1146/annurev-conmatphys-031113-133848.URL http://dx.doi.org/10.1146/

annurev-conmatphys-031113-133848

[11] H. Sewell, J. Mulkens, Materials for optical lithography tool applica-tion, Annual Review of Materials Research 39 (1) (2009) 127–153.doi:10.1146/annurev-matsci-082908-145309.URL http://apps.webofknowledge.com/full_record.

do?product=UA&search_mode=GeneralSearch&qid=1&SID=

4AHB4MbNNLdDl939HC6&page=2&doc=15

[12] J. M. Macak, H. Tsuchiya, A. Ghicov, K. Yasuda, R. Hahn,S. Bauer, P. Schmuki, TiO2 nanotubes: Self-organized electro-chemical formation, properties and applications, Current Opinionin Solid State and Materials Science 11 (12) (2007) 3–18, 00000.doi:10.1016/j.cossms.2007.08.004.URL http://www.sciencedirect.com/science/article/pii/

S1359028607000496

[13] L. Yang, C. Adam, G. S. Nichol, S. L. Cockroft, How much do van derwaals dispersion forces contribute to molecular recognition in solution?,Nature Chemistry 5 (12) (2013) 1006–1010. doi:10.1038/nchem.

1779.URL http://www.nature.com/doifinder/10.1038/nchem.1779

[14] E. C. Garnett, M. L. Brongersma, Y. Cui, M. D. McGehee, Nanowiresolar cells, Annual Review of Materials Research 41 (1) (2011) 269–295.doi:10.1146/annurev-matsci-062910-100434.URL http://dx.doi.org/10.1146/

annurev-matsci-062910-100434

[15] G. Crabtree, S. C. Glotzer, B. McCurdy, J. Roberto, Computationalmaterials science and chemistry: Accelerating discovery and innovationthrough simulation-based engineering and science (2010).URL http://science.energy.gov/bes/news-and-resources/

reports/abstracts/#CMSC

[16] D. W. Brenner, Challenges to marrying atomic and continuum modelingof materials, Current Opinion in Solid State and Materials Science 17 (6)(2013) 257–262. doi:10.1016/j.cossms.2013.07.005.URL http://www.sciencedirect.com/science/article/pii/

S1359028613000533

[17] M. Praprotnik, L. D. Site, K. Kremer, Multiscale simulation of softmatter: From scale bridging to adaptive resolution, Annual Review ofPhysical Chemistry 59 (1) (2008) 545–571, 00185 PMID: 18062769.doi:10.1146/annurev.physchem.59.032607.093707.URL http://dx.doi.org/10.1146/annurev.physchem.59.

032607.093707

[18] S. C. Kamerlin, S. Vicatos, A. Dryga, A. Warshel, Coarse-grained(multiscale) simulations in studies of biophysical and chemical systems,Annual Review of Physical Chemistry 62 (1) (2011) 41–64, 00074 PMID:21034218. doi:10.1146/annurev-physchem-032210-103335.URL http://dx.doi.org/10.1146/

annurev-physchem-032210-103335

[19] I. G. Kevrekidis, G. Samaey, Equation-free multiscale compu-tation: Algorithms and applications, Annual Review of Physi-cal Chemistry 60 (1) (2009) 321–344, 00096 PMID: 19335220.doi:10.1146/annurev.physchem.59.032607.093610.URL http://dx.doi.org/10.1146/annurev.physchem.59.

032607.093610

[20] J. Walpole, J. A. Papin, S. M. Peirce, Multiscale computationalmodels of complex biological systems, Annual Review of Biomed-ical Engineering 15 (1) (2013) 137–154, 00014 PMID: 23642247.doi:10.1146/annurev-bioeng-071811-150104.URL http://dx.doi.org/10.1146/

annurev-bioeng-071811-150104

[21] J. Holdren, Goals of the materials genome initiative | the white house(2011).URL http://www.whitehouse.gov/mgi/goals

[22] J. J. de Pablo, B. Jones, C. L. Kovacs, V. Ozolins, A. P. Ramirez, Thematerials genome initiative, the interplay of experiment, theory andcomputation, Current Opinion in Solid State and Materials Science 18 (2)(2014) 99–117, 00000. doi:10.1016/j.cossms.2014.02.003.URL http://www.sciencedirect.com/science/article/pii/

S1359028614000060

[23] A. N. Andriotis, G. Mpourmpakis, S. Broderick, K. Rajan, S. Datta,M. Sunkara, M. Menon, Informatics guided discovery of sur-face structure-chemistry relationships in catalytic nanoparticles,The Journal of Chemical Physics 140 (9) (2014) 094705, 00002.doi:10.1063/1.4867010.URL http://scitation.aip.org/content/aip/journal/jcp/

140/9/10.1063/1.4867010

[24] M. Jordan (Ed.), Frontiers in Massive Data Analysis, National ResearchCouncil, National Academies Press, 2013.URL http://www.nap.edu/catalog.php?record_id=18374

[25] B. Obama, US OSTP executive order – making open and machinereadable the new default for government information | the white house(2013).URL http://www.whitehouse.gov/the-press-office/2013/

05/09/executive-order-making-open-and-machine-readable-new-default-government-

[26] O. D. Charter”, Open data charter -https://www.gov.uk/government/publications/open-data-charter (Jun.2013).URL https://www.gov.uk/government/publications/

open-data-charter

[27] E. Maxwell, Harnessing openness to improve research, teaching andlearning in higher education, Innovations: Technology, Governance,Globalization 5 (2) (2010) 155–166. doi:10.1162/inov_a_00019.URL http://dx.doi.org/10.1162/inov_a_00019

[28] S. Ghemawat, H. Gobioff, S.-T. Leung, The google file system, ACMSIGOPS Operating Systems Review 37 (5) (2003) 29–43, 04544. doi:

10.1145/1165389.945450.URL http://journals.ohiolink.edu/ejc/article.cgi?issn=

01635980&issue=v37i0005&article=29_tgfs

[29] F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Bur-rows, T. Chandra, A. Fikes, R. E. Gruber, Bigtable: A distributed stor-age system for structured data, ACM Transactions on Computer Systems(TOCS) 26 (2) (2008) 1–26, 03103. doi:10.1145/1365815.1365816.URL http://journals.ohiolink.edu/ejc/article.cgi?issn=

07342071&issue=v26i0002&article=1_b

[30] J. Dean, S. Ghemawat, MapReduce: Simplified data processing on largeclusters, Communications of the ACM 51 (1) (2008) 107–113, 11882.

11

doi:10.1145/1327452.1327492.URL http://journals.ohiolink.edu/ejc/article.cgi?issn=

00010782&issue=v51i0001&article=107_m

[31] J. B. Goodenough, Y. Kim, Challenges for rechargeable li batter-ies, Chemistry of Materials 22 (3) (2010) 587–603. doi:10.1021/

cm901452z.URL http://dx.doi.org/10.1021/cm901452z

[32] P. Verma, P. Maire, P. Novk, A review of the features and analyses of thesolid electrolyte interphase in li-ion batteries, Electrochimica Acta 55 (22)(2010) 6332–6341. doi:10.1016/j.electacta.2010.05.072.URL http://linkinghub.elsevier.com/retrieve/pii/

S0013468610007747

[33] P. Hacke, K. Terwilliger, R. Smith, S. Glick, J. Pankow, M. Kempe,S. K. I. Bennett, M. Kloos, System voltage potential-induced degradationmechanisms in PV modules and methods for test, in: PhotovoltaicSpecialists Conference (PVSC), 2011 37th IEEE, IEEE, 2011, pp.000814–000820, 00036.URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?

arnumber=6186079

[34] F. D. Novoa, D. C. Miller, R. H. Dauskardt, Environmental mech-anisms of debonding in photovoltaic backsheets, Solar EnergyMaterials and Solar Cells 120, Part A (2014) 87–93, 00000.doi:10.1016/j.solmat.2013.08.020.URL http://www.sciencedirect.com/science/article/pii/

S0927024813004194

[35] C. Peike, S. Hoffmann, P. Hlsmann, K.-A. Wei, M. Koehl, P. Bentz,Permeation impact on metallization degradation, 2012, pp. 84720U–84720U–9. doi:10.1117/12.929819.URL http://proceedings.spiedigitallibrary.org/

proceeding.aspx?articleid=1379672

[36] M. Koehl, M. Heck, S. Wiesmeier, Modelling of conditions for acceler-ated lifetime testing of humidity impact on PV-modules based on moni-toring of climatic data, Solar Energy Materials and Solar Cells 99 (2012)282–291. doi:10.1016/j.solmat.2011.12.011.URL http://journals.ohiolink.edu/ejc/article.cgi?issn=

09270248&issue=v99inone_c&article=282_mocfalbomocd

[37] IEC, IEC61215 ed2.0 - crystalline silicon terrestrial photovoltaic (PV)modules - design qualification and type approval | IEC webstore |publication abstract, preview, scope (2005).URL http://webstore.iec.ch/webstore/webstore.nsf/

Artnum_PK/34077

[38] E. Hasselbrink, M. Anderson, Z. Defreitas, M. Mikofski, Y.-C. Shen,S. Caldwell, A. Terao, D. Kavulak, Z. Campeau, D. DeGraaff, Validationof the PVLife model using 3 million module-years of live site data, in:Photovoltaic Specialists Conference (PVSC), 2013 IEEE 39th, IEEE,2013, pp. 0007–0012, 00004.URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?

arnumber=6744087

[39] Y. Hu, PV module performance under real-world test conditions - adata analytics approach, Ph.D. thesis, Case Western Reserve University(2014).URL https://etd.ohiolink.edu/ap/10?0::NO:10:P10_

ACCESSION_NUM:case1396615109

[40] M. A. Hossain, Thermal characteristics of microinverters on dual-axistrackers, Master of science thesis, Case Western Reserve University,Cleveland, OH (2014).URL http://rave.ohiolink.edu/etdc/view?acc_num=

case1396888841

[41] D. C. Jordan, S. R. Kurtz, Photovoltaic degradation ratesan analytical re-view, Progress in Photovoltaics: Research and Applications 21 (1) (2013)12–29, 00090. doi:10.1002/pip.1182.URL http://journals.ohiolink.edu/ejc/article.cgi?issn=

10627995&issue=v21i0001&article=12_pdrar

[42] S. V. Janakeeraman, J. Singh, J. Kuitche, J. K. Mallineni, G. Tamizh-Mani, A statistical analysis on the cell parameters responsible for powerdegradation of fielded pv modules in a hot-dry climate, in: PhotovoltaicSpecialist Conference (PVSC), 2014 IEEE 40th, IEEE, 2014, pp. 3234–3238, 00002.URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?

arnumber=6925624

[43] P. Guttorp, Statistics and climate, Annual Review of Statis-

tics and Its Application 1 (1) (2014) 87–101. doi:10.1146/

annurev-statistics-022513-115648.URL http://dx.doi.org/10.1146/

annurev-statistics-022513-115648

[44] D. Randall, M. Khairoutdinov, A. Arakawa, W. Grabowski, Break-ing the cloud parameterization deadlock, Bulletin of the Amer-ican Meteorological Society 84 (11) (2003) 1547–1564, 00417.doi:10.1175/BAMS-84-11-1547.URL http://journals.ametsoc.org/doi/abs/10.1175/

BAMS-84-11-1547

[45] K. Rajan (Ed.), Informatics for Materials Science and Engineering,Butterworth-Heinemann, Oxford, 2013, 00000.URL http://www.sciencedirect.com/science/article/pii/

B9780123943996000217

[46] N. R. Wheeler, Y. Xu, A. Gok, I. V. Kidd, L. S. Bruckman, J. Sun, R. H.French, Data science study protocols for investigating lifetime and degra-dation of PV technology systems, in: IEEE PVSC 40, Denver, Colorado,2014, 00000.

[47] M. de Graef, Storing, archiving, and retrieving materials data sets,Annual Review of Materials Research 45 (1) (2015) null, 00000.doi:10.1146/annurev-matsci-070214-020844.URL http://www.annualreviews.org/doi/abs/10.1146/

annurev-matsci-070214-020844

[48] W.-Y. Ching, P. Rulis, Electronic Structure Methods for Complex Mate-rials: The orthogonalized linear combination of atomic orbitals, OxfordUniversity Press, Oxford, 2012, 00027.

[49] P. M. Chaikin, T. C. Lubensky, Principles of Condensed Matter Physics,reprint edition Edition, Cambridge University Press, Cambridge; NewYork, NY, USA, 2000, 03525.

[50] P. G. d. Gennes, J. Prost, The Physics of Liquid Crystals, 2nd Edition,Oxford University Press, Oxford; New York, 1995, 00042.

[51] M. Kleman, O. D. Laverntovich, J. Friedel, Soft Matter Physics: An In-troduction, 2003rd Edition, Springer, New York, 2002, 00000.

[52] J. Happel, H. Brenner, Low Reynolds number hydrodynamics: withspecial applications to particulate media, 1983rd Edition, Springer, TheHague ; Boston : Hingham, MA, USA, 1983, 06192.

[53] D. ben Avraham, S. Havlin, Diffusion and Reactions in Fractals and Dis-ordered Systems, Cambridge University Press, Cambridge, 2005, 00000.

[54] A. Cavagna, I. Giardina, Bird flocks as condensed matter, AnnualReview of Condensed Matter Physics 5 (1) (2014) 183–207, 00005.doi:10.1146/annurev-conmatphys-031113-133834.URL http://dx.doi.org/10.1146/

annurev-conmatphys-031113-133834

[55] M. G. Garner, S. A. Hamilton, Principles of epidemiological modelling.,Revue scientifique et technique (International Office of Epizootics) 30 (2)(2011) 407–416, 00014 PMID: 21961213.URL http://europepmc.org/abstract/med/21961213

[56] D. R. Nelson, Defects and Geometry in Condensed Matter Physics, Cam-bridge University Press, Cambridge ; New York, 2002, 00370.

[57] K. Binder, W. Kob, Glassy Materials and Disordered Solids: An Introduc-tion to Their Statistical Mechanics, World Scientific Publishing Company,Hackensack, N.J., 2005, 00003.

[58] D. Weinberger, The machine that would predict the future,Scientific American 305 (6) (2011) 52–57, 00018. doi:

10.1038/scientificamerican1211-52.URL http://www.nature.com/scientificamerican/journal/

v305/n6/full/scientificamerican1211-52.html

[59] N. N. Taleb, The black swan: the impact of the highly improbable, Ran-dom House Trade Paperbacks, New York, NY, USA, 2010.

[60] N. N. Taleb, Antifragile: Things that Gain from Disorder, Random House,New York, NY, USA, 2012.

[61] A. L. B. Ramirez, Z. S. Kean, J. A. Orlicki, M. Champhekar, S. M. Elsakr,W. E. Krause, S. L. Craig, Mechanochemical strengthening of a syntheticpolymer in response to typically destructive shear forces, Nature Chem-istry 5 (9) (2013) 757–761. doi:10.1038/nchem.1720.URL http://www.nature.com/doifinder/10.1038/nchem.1720

[62] D. T. Gillespie, Stochastic simulation of chemical kinetics, AnnualReview of Physical Chemistry 58 (1) (2007) 35–55, 00868 PMID:17037977. doi:10.1146/annurev.physchem.58.032806.104637.URL http://dx.doi.org/10.1146/annurev.physchem.58.

032806.104637

12

[63] W. McMahon, H. A. Birdsall, G. R. Johnson, C. T. Camilli, Degradationstudies of polyethylene terephthalate., Journal of Chemical & Engineer-ing Data 4 (1) (1959) 57–79. doi:10.1021/je60001a009.URL http://pubs.acs.org/cgi-bin/doilookup/?10.1021/

je60001a009

[64] L. H. Buxbaum, The degradation of poly(ethylene terephthalate), Ange-wandte Chemie International Edition in English 7 (3) (1968) 182–190.doi:10.1002/anie.196801821.URL http://onlinelibrary.wiley.com/doi/10.1002/anie.

196801821/abstract

[65] M. Day, D. M. Wiles, Photochemical decomposition mechanism ofpoly(ethylene terephthalate), Journal of Polymer Science Part B: PolymerLetters 9 (9) (1971) 665–669. doi:10.1002/pol.1971.110090906.URL http://onlinelibrary.wiley.com/doi/10.1002/pol.

1971.110090906/abstract

[66] D. M. Wiles, The effect of light on some commercially importantpolymers, Polymer Engineering & Science 13 (1) (1973) 74–77.doi:10.1002/pen.760130112.URL http://onlinelibrary.wiley.com/doi/10.1002/pen.

760130112/abstract

[67] J. E. Pickett, D. J. Coyle, Hydrolysis kinetics of condensation polymersunder humidity aging conditions, Polymer Degradation and Stability98 (7) (2013) 1311–1320. doi:10.1016/j.polymdegradstab.2013.04.001.URL http://linkinghub.elsevier.com/retrieve/pii/

S0141391013000918

[68] Y. Hu, M. A. Hosain, T. Jain, Y. R. Gunapati, L. Elkin, G. Zhang, R. H.French, Global SunFarm data acquisition network, energy CRADLE, andtime series analysis, in: 2013 IEEE Energytech, Cleveland, OH, 2013, pp.1–5. doi:10.1109/EnergyTech.2013.6645317.

[69] D. C. Ince, L. Hatton, J. Graham-Cumming, The case foropen computer programs, Nature 482 (7386) (2012) 485–488.doi:10.1038/nature10836.URL http://www.nature.com/nature/journal/v482/n7386/

full/nature10836.html

[70] J. Hendler, J. Holm, C. Musialek, G. Thomas, US government linked opendata: Semantic.data.gov, IEEE Intelligent Systems 27 (3) (2012) 25–31.

[71] S. J. Plimpton, J. D. Gale, Developing community codes for materialsmodeling, Current Opinion in Solid State and Materials Science 17 (6)(2013) 271–276, 00000. doi:10.1016/j.cossms.2013.09.005.URL http://www.sciencedirect.com/science/article/pii/

S1359028613000764

13