Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning

Naik, Richa Ramesh; Tiihonen, Armi; Thapa, Janak; Batali, Clio; Liu, Zhe; Sun, Shijing; Buonassisi, Tonio

doi:10.1038/s41524-022-00751-5

Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning

Journal Article · Wed Apr 20 00:00:00 EDT 2022 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-022-00751-5· OSTI ID:1864089

Naik, Richa Ramesh; ; Thapa, Janak; Batali, Clio; ; ;

Abstract

While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, extracting fungible knowledge representations from experimental data remains an elusive task. In this manuscript, we use ML to infer the underlying differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). Using a sparse regression algorithm, we find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85 °C is described minimally by a second-order polynomial. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous to self-propagating reactions. We examine the robustness of our conclusions to experimental variance and Gaussian noise and describe the experimental limits within which this methodology can be applied. Our study highlights the promise and challenges associated with ML-aided scientific discovery by demonstrating its application in experimental chemical and materials systems.

View Journal Article

Sponsoring Organization:: USDOE

Grant/Contract Number:: EE0007535; EE0009096

OSTI ID:: 1864089

Alternate ID(s):: OSTI ID: 1978599

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 8; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (53)

Intrinsic Thermal Instability of Methylammonium Lead Trihalide Perovskite Conings, Bert; Drijkoningen, Jeroen; Gauquelin, Nicolas Advanced Energy Materials, Vol. 5, Issue 15 https://doi.org/10.1002/aenm.201500477	journal	June 2015
Ist die Trägheit eines Körpers von seinem Energieinhalt abhängig? Einstein, A. Annalen der Physik, Vol. 323, Issue 13 https://doi.org/10.1002/andp.19053231314	journal	January 1905
Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression Schmelzer, Martin; Dwight, Richard P.; Cinnella, Paola Flow, Turbulence and Combustion, Vol. 104, Issue 2-3 https://doi.org/10.1007/s10494-019-00089-x	journal	December 2019
Use and misuse of logistic equations for modeling chemical kinetics Burnham, Alan K. Journal of Thermal Analysis and Calorimetry, Vol. 127, Issue 1 https://doi.org/10.1007/s10973-015-4879-3	journal	July 2015
Analysis of logistic growth models Tsoularis, A.; Wallace, J. Mathematical Biosciences, Vol. 179, Issue 1 https://doi.org/10.1016/S0025-5564(02)00096-2	journal	July 2002
When a good fit can be bad Pitt, Mark A.; Myung, In Jae Trends in Cognitive Sciences, Vol. 6, Issue 10 https://doi.org/10.1016/S1364-6613(02)01964-2	journal	October 2002
Decomposition of the single-phase high-entropy alloy CrMnFeCoNi after prolonged anneals at intermediate temperatures Otto, F.; Dlouhý, A.; Pradeep, K. G. Acta Materialia, Vol. 112 https://doi.org/10.1016/j.actamat.2016.04.005	journal	June 2016
Non-invasive inference of thrombus material properties with physics-informed neural networks Yin, Minglang; Zheng, Xiaoning; Humphrey, Jay D. Computer Methods in Applied Mechanics and Engineering, Vol. 375 https://doi.org/10.1016/j.cma.2020.113603	journal	March 2021
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Raissi, M.; Perdikaris, P.; Karniadakis, G. E. Journal of Computational Physics, Vol. 378 https://doi.org/10.1016/j.jcp.2018.10.045	journal	February 2019
PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network Long, Zichao; Lu, Yiping; Dong, Bin Journal of Computational Physics, Vol. 399 https://doi.org/10.1016/j.jcp.2019.108925	journal	December 2019
Layer-by-Layer Degradation of Methylammonium Lead Tri-iodide Perovskite Microplates Fan, Zheng; Xiao, Hai; Wang, Yiliu Joule, Vol. 1, Issue 3 https://doi.org/10.1016/j.joule.2017.08.005	journal	November 2017
A data fusion approach to optimize compositional stability of halide perovskites Sun, Shijing; Tiihonen, Armi; Oviedo, Felipe Matter, Vol. 4, Issue 4 https://doi.org/10.1016/j.matt.2021.01.008	journal	April 2021
Predicting degradation rate of genipin cross-linked gelatin scaffolds with machine learning Entekhabi, Elahe; Haghbin Nazarpak, Masoumeh; Sedighi, Mehdi Materials Science and Engineering: C, Vol. 107 https://doi.org/10.1016/j.msec.2019.110362	journal	February 2020
Thermal degradation of epoxy–silica organic–inorganic hybrid materials Macan, Jelena; Brnardić, Ivan; Orlić, Sebastijan Polymer Degradation and Stability, Vol. 91, Issue 1 https://doi.org/10.1016/j.polymdegradstab.2005.04.024	journal	January 2006
Device stability of perovskite solar cells – A review Asghar, M. I.; Zhang, J.; Wang, H. Renewable and Sustainable Energy Reviews, Vol. 77 https://doi.org/10.1016/j.rser.2017.04.003	journal	September 2017
Effect of temperature on light induced degradation in methylammonium lead iodide perovskite thin films and solar cells Abdelmageed, Ghada; Mackeen, Cameron; Hellier, Kaitlin Solar Energy Materials and Solar Cells, Vol. 174 https://doi.org/10.1016/j.solmat.2017.09.053	journal	January 2018
Kepler's Laws of Planetary Motion: 1609–1666 Russell, J. L. The British Journal for the History of Science, Vol. 2, Issue 1 https://doi.org/10.1017/S0007087400001813	journal	June 1964
Understanding Degradation Mechanisms and Improving Stability of Perovskite Photovoltaics Boyd, Caleb C.; Cheacharoen, Rongrong; Leijtens, Tomas Chemical Reviews, Vol. 119, Issue 5 https://doi.org/10.1021/acs.chemrev.8b00336	journal	November 2018
Hybrid Perovskites with Larger Organic Cations Reveal Autocatalytic Degradation Kinetics and Increased Stability under Light Ellis, Christie L. C.; Javaid, Hamza; Smith, Emily C. Inorganic Chemistry, Vol. 59, Issue 17 https://doi.org/10.1021/acs.inorgchem.0c01133	journal	August 2020
Thermal Stability and Cation Composition of Hybrid Organic–Inorganic Perovskites Schwenzer, Jonas A.; Hellmann, Tim; Nejand, Bahram Abdollahi ACS Applied Materials & Interfaces, Vol. 13, Issue 13 https://doi.org/10.1021/acsami.1c01547	journal	March 2021
Model for the Prediction of the Lifetime and Energy Yield of Methyl Ammonium Lead Iodide Perovskite Solar Cells at Elevated Temperatures Bastos, João P.; Uytterhoeven, Griet; Qiu, Weiming ACS Applied Materials & Interfaces, Vol. 11, Issue 18 https://doi.org/10.1021/acsami.9b00923	journal	April 2019
Investigation of CH ₃ NH ₃ PbI ₃ Degradation Rates and Mechanisms in Controlled Humidity Environments Using in Situ Techniques Yang, Jinli; Siempelkamp, Braden D.; Liu, Dianyi ACS Nano, Vol. 9, Issue 2 https://doi.org/10.1021/nn506864k	journal	January 2015
Data‐Driven Equation Discovery of Ocean Mesoscale Closures Zanna, Laure; Bolton, Thomas Geophysical Research Letters, Vol. 47, Issue 17 https://doi.org/10.1029/2020GL088376	journal	August 2020
How persuasive is a good fit? A comment on theory testing. Roberts, Seth; Pashler, Harold Psychological Review, Vol. 107, Issue 2 https://doi.org/10.1037/0033-295X.107.2.358	journal	January 2000
Ionic transport in hybrid lead iodide perovskite solar cells Eames, Christopher; Frost, Jarvist M.; Barnes, Piers R. F. Nature Communications, Vol. 6, Issue 1 https://doi.org/10.1038/ncomms8497	journal	June 2015
In situ observation of heat-induced degradation of perovskite solar cells Divitini, G.; Cacovich, S.; Matteocci, F. Nature Energy, Vol. 1, Issue 2 https://doi.org/10.1038/nenergy.2015.12	journal	January 2016
U1 snRNP regulates cancer cell migration and invasion in vitro Oh, Jung-Min; Venters, Christopher C.; Di, Chao Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-019-13993-7	journal	January 2020
A review of deep learning in the study of materials degradation Nash, Will; Drummond, Tom; Birbilis, Nick npj Materials Degradation, Vol. 2, Issue 1 https://doi.org/10.1038/s41529-018-0058-x	journal	November 2018
Data-driven prediction of battery cycle life before capacity degradation Severson, Kristen A.; Attia, Peter M.; Jin, Norman Nature Energy, Vol. 4, Issue 5 https://doi.org/10.1038/s41560-019-0356-8	journal	March 2019
Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures Khenkin, Mark V.; Katz, Eugene A.; Abate, Antonio Nature Energy, Vol. 5, Issue 1 https://doi.org/10.1038/s41560-019-0529-5	journal	January 2020
Machine learning for molecular and materials science Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh Nature, Vol. 559, Issue 7715 https://doi.org/10.1038/s41586-018-0337-2	journal	July 2018
Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease Cappello, Valentina; Marchetti, Laura; Parlanti, Paola Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/s41598-016-0001-8	journal	December 2016
Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness Nishita, Michiru; Park, Seung-Yeol; Nishio, Tadashi Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-016-0028-x	journal	January 2017
Machine learning for active matter Cichos, Frank; Gustavsson, Kristian; Mehlig, Bernhard Nature Machine Intelligence, Vol. 2, Issue 2 https://doi.org/10.1038/s42256-020-0146-9	journal	February 2020
Structures, Phase Transitions and Tricritical Behavior of the Hybrid Perovskite Methyl Ammonium Lead Iodide Whitfield, P. S.; Herron, N.; Guise, W. E. Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep35685	journal	October 2016
Phase transition kinetics and surface binding states of methylammonium lead iodide perovskite Rajendra Kumar, G.; Dennyson Savariraj, A.; Karthick, S. N. Physical Chemistry Chemical Physics, Vol. 18, Issue 10 https://doi.org/10.1039/C5CP06232B	journal	January 2016
Degradation observations of encapsulated planar CH ₃ NH ₃ PbI ₃ perovskite solar cells at high temperatures and humidity Han, Yu; Meyer, Steffen; Dkhissi, Yasmina Journal of Materials Chemistry A, Vol. 3, Issue 15 https://doi.org/10.1039/C5TA00358J	journal	January 2015
Stability of solution-processed MAPbI ₃ and FAPbI ₃ layers Smecca, Emanuele; Numata, Youhei; Deretzis, Ioannis Physical Chemistry Chemical Physics, Vol. 18, Issue 19 https://doi.org/10.1039/C6CP00721J	journal	January 2016
Long term stability of air processed inkjet infiltrated carbon-based printed perovskite solar cells under intense ultra-violet light soaking Hashmi, Syed Ghufran; Tiihonen, Armi; Martineau, David Journal of Materials Chemistry A, Vol. 5, Issue 10 https://doi.org/10.1039/C6TA10605F	journal	January 2017
Photodecomposition and thermal decomposition in methylammonium halide lead perovskites and inferred design principles to increase photovoltaic device stability Juarez-Perez, Emilio J.; Ono, Luis K.; Maeda, Maki Journal of Materials Chemistry A, Vol. 6, Issue 20 https://doi.org/10.1039/C8TA03501F	journal	January 2018
I ₂ vapor-induced degradation of formamidinium lead iodide based perovskite solar cells under heat–light soaking conditions Fu, Fan; Pisoni, Stefano; Jeangros, Quentin Energy & Environmental Science, Vol. 12, Issue 10 https://doi.org/10.1039/C9EE02043H	journal	January 2019
Kinetics of Phase Change. I General Theory Avrami, Melvin The Journal of Chemical Physics, Vol. 7, Issue 12, p. 1103-1112 https://doi.org/10.1063/1.1750380	journal	December 1939
Deactivation of metastable single-crystal silicon hyperdoped with sulfur Simmons, C. B.; Akey, Austin J.; Krich, Jacob J. Journal of Applied Physics, Vol. 114, Issue 24 https://doi.org/10.1063/1.4854835	journal	December 2013
Stability of organometal perovskites with organic overlayers Tran, Catherine D. T.; Liu, Yi; Thibau, Emmanuel S. AIP Advances, Vol. 5, Issue 8 https://doi.org/10.1063/1.4930082	journal	August 2015
Data-driven discovery of coordinates and governing equations Champion, Kathleen; Lusch, Bethany; Kutz, J. Nathan Proceedings of the National Academy of Sciences, Vol. 116, Issue 45 https://doi.org/10.1073/pnas.1906995116	journal	October 2019
Beyond Eyeballing: Fitting Models to Experimental Data Christopoulos, Arthur; Lew, Michael J. Critical Reviews in Biochemistry and Molecular Biology, Vol. 35, Issue 5 https://doi.org/10.1080/10409230091169212	journal	January 2000
A Neural Network Model for Material Degradation Detection and Diagnosis Using Microscopic Images Choi, Woosung; Huh, Hyunsuk; Tama, Bayu Adhi IEEE Access, Vol. 7 https://doi.org/10.1109/ACCESS.2019.2927162	journal	January 2019
Explainable Machine Learning for Scientific Insights and Discoveries Roscher, Ribana; Bohn, Bastian; Duarte, Marco F. IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.2976199	journal	January 2020
Data-driven discovery of partial differential equations Rudy, Samuel H.; Brunton, Steven L.; Proctor, Joshua L. Science Advances, Vol. 3, Issue 4 https://doi.org/10.1126/sciadv.1602614	journal	April 2017
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations Raissi, Maziar; Yazdani, Alireza; Karniadakis, George Em Science, Vol. 367, Issue 6481 https://doi.org/10.1126/science.aaw4741	journal	January 2020
Machine Learning for Fluid Mechanics Brunton, Steven L.; Noack, Bernd R.; Koumoutsakos, Petros Annual Review of Fluid Mechanics, Vol. 52, Issue 1 https://doi.org/10.1146/annurev-fluid-010719-060214	journal	September 2019
Universal Differential Equations for Scientific Machine Learning Rackauckas, Christopher; Ma, Yingbo; Martensen, Julius https://doi.org/10.21203/rs.3.rs-55125/v1	preprint	August 2020
Properties of inductive reasoning Heit, Evan Psychonomic Bulletin & Review, Vol. 7, Issue 4 https://doi.org/10.3758/BF03212996	journal	December 2000

Similar Records

Role of Water in the Reversible Optoelectronic Degradation in Hybrid Perovskites at Low Pressure

Journal Article · Tue Oct 10 00:00:00 EDT 2017 · Journal of Physical Chemistry. C · OSTI ID:1432957

Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning

Citation Formats

References (53)

Similar Records

Related Subjects