Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning
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s41524-022-00751-5.pdf
Description
Published version
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1.13 MB
Format
Adobe PDF
Checksum (MD5)
015be8ab672cc239eb67dc23253fa0ef
Author(s)
Naik, Richa Ramesh
Tiihonen, Armi
Thapa, Janak
Batali, Clio
Liu, Zhe
Sun, Shijing
Buonassisi, Tonio
Date Issued
2022
Journal
npj Computational Materials
Publisher
Springer Science and Business Media LLC
Citation
Naik, Richa Ramesh, Tiihonen, Armi, Thapa, Janak, Batali, Clio, Liu, Zhe et al. 2022. "Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning." npj Computational Materials, 8 (1).
Version
Final published version
Abstract
AbstractWhile 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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1038/S41524-022-00751-5
