Discovering Equations that Govern Experimental Materials Stability under Environmental Stress using Scientific Machine Learning

• DOI: 10.48550/arxiv.2106.10951
• Publication Date: 2022-04-20
• Authors: Richa Ramesh Naik; Armi Tiihonen; Janak Thapa; Clio Batali; Zhe Liu; Shijing Sun; Tonio Buonassisi
• Keywords: 0301 basic medicine; Condensed Matter - Materials Science; Materials Science (cond-mat.mtrl-sci); FOS: Physical sciences; 02 engineering and technology; QA76.75-76.765; 03 medical and health sciences; 669; TA401-492; Computer software; 0210 nano-technology; Materials of engineering and construction. Mechanics of materials

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.