  While machine learning (ML) in experimental research has demonstrated
impressive predictive capabilities, inductive reasoning and knowledge
extraction remain elusive tasks, in part because of the difficulty extracting
fungible knowledge representations from experimental data. In this manuscript,
we use ML to infer the underlying dynamical 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). We apply a sparse regression algorithm that
automatically identifies the differential equation describing the dynamics from
time-series data. We find that the underlying DE governing MAPI degradation
across a broad temperature range of 35 to 85{\deg}C is described minimally with
three terms (specifically, a second-order polynomial), and not a simple
single-order reaction (i.e. 0th, 1st, or 2nd-order reaction). We demonstrate
how computer-derived results can aid the researcher to develop profound
mechanistic insights. This DE corresponds to the Verhulst logistic function,
which describes reaction kinetics analogous in functional form to autocatalytic
or self-propagating reactions, suggesting future strategies to suppress MAPI
degradation. We examine the robustness of our conclusions to experimental
luck-of-the-draw variance and Gaussian noise using a combination of experiment
and simulation, and describe the experimental limits within which this
methodology can be applied. Our study demonstrates the application of
scientific ML in experimental chemical and materials systems, highlighting the
promise and challenges associated with ML-aided scientific discovery.
