Self-assembly of electronic materials and the power of machine learning

There are many problems at the forefront of materials chemistry that are stymied by their inherent complexity. Such problems are characterized by a rich landscape of parameters and processing variables that is combinatorially too large for either an experimental or a computational approach to solve through an exhaustive search. In such cases, the usual approach is an Edisonian trial-and-error approach, which inevitably leaves areas of parameter space largely or wholly unexplored. The problems that we have explored are also characterized by a scarcity of data, since the data are expensive in time and resources to acquire, both experimentally and computationally. This makes it an ideal candidate to solve using a Bayesian optimization (BayesOpt) approach, which provides a strategy for a global optimization of "black box" functions lacking a functional form. For much of a decade, we have used a Bayesian optimization approach to study the solution processing of metal halide perovskites, a promising class of materials for solar cell development. Solution processing offers a low-energy-use and deceptively simple protocol to create electronically active thin films with high solar cell efficiency. In this talk, we will cover our accomplishments, challenges and outlook for what Bayesian optimization might achieve to help us understand, and hence control, these processes. I will end with some ideas of where we are taking BayesOpt in terms of having the ability to model nucleation and growth of metal halide perovskites and the algorithm development we will need to get us there. We will conclude with some observations on where the broader field of 'materials discovery' is headed.