Machine learning for high-throughput experimental exploration of metal halide perovskites

Metal halide perovskites (MHPs) have catapulted to the forefront of energy research due to the unique combination of high device performance, low materials cost, and facile solution processability. A remarkable merit of these materials is their compositional flexibility allowing for multiple substitutions at all crystallographic sites, and hence thousands of possible pure compounds and virtually a near-infinite number ofmulticomponent solid solutions. Harnessing the full potential of MHPs necessitates rapid exploration of multidimensional chemical space toward desired functionalities. Recent advances in laboratory automation, ranging from bespoke fully automated robotic labs to microfluidic systems and to pipetting robots, have enabled high-throughput experimental workflows for synthesizing MHPs. Here, we provide an overview of the state of the art in the automated MHP synthesis and existing methods for navigating multicomponent compositional space. We highlight the limitations and pitfalls of the existing strategies and formulate the requirements for necessary machine learning tools including causal and Bayesian methods, as well as strategies based on co-navigation of theoritical and experimental spaces. We argue that ultimately the goal of automated experiments is to simultaneously optimize the materials synthesis and refine the theoretical models that underpin target functionalities. Furthermore, the near-term development of automated experimentation will not lead to the full exclusion of human operator but rather automatization of repetitive operations, deferring human role to high-level slow decisions. We also discuss the emerging opportunities leveraging machine learning-guided automated synthesis to the development of high-performance perovskite optoelectronics.

Automated summary

Metal halide perovskites (MHPs) have gained attention in energy research due to their high performance, low cost, and easy processing. Automated experimentation, aided by machine learning, allows for high-throughput exploration and optimization of multicomponent compositional space for synthesizing MHPs. The goal is to optimize materials synthesis and refine theoretical models for target functionalities with the help of automation.