Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

TL;DR

This paper presents an active learning method that combines Gaussian Processes and reinforcement learning to efficiently explore parameter spaces in automated experiments, demonstrated on materials phase transitions.

Contribution

It introduces a novel hypothesis-driven active learning framework that integrates probabilistic modeling with policy refinement for experimental discovery.

Findings

Effective exploration of phase transitions in materials.

Framework adaptable to complex physical problems.

Reduced number of experimental steps needed.

Abstract

Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. Here we introduce an active learning approach based on co-navigation of the hypothesis and experimental spaces. This is realized by combining the structured Gaussian Processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. We demonstrate this approach for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO3 using Piezoresponse Force Microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis-generation are available.