Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping

TL;DR

This paper introduces methods for integrating human expertise into autonomous materials phase mapping, enhancing the accuracy of phase identification in x-ray diffraction data through probabilistic priors.

Contribution

It presents a novel framework for incorporating human input into Bayesian autonomous experimentation for materials exploration, demonstrated on phase mapping tasks.

Findings

Human input improves phase map accuracy

Probabilistic priors effectively incorporate expert knowledge

Significant performance gains observed with human guidance

Abstract

Autonomous experimentation (AE) combines machine learning and research hardware automation in a closed loop, guiding subsequent experiments toward user goals. As applied to materials research, AE can accelerate materials exploration, reducing time and cost compared to traditional Edisonian studies. Additionally, integrating knowledge from diverse sources including theory, simulations, literature, and domain experts can boost AE performance. Domain experts may provide unique knowledge addressing tasks that are difficult to automate. Here, we present a set of methods for integrating human input into an autonomous materials exploration campaign for composition-structure phase mapping. The methods are demonstrated on x-ray diffraction data collected from a thin film ternary combinatorial library. At any point during the campaign, the user can choose to provide input by indicating regions-of-interest, likely phase regions, and likely phase boundaries based on their prior knowledge (e.g., knowledge of the phase map of a similar material system), along with quantifying their certainty. The human input is integrated by defining a set of probabilistic priors over the phase map. Algorithm output is a probabilistic distribution over potential phase maps, given the data, model, and human input. We demonstrate a significant improvement in phase mapping performance given appropriate human input.