# Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials

## TL;DR

This paper introduces an error-correction learning approach that leverages historical data and experimental feedback to accelerate the discovery of thermoelectric materials, significantly reducing experimental efforts and uncovering new chemical families.

## Contribution

It presents a novel closed-loop learning strategy that refines models with experimental feedback, enabling faster discovery of thermoelectric materials at low synthesis temperatures.

## Key findings

- Discovered a new thermoelectric chemical family, PbSe:SnSb.
- Achieved over 2x improvement in power factor with optimized doping.
- Reduced experimental search efforts by up to 3x compared to traditional machine learning methods.

## Abstract

The exploration of thermoelectric materials is challenging considering the large materials space, combined with added exponential degrees of freedom coming from doping and the diversity of synthetic pathways. Here we seek to incorporate historical data and update and refine it using experimental feedback by employing error-correction learning (ECL). We thus learn from prior datasets and then adapt the model to differences in synthesis and characterization that are otherwise difficult to parameterize. We then apply this strategy to discovering thermoelectric materials where we prioritize synthesis at temperatures < 300{\deg}C. We document a previously unreported chemical family of thermoelectric materials, PbSe:SnSb, finding that the best candidate in this chemical family, 2 wt% SnSb doped PbSe, exhibits a power factor more than 2x that of PbSe. Our investigations show that our closed-loop experimentation strategy reduces the required number of experiments to find an optimized material by as much as 3x compared to high-throughput searches powered by state-of-the-art machine learning models. We also observe that this improvement is dependent on the accuracy of prior in a manner that exhibits diminishing returns, and after a certain accuracy is reached, it is factors associated with experimental pathways that dictate the trends.

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Source: https://tomesphere.com/paper/2302.13380