@inproceedings{
pekala2023evaluating,
title={Evaluating {AI}-guided Design for Scientific Discovery},
author={Michael Pekala and Elizabeth Ann Pogue and Kyle McElroy and Alexander New and Gregory Bassen and Brandon Wilfong and Janna Domenico and Tyrel McQueen and Christopher D Stiles},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=SfEsK3O2KT}
}
Machine learning has great potential to revolutionize experimental materials research; however, the degree to which these approaches accelerate novel discovery is rarely quantified. To this end, we propose a framework for characterizing the rate of “first discovery” of scientific hypotheses in the form of materials families. We use a combination of the SuperCon and Materials Project databases to simulate a scientific needle-in-a-haystack discovery problem as a motivating example. We use this approach to compare the ability of different adaptive sampling strategies to rediscover promising superconductor families, such as the Cuprates and iron-based superconductors. This methodology can be applied using various notions of novelty, making it applicable to discovery problems more broadly.
Evaluating AI-guided Design for Scientific Discovery
Michael Pekala Elizabeth Ann Pogue Kyle McElroy Alexander New Gregory Bassen Brandon Wilfong Janna Domenico Tyrel McQueen Christopher D Stiles