Crystallography Companion Agent for High-throughput Materials Discovery

Phillip M. Maffettone, Lars Banko, Peng Cui, Yury Lysogorskiy, Marc A. Little, Daniel Olds, Alfred Ludwig, Andrew I. Cooper*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)
20 Downloads (Pure)

Abstract

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

Original languageEnglish
Pages (from-to)290-297
Number of pages8
JournalNature Computational Science
Volume1
Issue number4
Early online date19 Apr 2021
DOIs
Publication statusPublished - Apr 2021

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science Applications
  • Computer Networks and Communications

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