Crystallography companion agent for high-throughput materials discovery



Maffettone, Phillip M, Banko, Lars, Cui, Peng, Lysogorskiy, Yury, Little, Marc A, Olds, Daniel, Ludwig, Alfred and Cooper, Andrew I
(2021) Crystallography companion agent for high-throughput materials discovery. NATURE COMPUTATIONAL SCIENCE, 1 (4). pp. 290-297.

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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.

Item Type: Article
Uncontrolled Keywords: 46 Information and Computing Sciences, 4602 Artificial Intelligence, 34 Chemical Sciences, Bioengineering, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD)
Divisions: Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 09 Apr 2021 15:19
Last Modified: 22 Jun 2024 14:05
DOI: 10.1038/s43588-021-00059-2
Open Access URL: https://arxiv.org/abs/2008.00283
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3118834