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Crystallography companion agent for high-throughput materials discovery

Nature Computational Science volume 1, pages 290–297 (2021)Cite this article

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A preprint version of the article is available at arXiv.

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.

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Fig. 1: Experimental XRD complexity and training an ensemble using synthetic data.
Fig. 2: Schematic of the crystallography companion agent (XCA).
Fig. 3: Schematics and example XRD data of three different inorganic and organic materials challenges.
Fig. 4: Autonomous XRD analysis results from XCA.
Fig. 5: Comparing different approaches to building a synthetic dataset and classifier.

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Data availability

The experimental datasets and code used for constructing the synthetic datasets are available as examples with the source code. Source data are provided with this paper.

Code availability

To facilitate the impact of this tool, the approach is kept entirely open-source under the BSD 3-clause license and is being embedded into data acquisition frameworks at central facilities (https://blueskyproject.io). Ongoing development of this tool is located at https://github.com/maffettone/xca. A release at the time of publication and example code for the results contained here are available at https://github.com/bnl/pub-Maffettone_2020_0853. The Bayesian optimization code is available at https://github.com/maffettone/bayes_opt.

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Acknowledgements

We acknowledge financial support from the Engineering and Physical Sciences Research Council (EPSRC) (grant no. EP/N004884/1; P.M.M., M.A.L. and A.I.C.), BNL Laboratory Directed Research and Development (LDRD) projects 20-032 ‘Accelerating materials discovery with total scattering via machine learning’ (P.M.M. and D.O.), the Leverhulme Trust via the Leverhulme Research Centre for Functional Materials Design (P.C. and A.I.C.) and the German Research Foundation (DFG) as part of the Collaborative Research Centre TRR87/3 ‘Pulsed high power plasmas for the synthesis of nanostructured functional layers’ (SFB-TR 87), project C2 (L.B., Y.L. and A.L.). This research utilized the PDF (28-ID-1) Beamline and resources of the National Synchrotron Light Source II, a US Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under contract no. DE-SC0012704. We thank ZGH (Zentrum für Grenzflächendominierte Höchstleistungswerkstoffe, Ruhr-Universität Bochum) and Diamond Light Source for access to beamlines I19 (MT15777) and I11 (EE17193) for XRD measurements.

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Authors and Affiliations

  1. National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, USA

    Phillip M. Maffettone & Daniel Olds

  2. Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, UK

    Phillip M. Maffettone, Peng Cui, Marc A. Little & Andrew I. Cooper

  3. Institute for Materials, Faculty of Mechanical Engineering, Ruhr University Bochum, Bochum, Germany

    Lars Banko & Alfred Ludwig

  4. Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr University, Bochum, Germany

    Yury Lysogorskiy

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  1. Phillip M. Maffettone
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Contributions

P.M.M., L.B. and Y.L. conceived the project. P.M.M. led the development of XCA and coordinated the research teams. L.B. contributed to development, prepared the alloy dataset and guided the inorganic dataset synthesis. P.C. and M.A.L. crystallized ADTA and measured XRD data. Y.L. advised on the machine learning. D.O. measured the BaTiO3 and advised on the relevant studies. A.L. supervised the development and the alloy studies. A.I.C. supervised the development and organic materials studies. Data were interpreted by all authors and the manuscript was prepared by all authors.

Corresponding authors

Correspondence to Phillip M. Maffettone or Andrew I. Cooper.

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The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Wenhao Sun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Jie Pan was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–40, discussion and Tables 1–4.

Source data

Source Data Fig. 1

XRD pattern data for Fig. 1a,b.

Source Data Fig. 3

Sample experimental XRD data from each dataset in Fig. 3.

Source Data Fig. 4

Probability data for BaTiO3, Confusion matrix for ADTA, and output probabilities for ternary NiCoAl phase diagrams.

Source Data Fig. 5

Source data for benchmark plots.

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Maffettone, P.M., Banko, L., Cui, P. et al. Crystallography companion agent for high-throughput materials discovery. Nat Comput Sci 1, 290–297 (2021). https://doi.org/10.1038/s43588-021-00059-2

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