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Crystallography companion agent for high-throughput materials discovery

Published:2021-04 Issue:4 Volume:1 Page:290-297
ISSN:2662-8457
Container-title:Nature Computational Science
language:en
Short-container-title:Nat Comput Sci

Author:

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

Funder

RCUK | Engineering and Physical Sciences Research Council

DOE | LDRD | Brookhaven National Laboratory

Deutsche Forschungsgemeinschaft

Leverhulme Trust

Leverhulme Centre for Integrative Research on Agriculture and Health

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

Link

http://www.nature.com/articles/s43588-021-00059-2.pdf

Reference53 articles.

1. Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377–381 (2018).

2. MacLeod, B. P. et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci. Adv. 6, eaaz8867 (2020).

3. Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).

4. Iwasaki, Y., Kusne, A. G. & Takeuchi, I. Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries. npj Comput. Mater. 3, 4 (2017).

5. Stanev, V. et al. Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering. npj Comput. Mater. 4, 43 (2018).

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