Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

He, Tanjin; Huo, Haoyan; Bartel, Christopher J.; Wang, Zheren; Cruse, Kevin James; Ceder, Gerbrand

doi:10.1126/sciadv.adg8180

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

Journal Article · Fri Jun 09 00:00:00 EDT 2023 · Science Advances

DOI:https://doi.org/10.1126/sciadv.adg8180· OSTI ID:2234098

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Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of Minnesota, Minneapolis, MN (United States)

Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); National Science Foundation (NSF)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2234098

Journal Information:: Science Advances, Journal Name: Science Advances Journal Issue: 23 Vol. 9; ISSN 2375-2548

Publisher:: AAASCopyright Statement

Country of Publication:: United States

Language:: English

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