Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

Hautier, Geoffroy; Fischer, Christopher C.; Jain, Anubhav; Mueller, Tim; Ceder, Gerbrand

doi:10.1021/cm100795d

Cited by 580Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesCrystal Structures and Properties

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

Hautier, Geoffroy, Fischer, Christopher C., Jain, Anubhav, Mueller, Tim, Ceder, Gerbrand·2010·Chemistry of Materials

DOI 10.1021/cm100795d

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Abstract

Finding new compounds and their crystal structures is an essential step to new materials discoveries. We demonstrate how this search can be accelerated using a combination of machine learning techniques and high-throughput ab initio computations. Using a probabilistic model built on an experimental crystal structure database, novel compositions that are most likely to form a compound, and their most-probable crystal structures, are identified and tested for stability by ab initio computations. We performed such a large-scale search for new ternary oxides, discovering 209 new compounds with a limited computational budget. A list of these predicted compounds is provided, and we discuss the chemistries in which high discovery rates can be expected.

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The paradigm of TGDS has already begun to show promise in scientific problems from diverse disciplines.
“The paradigm of TGDS has already begun to show promise in scientific problems from diverse disciplines.”
From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [22]
This line of research has resulted in the discovery of a hundred new ternary oxide compounds that were previously unknown using traditional approaches.
“This line of research has resulted in the discovery of a hundred new ternary oxide compounds that were previously unknown using traditional approaches [22], highlighting the effectiveness of TGDS in advancing scientific knowledge.”
From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [22]

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Authors: Hautier, G., Fischer, C. C., Jain, A., Mueller, T., & Ceder, G.
Affiliations: Massachusetts Institute of Technology (US)
Corresponding author: Ceder, Gerbrand
ORCID: Hautier, Geoffroy
Jain, Anubhav
Mueller, Tim
Ceder, Gerbrand
Venue: Chemistry of Materials
Publisher: American Chemical Society (ACS)
Published: 2010-05-27
Volume: 22
Issue: 12
Pages: 3762-3767
ISSN: 0897-4756 (ISSN-L)
1520-5002
DOI: 10.1021/cm100795d
OpenAlex: W1982598895
Cited by: 580
References: 41
Citations by year: 2026: 14 · 2025: 24 · 2024: 32 · 2023: 29 · 2022: 51 · 2021: 61 · 2020: 69 · 2019: 60 · 2018: 53 · 2017: 51 · 2016: 42 · 2015: 15 · 2014: 25 · 2013: 23 · 2012: 17
FWCI: 8.49
Type: journal article
Language: en
Open access: closed
Locations: Chemistry of Materials (publishedVersion)
DIAL (Catholic University of Leuven) (submittedVersion)
Funders: Belgian American Educational Foundation
Division of Materials Research
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Crystal Structures and Properties
Keywords: ternary oxides, crystal structure prediction, materials discovery, probabilistic models, crystal structure database, compound stability, high-throughput ab initio calculations, machine learning

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Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

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