Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
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Combining ab initio calculations with simple substitutions has allowed researchers to improve to 48,000 computationally stable materials according to the paper's own recalculations.
“Combining ab initio calculations with simple substitutions has allowed researchers to improve to 48,000 computationally stable materials according to our own recalculations22–24 (see Methods).”
From Scaling deep learning for materials discovery (2023) · cites this paper as [23]
Section 6 (page unavailable) describes a Sn-for-Ge substitution followed by a first-principles calculation that predicted one material to be stable. Section 3 also describes substitution as a way to generate candidate materials. The paper does not report 48,000 computationally stable materials or its own recalculation of that figure; the citing sentence calls the recalculations “our own.” (LLM, conf=0.88)
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- Authors
- Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G.
- Affiliations
- Massachusetts Institute of Technology (US)
- Lawrence Berkeley National Laboratory (US)
- UCLouvain (BE)
- 3M (United States) (US)
- Corresponding author
- Ong, Shyue Ping
- ORCID
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 68
- Pages
- 314-319
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W2015197254
- Cited by
- 4,534
- References
- 2
- Citations by year
- 2026: 833 · 2025: 697 · 2024: 654 · 2023: 502 · 2022: 459 · 2021: 377 · 2020: 321 · 2019: 221 · 2018: 167 · 2017: 116 · 2016: 80 · 2015: 65 · 2014: 30 · 2013: 9 · 2012: 2
- FWCI
- 13.82
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- Computational Materials Science (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- DIAL (Catholic University of Leuven) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Funders
- U.S. Department of Energy (DE‐AC02‐05CH1123, DEFG0296ER45571, DE‐FG02‐, Contract No. DE-AC02-05CH1123, FG0296ER45571, AC02-05CH1123, DE-AC02-)
- National Energy Research Scientific Computing Center (DE-AC02-05CH1123)
- Basic Energy Sciences (DE-AC02, DE-AC0205CH1123, DE-FG02, No. DE-AC02-05CH1123)
- Laboratory Directed Research and Development
- Lawrence Berkeley National Laboratory (DE‐AC02‐05CH1123)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- pymatgen, crystal structure analysis, thermodynamic analysis, electrochemical stability, lithium-ion battery anodes, first-principles calculations
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