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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Open accessAbstract
Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design.
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“Instead, computational approaches championed by the Materials Project (MP)16, the Open Quantum Materials Database (OQMD)17, AFLOWLIB20 and NOMAD21 have used first-principles calculations based on density functional theory (DFT) as approximations of physical energies.”
From Scaling deep learning for materials discovery (2023) · cites this paper as [16]
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“while further structural details of more than 2000 of these new compounds have been placed in the GNoME Explorer archive3 within the Materials Project.4”
From Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery (2024) · cites this paper as [4]
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“Assuming future advances in materials science, ML research could potentially draw upon open databases such as the Materials Project [380] and the UCI Machine Learning Repository [271] to invent new, climate-friendly materials [830].”
From Tackling climate change with machine learning (2023) · cites this paper as [380]
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“109,110 Therefore, we conclude by showcasing a new interactive web application hosted on the Materials Project, 41,111 which can be accessed at the following webpage: https://materialsproject.org/mofs.”
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Metadata
- Authors
- Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- ORCID
- Venue
- APL Materials
- Publisher
- AIP Publishing
- Published
- 2013-07-01
- Volume
- 1
- Issue
- 1
- ISSN
- 2166-532X (ISSN-L)
- OpenAlex
- W1992985800
- Semantic Scholar
- 98476502ec663771d41d2f8c948fd176257f17bd
- Cited by
- 13,094
- References
- 59
- Citations by year
- 2026: 1877 · 2025: 2248 · 2024: 2086 · 2023: 1534 · 2022: 1425 · 2021: 1182 · 2020: 902 · 2019: 619 · 2018: 462 · 2017: 308 · 2016: 235 · 2015: 145 · 2014: 64 · 2013: 4
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- APL Materials (publishedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Digital Access to Libraries (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Energy (-AC02-05CH11231, DEFG0296ER45571, DE‐FG02‐, 05CH11231, DE AC02 05CH11231, FG0296ER45571, DE-AC02-)
- National Energy Research Scientific Computing Center
- European Commission (321988, DE-AC02-05CH11231)
- Fonds De La Recherche Scientifique - FNRS
- Basic Energy Sciences (DE-AC02, DE-AC02-05CH11231., DE-FG02, AC02–05CH11231)
- Lawrence Berkeley National Laboratory (AC02‐05CH11231, DE-AC02-05CH11231, 05CH11231)
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- Materials Project, Materials Genome Initiative, inorganic materials, high-throughput computing, materials property prediction, materials discovery
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