article Open AccessTop 1% cited
Machine learning unifies the modeling of materials and molecules
Science Advances · 2017 · Vol. 3(12)
Albert P. Bartók(Science and Technology Facilities Council)Sandip De(École Polytechnique Fédérale de Lausanne)Carl Poelking(University of Cambridge)Noam Bernstein(United States Naval Research Laboratory)James R. Kermode(University of Warwick)Gábor Cśanyi(University of Cambridge)Michele Ceriotti✉(École Polytechnique Fédérale de Lausanne)
Abstract
Statistical learning based on a local representation of atomic structures provides a universal model of chemical stability.
Machine Learning in Materials ScienceComputational Drug Discovery MethodsX-ray Diffraction in CrystallographyComputer scienceComputational biologyArtificial intelligenceData scienceBiology
Funding
- National Science Foundation
- Leverhulme Trust
- European Commission
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Isaac Newton Trust
- Engineering and Physical Sciences Research Council
- European Research Council
- Office of Naval Research
- U.S. Naval Research Laboratory
Citations
751
FWCI
32.46
field-weighted impact
References
72
Percentile
100%
vs. same field & year
Citations per year
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References
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The Journal of Chemical Physics · 1980 · 17,227 citations
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Physical review. B, Condensed matter · 1993 · 17,093 citations
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Physical Review Letters · 2007 · 4,776 citations
On representing chemical environments
Physical Review B · 2013 · 2,551 citations
Machine learning of molecular electronic properties in chemical compound space
New Journal of Physics · 2013 · 621 citations
Computer simulation of local order in condensed phases of silicon
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