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Machine learning unifies the modeling of materials and molecules
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Bartók, Albert P., De, Sandip, Poelking, Carl, Bernstein, Noam, Kermode, James R., Csányi, Gábor and Ceriotti, Michele (2017) Machine learning unifies the modeling of materials and molecules. Science Advances, 3 (12). e1701816. doi:10.1126/sciadv.1701816 ISSN 2375-2548.
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WRAP-machine-learning-unifies-modeling-materials-molecules-Kermode-2017.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons: Attribution-Noncommercial 4.0. Download (2233Kb) | Preview |
Official URL: http://dx.doi.org/10.1126/sciadv.1701816
Abstract
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a local description of chemical environments and Bayesian statistical learning, provides a unified framework to predict atomic-scale properties. It captures the quantum mechanical effects governing the complex surface reconstructions of silicon, predicts the stability of different classes of molecules with chemical accuracy, and distinguishes active and inactive protein ligands with more than 99% reliability. The universality and the systematic nature of our framework provide new insight into the potential energy surface of materials and molecules.
Item Type: | Journal Article | ||||||||||||||||||||||||||||||||||||||||||||||||
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Subjects: | Q Science > Q Science (General) | ||||||||||||||||||||||||||||||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||||||||||||||||||||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Machine learning , Molecules -- Models | ||||||||||||||||||||||||||||||||||||||||||||||||
Journal or Publication Title: | Science Advances | ||||||||||||||||||||||||||||||||||||||||||||||||
Publisher: | American Association for the Advancement of Science | ||||||||||||||||||||||||||||||||||||||||||||||||
ISSN: | 2375-2548 | ||||||||||||||||||||||||||||||||||||||||||||||||
Official Date: | 13 December 2017 | ||||||||||||||||||||||||||||||||||||||||||||||||
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Volume: | 3 | ||||||||||||||||||||||||||||||||||||||||||||||||
Number: | 12 | ||||||||||||||||||||||||||||||||||||||||||||||||
Article Number: | e1701816 | ||||||||||||||||||||||||||||||||||||||||||||||||
DOI: | 10.1126/sciadv.1701816 | ||||||||||||||||||||||||||||||||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||||||||||||||||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||||||||||||||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||||||||||||||||||||||||||||||||
Date of first compliant deposit: | 14 December 2017 | ||||||||||||||||||||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 14 December 2017 | ||||||||||||||||||||||||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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