Machine learning unifies the modeling of materials and molecules. Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. Science Advances, American Association for the Advancement of Science, December, 2017.
Machine learning unifies the modeling of materials and molecules [link]Paper  abstract   bibtex   
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.
@article{wrap96346,
          volume = {3},
          number = {12},
           month = {December},
          author = {Albert P. Bart{\'o}k and Sandip De and Carl Poelking and Noam Bernstein and James R. Kermode and G{\'a}bor Cs{\'a}nyi and Michele Ceriotti},
           title = {Machine learning unifies the modeling of materials and molecules},
       publisher = {American Association for the Advancement of Science},
         journal = {Science Advances},
            year = {2017},
        keywords = {DataAyDn DataSy Datai Datar},
             url = {https://wrap.warwick.ac.uk/96346/},
        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.
}
}
Downloads: 0
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021