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      Machine learning reveals orbital interaction in materials

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          We propose a novel representation of materials named an ‘orbital-field matrix (OFM)’, which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies of crystalline materials, atomization energies of molecular materials, and local magnetic moments of the constituent atoms in bimetal alloys of lanthanide metal and transition-metal can be predicted with high accuracy using the OFM. Knowledge regarding the role of the coordination numbers of the transition-metal and lanthanide elements in determining the local magnetic moments of the transition-metal sites can be acquired directly from decision tree regression analyses using the OFM.

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          Most cited references 28

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          Generalized Gradient Approximation Made Simple.

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            Atom-centered symmetry functions for constructing high-dimensional neural network potentials.

             Jörg Behler (2011)
            Neural networks offer an unbiased and numerically very accurate approach to represent high-dimensional ab initio potential-energy surfaces. Once constructed, neural network potentials can provide the energies and forces many orders of magnitude faster than electronic structure calculations, and thus enable molecular dynamics simulations of large systems. However, Cartesian coordinates are not a good choice to represent the atomic positions, and a transformation to symmetry functions is required. Using simple benchmark systems, the properties of several types of symmetry functions suitable for the construction of high-dimensional neural network potential-energy surfaces are discussed in detail. The symmetry functions are general and can be applied to all types of systems such as molecules, crystalline and amorphous solids, and liquids.
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              Accelerating materials property predictions using machine learning

              The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials.

                Author and article information

                Sci Technol Adv Mater
                Sci Technol Adv Mater
                Science and Technology of Advanced Materials
                Taylor & Francis
                26 October 2017
                : 18
                : 1
                : 756-765
                [ a ] Japan Advanced Institute of Science and Technology , Nomi, Japan.
                [ b ] Elements Strategy Initiative Center for Magnetic Materials, National Institute for Materials Science , Tsukuba, Japan.
                [ c ] Center for Materials Research by Information Integration, Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science , Tsukuba, Japan.
                [ d ] Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology , Tsukuba, Japan.
                [ e ] JST, PRESTO , Kawaguchi, Japan.
                [ f ] Graduate School of Information Science and Technology, Hokkaido University , Sapporo, Japan.
                [ g ] Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo , Kashiwa, Japan.
                [ h ] RIKEN Center for Advanced Intelligence Project , Tokyo, Japan.
                Author notes
                © 2017 Informa UK Limited, trading as Taylor & Francis Group

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 4, Tables: 3, Equations: 125, References: 40, Pages: 10
                Funded by: Precursory Research for Embryonic Science and Technology 10.13039/501100009023
                This work was supported in part by Precursory Research for Embryonic Science and Technology from Japan Science and Technology Agency (JST), by the Elements Strategy Initiative Project under the auspice of MEXT, by ‘Materials research by Information Integration’ Initiative (MI2 I) project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency (JST), by MEXT as a social and scientific priority issue (Creation of new functional devices and high-performance materials to support next-generation industries; CDMSI) to be tackled by using post-K computer, and also by JSPS KAKENHI [Grant Numbers 17K19953 and 17H01783]..
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