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      ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

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          Abstract

          Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples. We present the design and implementation of a deep neural network model referred to as ElemNet; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed. The speed and best-in-class accuracy of ElemNet enable us to perform a fast and robust screening for new material candidates in a huge combinatorial space; where we predict hundreds of thousands of chemical systems that could contain yet-undiscovered compounds.

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          Machine learning for molecular and materials science

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            SchNet – A deep learning architecture for molecules and materials

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              Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

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                Author and article information

                Contributors
                ankitag@eecs.northwestern.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 December 2018
                4 December 2018
                2018
                : 8
                : 17593
                Affiliations
                [1 ]ISNI 0000 0001 2299 3507, GRID grid.16753.36, Department of Electrical Engineering and Computer Science, , Northwestern University, ; Evanston, USA
                [2 ]ISNI 0000 0004 1936 7822, GRID grid.170205.1, Computation Institute, , University of Chicago, ; Chicago, USA
                [3 ]ISNI 0000 0001 2299 3507, GRID grid.16753.36, Department of Materials Science and Engineering, , Northwestern University, ; Evanston, USA
                Author information
                http://orcid.org/0000-0002-1323-5939
                Article
                35934
                10.1038/s41598-018-35934-y
                6279928
                30514926
                c9fce35e-6efe-43b9-90d3-cd43d03ba9a2
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 August 2018
                : 6 November 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000190, U.S. Department of Commerce (DOC);
                Award ID: 70NANB14H012
                Award ID: 70NANB14H12
                Award ID: 70NANB14H012
                Award ID: 70NANB14H012
                Award ID: 70NANB14H012
                Award ID: 70NANB14H012
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000161, DOC | National Institute of Standards and Technology (NIST);
                Award ID: 70NANB14H012
                Award ID: 70NANB14H012
                Award ID: 70NANB14H012
                Award ID: 70NANB14H012
                Award ID: 70NANB14H012
                Award Recipient :
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