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      Quantum-chemical insights from deep tensor neural networks

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          Abstract

          Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol −1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

          Abstract

          Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning' framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in the training data.

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          Most cited references48

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          Generalized Gradient Approximation Made Simple

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            Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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              Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density

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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                09 January 2017
                2017
                : 8
                : 13890
                Affiliations
                [1 ]Machine Learning Group, Technische Universität Berlin , Marchstr. 23, 10587 Berlin, Germany
                [2 ]Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu , Seoul 136-713, Republic of Korea
                [3 ]Theory Department, Fritz-Haber-Institut der Max-Planck-Gesellschaft , Faradayweg 4-6, D-14195 Berlin, Germany
                [4 ]Physics and Materials Science Research Unit, University of Luxembourg, Luxembourg, , L-1511 Luxembourg
                Author notes
                Article
                ncomms13890
                10.1038/ncomms13890
                5228054
                28067221
                6ac266aa-22a3-450e-b7cd-f1290ae8ddb1
                Copyright © 2017, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 24 June 2016
                : 09 November 2016
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