Journal ArticleDOI
Quantum Chemistry in the Age of Machine Learning.
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TLDR
A view on the current state of affairs in this new exciting research field is offered, challenges of using ML in QC applications are described, and potential future developments are outlined.Abstract:
As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.read more
Citations
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Journal Article
Quantum-Chemical Insights from Deep Tensor Neural Networks
Kristof T. Sch "utt,Farhad Arbabzadah,Stefan Chmiela,Klaus-Robert M "uller,Alexandre Tkatchenko +4 more
TL;DR: An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.
Journal ArticleDOI
Machine Learning of Molecular Electronic Properties in Chemical Compound Space
Grégoire Montavon,Matthias Rupp,Vivekanand V. Gobre,Álvaro Vázquez-Mayagoitia,Katja Hansen,Alexandre Tkatchenko,Alexandre Tkatchenko,Klaus-Robert Müller,Klaus-Robert Müller,O. Anatole von Lilienfeld +9 more
TL;DR: In this paper, a deep multi-task artificial neural network is used to predict multiple electronic ground-and excited-state properties, such as atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies.
Journal ArticleDOI
Gaussian Process Regression for Materials and Molecules.
Volker L. Deringer,Albert P. Bartók,Noam Bernstein,David M. Wilkins,Michele Ceriotti,Gábor Csányi +5 more
TL;DR: In this paper, the authors provide an introduction to Gaussian process regression (GPR) machine learning methods in computational materials science and chemistry, focusing on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian approximation potential (GAP) framework.
Journal ArticleDOI
Machine Learning for Electronically Excited States of Molecules.
TL;DR: In this article, a review of machine learning for excited states of molecules is presented, focusing on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects.
Journal ArticleDOI
Quantum Machine Learning in Chemical Compound Space.
TL;DR: The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.
References
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Journal ArticleDOI
Inhomogeneous Electron Gas
P. C. Hohenberg,Walter Kohn +1 more
TL;DR: In this article, the ground state of an interacting electron gas in an external potential was investigated and it was proved that there exists a universal functional of the density, called F[n(mathrm{r})], independent of the potential of the electron gas.
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A Chemist's Guide to Density Functional Theory
Wolfram Koch,Max C. Holthausen +1 more
TL;DR: A Chemist's Guide to Density Functional Theory should be an invaluable source of insight and knowledge for many chemists using DFT approaches to solve chemical problems.
Journal ArticleDOI
Generalized neural-network representation of high-dimensional potential-energy surfaces.
Jörg Behler,Michele Parrinello +1 more
TL;DR: A new kind of neural-network representation of DFT potential-energy surfaces is introduced, which provides the energy and forces as a function of all atomic positions in systems of arbitrary size and is several orders of magnitude faster than DFT.
Journal ArticleDOI
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.
David Silver,Thomas Hubert,Julian Schrittwieser,Ioannis Antonoglou,Matthew Lai,Arthur Guez,Marc Lanctot,Laurent Sifre,Dharshan Kumaran,Thore Graepel,Timothy P. Lillicrap,Karen Simonyan,Demis Hassabis +12 more
TL;DR: This paper generalizes the AlphaZero approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games, and convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.
Journal ArticleDOI
Machine learning for molecular and materials science.
TL;DR: A future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence is envisaged.
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