Atomistic structure learning

Reinforced Adversarial Neural Computer for de Novo Molecular Design

(2018) Evgeny Putin et al.   Journal of Chemical Information and Modeling

Data-Driven Learning of Total and Local Energies in Elemental Boron

(2018) Volker L. Deringer et al.   PHYSICAL REVIEW LETTERS

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Accelerating the discovery of materials for clean energy in the era of smart automation

(2018) Daniel P. Tabor et al.   Nature Reviews Materials

Atomic structure of boron resolved using machine learning and global sampling

(2018) Si-Da Huang et al.   Chemical Science

Deep reinforcement learning for de novo drug design

(2018) Mariya Popova et al.   Science Advances

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

The atomic simulation environment—a Python library for working with atoms

(2017) Ask Hjorth Larsen et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Mastering the game of Go without human knowledge

(2017) David Silver et al.   NATURE

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Molecular de-novo design through deep reinforcement learning

(2017) Marcus Olivecrona et al.   Journal of Cheminformatics

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

(2016) Bing Huang et al.   JOURNAL OF CHEMICAL PHYSICS

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

(2016) Rafael Gómez-Bombarelli et al.   NATURE MATERIALS

Global minimization of gold clusters by combining neural network potentials and the basin-hopping method

(2015) Runhai Ouyang et al.   Nanoscale

The favourable large misorientation angle grain boundaries in graphene

(2015) Xiuyun Zhang et al.   Nanoscale

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

(2015) Sebastian Bach et al.   PLoS One

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

(2013) James E. Saal et al.   JOM

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Discovery of an  -Amino C-H Arylation Reaction Using the Strategy of Accelerated Serendipity

(2011) A. McNally et al.   SCIENCE

Folding of a Helix at Room Temperature Is Critically Aided by Electrostatic Polarization of Intraprotein Hydrogen Bonds

(2010) Li L. Duan et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING