Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning

Quantum Chemistry in the Age of Machine Learning

(2020) Pavlo O. Dral   Journal of Physical Chemistry Letters

An On-the-fly Approach to Construct Generalized Energy‒Based Fragmentation Machine Learning Force Fields of Complex Systems

(2020) Zheng Cheng et al.   JOURNAL OF PHYSICAL CHEMISTRY A

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

(2020) Justin S. Smith et al.   Scientific Data

A Structure-Based Platform for Predicting Chemical Reactivity

(2020) Frederik Sandfort et al.   Chem

Quantum machine learning using atom-in-molecule-based fragments selected on the fly

(2020) Bing Huang et al.   Nature Chemistry

Machine learning for chemical discovery

(2020) Alexandre Tkatchenko   Nature Communications

Accurate molecular polarizabilities with coupled cluster theory and machine learning

(2019) David M. Wilkins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Bridging the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surfaces Using Neural Networks

(2019) Yaolong Zhang et al.   Journal of Physical Chemistry Letters

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

(2019) Oliver T. Unke et al.   Journal of Chemical Theory and Computation

Phase Transformation Contributions to Heat Capacity and Impact on Thermal Diffusivity, Thermal Conductivity, and Thermoelectric Performance

(2019) Matthias T. Agne et al.   ADVANCED MATERIALS

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

(2019) Alex Zhavoronkov et al.   NATURE BIOTECHNOLOGY

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

(2019) Roman Zubatyuk et al.   Science Advances

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

(2019) K. T. Schütt et al.   Nature Communications

Correction to Analyzing Learned Molecular Representations for Property Prediction

(2019) Kevin Yang et al.   Journal of Chemical Information and Modeling

Hierarchical modeling of molecular energies using a deep neural network

(2018) Nicholas Lubbers et al.   JOURNAL OF CHEMICAL PHYSICS

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

(2018) Nongnuch Artrith et al.   JOURNAL OF CHEMICAL PHYSICS

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks

(2018) Lin Shen et al.   Journal of Chemical Theory and Computation

Toward Accurate Conformational Energies of Smaller Peptides and Medium-Sized Macrocycles: MPCONF196 Benchmark Energy Data Set

(2018) Jan Řezáč et al.   Journal of Chemical Theory and Computation

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

(2018) Deping Hu et al.   Journal of Physical Chemistry Letters

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

(2018) Kun Yao et al.   Chemical Science

MoleculeNet: a benchmark for molecular machine learning

(2018) Zhenqin Wu et al.   Chemical Science

Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors

(2018) Harikrishna Sahu et al.   Advanced Energy Materials

Machine learning hydrogen adsorption on nanoclusters through structural descriptors

(2018) Marc O. J. Jäger et al.   npj Computational Materials

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks

(2018) Benjamin Nebgen et al.   Journal of Chemical Theory and Computation

Discovering a Transferable Charge Assignment Model Using Machine Learning

(2018) Andrew E. Sifain et al.   Journal of Physical Chemistry Letters

Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks

(2018) Masashi Tsubaki et al.   Journal of Physical Chemistry Letters

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

(2018) K. T. Schütt et al.   Journal of Chemical Theory and Computation

Deep Learning for Nonadiabatic Excited-State Dynamics

(2018) Wen-Kai Chen et al.   Journal of Physical Chemistry Letters

A Survey of Multi‐Task Learning Methods in Chemoinformatics

(2018) Sergey Sosnin et al.   Molecular Informatics

Isobaric and Isochoric Heat Capacities of Imidazolium-Based and Pyrrolidinium-Based Ionic Liquids as a Function of Temperature: Modeling of Isobaric Heat Capacity

(2017) Edward Zorębski et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Direct Quantum Dynamics Using Grid-Based Wave Function Propagation and Machine-Learned Potential Energy Surfaces

(2017) Gareth W. Richings et al.   Journal of Chemical Theory and Computation

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

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

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

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

Py SCF: the Python-based simulations of chemistry framework

(2017) Qiming Sun et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

A universal strategy for the creation of machine learning-based atomistic force fields

(2017) Tran Doan Huan et al.   npj Computational Materials

Neural network molecular dynamics simulations of solid–liquid interfaces: water at low-index copper surfaces

(2016) Suresh Kondati Natarajan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

How van der Waals interactions determine the unique properties of water

(2016) Tobias Morawietz et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Many Molecular Properties from One Kernel in Chemical Space

(2015) Raghunathan Ramakrishnan et al.   CHIMIA

The AFLOW standard for high-throughput materials science calculations

(2015) Camilo E. Calderon et al.   COMPUTATIONAL MATERIALS SCIENCE

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

(2015) O. Anatole von Lilienfeld et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm

(2015) Michael Gastegger et al.   Journal of Chemical Theory and Computation

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

Deep learning

(2015) Yann LeCun et al.   NATURE

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

The Combined Fragmentation and Systematic Molecular Fragmentation Methods

(2014) Michael A. Collins et al.   ACCOUNTS OF CHEMICAL RESEARCH

Generalized Energy-Based Fragmentation Approach and Its Applications to Macromolecules and Molecular Aggregates

(2014) Shuhua Li et al.   ACCOUNTS OF CHEMICAL RESEARCH

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Effect of electronic spatial extents (ESE) of ions on overpotential of lithium ion capacitors

(2013) Fan Xu et al.   ELECTROCHIMICA ACTA

A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections

(2013) Tobias Morawietz et al.   JOURNAL OF PHYSICAL CHEMISTRY A

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Fragmentation Methods: A Route to Accurate Calculations on Large Systems

(2011) Mark S. Gordon et al.   CHEMICAL REVIEWS

Tensor factorizations of local second-order Møller–Plesset theory

(2011) Jun Yang et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Extended-Connectivity Fingerprints

(2010) David Rogers et al.   Journal of Chemical Information and Modeling

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

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

Molecular dissociation of hydrogen peroxide (HOOH) on a neural network ab initio potential surface with a new configuration sampling method involving gradient fitting

(2009) Hung M. Le et al.   JOURNAL OF CHEMICAL PHYSICS

Battery materials for ultrafast charging and discharging

(2009) Byoungwoo Kang et al.   NATURE

Towards the computational design of solid catalysts

(2009) J. K. Nørskov et al.   Nature Chemistry

Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS