Hierarchical machine learning of potential energy surfaces

FCHL revisited: Faster and more accurate quantum machine learning

(2020) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

Machine learning for interatomic potential models

(2020) Tim Mueller et al.   JOURNAL OF CHEMICAL PHYSICS

Quantum Chemistry in the Age of Machine Learning

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

SciPy 1.0: fundamental algorithms for scientific computing in Python

(2020) Pauli Virtanen et al.   NATURE METHODS

Bayesian optimization for the inverse scattering problem in quantum reaction dynamics

(2019) R A Vargas-Hernández et al.   NEW JOURNAL OF PHYSICS

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

Approximate high mode coupling potentials using Gaussian process regression and adaptive density guided sampling

(2019) Gunnar Schmitz et al.   JOURNAL OF CHEMICAL PHYSICS

A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules

(2019) Lixue Cheng et al.   JOURNAL OF CHEMICAL PHYSICS

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

MLatom : A program package for quantum chemical research assisted by machine learning

(2019) Pavlo O. Dral   JOURNAL OF COMPUTATIONAL CHEMISTRY

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

(2019) Ryosuke Jinnouchi et al.   PHYSICAL REVIEW LETTERS

Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks

(2019) Geng Sun et al.   Journal of Chemical Theory and Computation

Harnessing machine learning potentials to understand the functional properties of phase-change materials

(2019) G.C. Sosso et al.   MRS BULLETIN

Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations

(2019) Logan Ward et al.   MRS Communications

Structure and dynamics of warm dense aluminum: A molecular dynamics study with density functional theory and deep potential

(2019) Qianrui Liu et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Quantum Machine Learning in Chemical Compound Space

(2018) O. Anatole von Lilienfeld   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

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

Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy

(2018) Aditya Kamath et al.   JOURNAL OF CHEMICAL PHYSICS

Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

(2018) Matthias Rupp et al.   JOURNAL OF CHEMICAL PHYSICS

Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(101¯0) interface from a high-dimensional neural network potential

(2018) Vanessa Quaranta 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

Making the Coupled Cluster Correlation Energy Machine-Learnable

(2018) Johannes T Margraf et al.   JOURNAL OF PHYSICAL CHEMISTRY A

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

Force Field for Water Based on Neural Network

(2018) Hao Wang et al.   Journal of Physical Chemistry Letters

ExoMol line lists – XXIX. Therotation-vibration spectrum of methyl chloride up to 1200 K

(2018) A Owens et al.   MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

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

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning

(2018) Miguel A. Caro et al.   CHEMISTRY OF MATERIALS

Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis

(2018) Matthew Welborn et al.   Journal of Chemical Theory and Computation

Nonadiabatic Excited-State Dynamics with Machine Learning

(2018) Pavlo O. Dral et al.   Journal of Physical Chemistry Letters

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

A transferable artificial neural network model for atomic forces in nanoparticles

(2018) Shweta Jindal et al.   JOURNAL OF CHEMICAL PHYSICS

Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited

(2018) Peter Zaspel et al.   Journal of Chemical Theory and Computation

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

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

Accelerated Computation of Free Energy Profile at ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semi-Empirical Reference Potential. I. Weighted Thermodynamics Perturbation

(2018) Pengfei Li 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

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

(2017) Pavlo O. Dral et al.   JOURNAL OF CHEMICAL PHYSICS

Neural Network and Nearest Neighbor Algorithms for Enhancing Sampling of Molecular Dynamics

(2017) Raimondas Galvelis et al.   Journal of Chemical Theory and Computation

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

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

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

Machine learning for atomic forces in a crystalline solid: Transferability to various temperatures

(2016) Teppei Suzuki et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Perspective: Accurate ro-vibrational calculations on small molecules

(2016) Jonathan Tennyson   JOURNAL OF CHEMICAL PHYSICS

Communication: Fitting potential energy surfaces with fundamental invariant neural network

(2016) Kejie Shao et al.   JOURNAL OF CHEMICAL PHYSICS

A highly accurate ab initio potential energy surface for methane

(2016) Alec Owens et al.   JOURNAL OF CHEMICAL PHYSICS

Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks

(2016) Brian Kolb et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks

(2016) Lin Shen et al.   Journal of Chemical Theory and Computation

Machine Learning Force Fields: Construction, Validation, and Outlook

(2016) V. Botu et al.   Journal of Physical Chemistry C

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Accurate ab initio vibrational energies of methyl chloride

(2015) Alec Owens et al.   JOURNAL OF CHEMICAL PHYSICS

Electronic spectra from TDDFT and machine learning in chemical space

(2015) Raghunathan Ramakrishnan et al.   JOURNAL OF CHEMICAL PHYSICS

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

Learning scheme to predict atomic forces and accelerate materials simulations

(2015) V. Botu et al.   PHYSICAL REVIEW B

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Continuous Global Optimization inR

(2015) Katharine M. Mullen   Journal of Statistical Software

Adaptive machine learning framework to accelerateab initiomolecular dynamics

(2014) Venkatesh Botu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces

(2014) Sergei Manzhos et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Alternative Approach to Chemical Accuracy: A Neural Networks-Based First-Principles Method for Heat of Formation of Molecules Made of H, C, N, O, F, S, and Cl

(2014) Jian Sun et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

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

A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges

(2012) Tobias Morawietz et al.   JOURNAL OF CHEMICAL PHYSICS

Chemical accuracy in ab initio thermochemistry and spectroscopy: current strategies and future challenges

(2012) Kirk A. Peterson et al.   THEORETICAL CHEMISTRY ACCOUNTS

Nucleation mechanism for the direct graphite-to-diamond phase transition

(2011) Rustam Z. Khaliullin et al.   NATURE MATERIALS

Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

(2011) Roman M. Balabin et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Molpro: a general-purpose quantum chemistry program package

(2011) Hans-Joachim Werner et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Potential Energy Surfaces Fitted by Artificial Neural Networks

(2010) Chris M. Handley et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Ab initioquality neural-network potential for sodium

(2010) Hagai Eshet et al.   PHYSICAL REVIEW B

Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree–Fock energies, and small subsets of the database

(2009) M. Malshe et al.   JOURNAL OF CHEMICAL PHYSICS

Extrapolating MP2 and CCSD explicitly correlated correlation energies to the complete basis set limit with first and second row correlation consistent basis sets

(2009) J. Grant Hill et al.   JOURNAL OF CHEMICAL PHYSICS

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

Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

(2009) Roman M. Balabin et al.   JOURNAL OF CHEMICAL PHYSICS

Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks

(2009) A. Pukrittayakamee et al.   JOURNAL OF CHEMICAL PHYSICS

Molecular Dynamics Investigation of the Bimolecular Reaction BeH + H2→ BeH2+ H on an ab Initio Potential-Energy Surface Obtained Using Neural Network Methods with Both Potential and Gradient Accuracy Determination

(2009) Hung M. Le et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Improving the B3LYP bond energies by using the X1 method

(2008) Jianming Wu et al.   JOURNAL OF CHEMICAL PHYSICS

Cis→trans, trans→cis isomerizations and N–O bond dissociation of nitrous acid (HONO) on an ab initio potential surface obtained by novelty sampling and feed-forward neural network fitting

(2008) Hung M. Le et al.   JOURNAL OF CHEMICAL PHYSICS

Quantitative quantum chemistry

(2008) Trygve Helgaker et al.   MOLECULAR PHYSICS

Pressure-induced phase transitions in silicon studied by neural network-based metadynamics simulations

(2008) Jörg Behler et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

Nonadiabatic effects in the dissociation of oxygen molecules at the Al(111) surface

(2008) Jörg Behler et al.   PHYSICAL REVIEW B

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

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS