Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimer

Interpolation and Extrapolation of Global Potential Energy Surfaces for Polyatomic Systems by Gaussian Processes with Composite Kernels

(2020) J. Dai et al.   Journal of Chemical Theory and Computation

Bayesian Optimization for Calibrating and Selecting Hybrid-Density Functional Models

(2020) R. A. Vargas−Hernández   JOURNAL OF PHYSICAL CHEMISTRY A

Bayesian optimization for the inverse scattering problem in quantum reaction dynamics

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

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

(2019) Stefan Chmiela et al.   COMPUTER PHYSICS COMMUNICATIONS

Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections

(2019) Yafu Guan et al.   JOURNAL OF CHEMICAL PHYSICS

Ab initio instanton rate theory made efficient using Gaussian process regression

(2018) Gabriel Laude et al.   FARADAY DISCUSSIONS

Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation

(2018) Gunnar Schmitz 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

Assessing Gaussian Process Regression and Permutationally Invariant Polynomial Approaches To Represent High-Dimensional Potential Energy Surfaces

(2018) Chen Qu et al.   Journal of Chemical Theory and Computation

Application of Clustering Algorithms to Partitioning Configuration Space in Fitting Reactive Potential Energy Surfaces

(2018) Yafu Guan et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Constructing High-Dimensional Neural Network Potential Energy Surfaces for Gas–Surface Scattering and Reactions

(2018) Qinghua Liu et al.   Journal of Physical Chemistry C

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

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Ab initio instanton rate theory made efficient using Gaussian process regression

(2018) Gabriel Laude et al.   FARADAY DISCUSSIONS

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

Phoenics: A Bayesian Optimizer for Chemistry

(2018) Florian Häse et al.   ACS Central Science

Extrapolating Quantum Observables with Machine Learning: Inferring Multiple Phase Transitions from Properties of a Single Phase

(2018) Rodrigo A. Vargas-Hernández et al.   PHYSICAL REVIEW LETTERS

The many-body expansion combined with neural networks

(2017) Kun Yao et al.   JOURNAL OF CHEMICAL PHYSICS

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

Reliable Estimation of Prediction Uncertainty for Physicochemical Property Models

(2017) Jonny Proppe et al.   Journal of Chemical Theory and Computation

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

(2017) S. T. John et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Construction of reactive potential energy surfaces with Gaussian process regression: active data selection

(2017) Yafu Guan et al.   MOLECULAR PHYSICS

A ground state potential energy surface for HONO based on a neural network with exponential fitting functions

(2017) Ekadashi Pradhan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

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

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

(2017) Justin S. Smith et al.   Scientific Data

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Optimization of an exchange-correlation density functional for water

(2016) Michelle Fritz et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes

(2016) Jie Cui et al.   JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS

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

Efficient sparsification for Gaussian process regression

(2016) Jens Schreiter et al.   NEUROCOMPUTING

Taking the Human Out of the Loop: A Review of Bayesian Optimization

(2016) Bobak Shahriari et al.   PROCEEDINGS OF THE IEEE

Neural Networks for the Prediction of Organic Chemistry Reactions

(2016) Jennifer N. Wei et al.   ACS Central Science

Efficient Optimization for Sparse Gaussian Process Regression

(2015) Yanshuai Cao et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers

(2015) Marco Caccin et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

The X1 family of methods that combines B3LYP with neural network corrections for an accurate yet efficient prediction of thermochemistry

(2015) Jianming Wu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Understanding kernel ridge regression: Common behaviors from simple functions to density functionals

(2015) Kevin Vu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

QCTFF: On the construction of a novel protein force field

(2015) Paul L. A. Popelier   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Gaussian approximation potentials: A brief tutorial introduction

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

Learning scheme to predict atomic forces and accelerate materials simulations

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

Gaussian Process Model for Collision Dynamics of Complex Molecules

(2015) Jie Cui et al.   PHYSICAL REVIEW LETTERS

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

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

Calculating vibrational spectra with sum of product basis functions without storing full-dimensional vectors or matrices

(2014) Arnaud Leclerc et al.   JOURNAL OF CHEMICAL PHYSICS

Assessing the reliability of calculated catalytic ammonia synthesis rates

(2014) A. J. Medford et al.   SCIENCE

A global potential energy surface for the H2 + OH ↔ H2O + H reaction using neural networks

(2013) Jun Chen et al.   JOURNAL OF CHEMICAL PHYSICS

Construction of high-dimensional neural network potentials using environment-dependent atom pairs

(2012) K. V. Jovan Jose et al.   JOURNAL OF CHEMICAL PHYSICS

Modeling Proton Transfer in Imidazole-like Dimers: A Density Functional Theory Study

(2011) Giuseppe Felice Mangiatordi et al.   JOURNAL OF PHYSICAL CHEMISTRY A

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

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Potential Energy Surfaces Fitted by Artificial Neural Networks

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

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

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

Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning

(2009) Chris M. Handley et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS