Machine learning prediction of interaction energies in rigid water clusters

The many-body expansion combined with neural networks

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

Toward chemical accuracy in the description of ion–water interactions through many-body representations. Alkali-water dimer potential energy surfaces

(2017) Marc Riera 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

Force Field Parametrization of Metal Ions from Statistical Learning Techniques

(2017) Francesco Fracchia et al.   Journal of Chemical Theory and Computation

Machine Learning Force Field Parameters from Ab Initio Data

(2017) Ying Li 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

Predicting electronic structure properties of transition metal complexes with neural networks

(2017) Jon Paul Janet et al.   Chemical Science

On the accuracy of the MB-pol many-body potential for water: Interaction energies, vibrational frequencies, and classical thermodynamic and dynamical properties from clusters to liquid water and ice

(2016) Sandeep K. Reddy et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning

(2016) Timothy L. Fletcher et al.   Journal of Chemical Theory and Computation

When are Many-Body Effects Significant?

(2016) John F. Ouyang et al.   Journal of Chemical Theory and Computation

Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks

(2016) Kun Yao et al.   Journal of Chemical Theory and Computation

Incorporation of local structure into kriging models for the prediction of atomistic properties in the water decamer

(2016) Stuart J Davie et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

On the representation of many-body interactions in water

(2015) Gregory R. Medders et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

(2015) Pavlo O. Dral et al.   Journal of Chemical Theory and Computation

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

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

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

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

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

(2015) Tristan Bereau et al.   Journal of Chemical Theory and Computation

i-TTM Model for Ab Initio-Based Ion–Water Interaction Potentials. 1. Halide–Water Potential Energy Functions

(2015) Daniel J. Arismendi-Arrieta et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Development of a “First-Principles” Water Potential with Flexible Monomers. III. Liquid Phase Properties

(2014) Gregory R. Medders et al.   Journal of Chemical Theory and Computation

Development of a “First Principles” Water Potential with Flexible Monomers. II: Trimer Potential Energy Surface, Third Virial Coefficient, and Small Clusters

(2014) Volodymyr Babin et al.   Journal of Chemical Theory and Computation

Advances in molecular quantum chemistry contained in the Q-Chem 4 program package

(2014) Yihan Shao et al.   MOLECULAR PHYSICS

Development of a “First Principles” Water Potential with Flexible Monomers: Dimer Potential Energy Surface, VRT Spectrum, and Second Virial Coefficient

(2013) Volodymyr Babin et al.   Journal of Chemical Theory and Computation

Accuracy and tractability of a kriging model of intramolecular polarizable multipolar electrostatics and its application to histidine

(2013) Shaun M. Kandathil et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

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

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Polarisable multipolar electrostatics from the machine learning method Kriging: an application to alanine

(2012) Matthew J. L. Mills et al.   THEORETICAL CHEMISTRY ACCOUNTS

Support vector machine regression (SVR/LS-SVM)—an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data

(2011) Roman M. Balabin et al.   ANALYST

Flexible, ab initio potential, and dipole moment surfaces for water. I. Tests and applications for clusters up to the 22-mer

(2011) Yimin Wang et al.   JOURNAL OF CHEMICAL PHYSICS

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

Intramolecular polarisable multipolar electrostatics from the machine learning method Kriging

(2011) Matthew J.L. Mills et al.   Computational and Theoretical Chemistry

Comparative Study of Selected Wave Function and Density Functional Methods for Noncovalent Interaction Energy Calculations Using the Extended S22 Data Set

(2010) Lucie Gráfová et al.   Journal of Chemical Theory and Computation

Simulating Monovalent and Divalent Ions in Aqueous Solution Using a Drude Polarizable Force Field

(2010) Haibo Yu et al.   Journal of Chemical Theory and Computation

Potential Energy Surfaces Fitted by Artificial Neural Networks

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

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

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

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

Using neural networks, optimized coordinates, and high-dimensional model representations to obtain a vinyl bromide potential surface

(2008) Sergei Manzhos et al.   JOURNAL OF CHEMICAL PHYSICS

Beyond Point Charges: Dynamic Polarization from Neural Net Predicted Multipole Moments

(2008) Michael G. Darley et al.   Journal of Chemical Theory and Computation

Long-range corrected hybrid density functionals with damped atom–atom dispersion corrections

(2008) Jeng-Da Chai et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS