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

Training Neural Nets to Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality

(2019) Silvia Amabilino et al.   JOURNAL OF PHYSICAL CHEMISTRY A

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

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

Gaussian process regression for geometry optimization

(2018) Alexander Denzel et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation

(2018) Gunnar Schmitz et al.   JOURNAL OF CHEMICAL PHYSICS

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen et al.   JOURNAL OF CHEMICAL PHYSICS

Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

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

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

Gaussian Process Regression for Transition State Search

(2018) Alexander Denzel et al.   Journal of Chemical Theory and Computation

SAMPL6 challenge results from pKa predictions based on a general Gaussian process model

(2018) Caitlin C. Bannan et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

(2018) David Silver et al.   SCIENCE

Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-Rank Matrix Completion

(2018) Abiy Tasissa et al.   IEEE TRANSACTIONS ON INFORMATION THEORY

Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces

(2017) Oliver T. Unke et al.   Journal of Chemical Information and Modeling

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

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

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

Machine learning for quantum mechanics in a nutshell

(2015) Matthias Rupp   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

(2015) Pavlo O. Dral 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

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

CODATA recommended values of the fundamental physical constants: 2010

(2012) Peter J. Mohr et al.   REVIEWS OF MODERN PHYSICS

Matérn Cross-Covariance Functions for Multivariate Random Fields

(2010) Tilmann Gneiting et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION