Machine learning for quantum mechanics in a nutshell

Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives

(2015) John C. Snyder 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

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

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

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

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

Representing potential energy surfaces by high-dimensional neural network potentials

(2014) J Behler   JOURNAL OF PHYSICS-CONDENSED MATTER

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids

(2014) Atsuto Seko et al.   PHYSICAL REVIEW B

Machine Learning Estimates of Natural Product Conformational Energies

(2014) Matthias Rupp et al.   PLoS Computational Biology

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

(2013) O. Anatole von Lilienfeld   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Reinforcement learning in robotics: A survey

(2013) Jens Kober et al.   INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH

Orbital-free bond breaking via machine learning

(2013) John C. Snyder et al.   JOURNAL OF CHEMICAL PHYSICS

Automated Force Field Parameterization for Nonpolarizable and Polarizable Atomic Models Based on Ab Initio Target Data

(2013) Lei Huang et al.   Journal of Chemical Theory and Computation

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

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

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

(2013) Anubhav Jain et al.   APL Materials

DFT in a nutshell

(2012) Kieron Burke et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?

(2012) Alexandre Varnek et al.   Journal of Chemical Information and Modeling

Optimizing transition states via kernel-based machine learning

(2012) Zachary D. Pozun et al.   JOURNAL OF CHEMICAL PHYSICS

Finding Density Functionals with Machine Learning

(2012) John C. Snyder et al.   PHYSICAL REVIEW LETTERS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

\mathcal{O}(N) methods in electronic structure calculations

(2012) D R Bowler et al.   REPORTS ON PROGRESS IN PHYSICS

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

(2011) Jörg Behler   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

Introduction to machine learning for brain imaging

(2010) Steven Lemm et al.   NEUROIMAGE

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

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

970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13

(2009) Lorenz C. Blum et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

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

Kernel methods in machine learning

(2008) Thomas Hofmann et al.   ANNALS OF STATISTICS