Machine Learning Estimates of Natural Product Conformational Energies

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

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

Understanding the Inhibitory Effect of Highly Potent and Selective Archazolides Binding to the Vacuolar ATPase

(2012) Sandra Dreisigacker 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

Bi-fidelity fitting and optimization

(2012) Ryan L. Miller 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

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

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

Neighborhood-Preserving Visualization of Adaptive Structure-Activity Landscapes: Application to Drug Discovery

(2011) Michael Reutlinger et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Molecular dynamics simulations and drug discovery

(2011) Jacob D Durrant et al.   BMC BIOLOGY

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

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Comparison of the performance of dispersion-corrected density functional theory for weak hydrogen bonds

(2011) Waldemar Hujo et al.   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

A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

(2010) Stefan Grimme et al.   JOURNAL OF CHEMICAL PHYSICS

Potential Energy Surfaces Fitted by Artificial Neural Networks

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

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

A self-learning algorithm for biased molecular dynamics

(2010) Gareth A. Tribello et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Approach to potential energy surfaces by neural networks. A review of recent work

(2009) Diogo A. R. S. Latino et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Inhibitors of V-ATPases: old and new players

(2009) M. Huss et al.   JOURNAL OF EXPERIMENTAL BIOLOGY

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

Simultaneous Determination of the Conformation and Relative Configuration of Archazolide A by Using Nuclear Overhauser Effects,J Couplings, and Residual Dipolar Couplings

(2008) Christophe Farès et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION