Kriging atomic properties with a variable number of inputs

Optimization Algorithms in Optimal Predictions of Atomistic Properties by Kriging

(2016) Nicodemo Di Pasquale 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

Molecular simulation by knowledgeable quantum atoms

(2016) Paul L A Popelier   PHYSICA SCRIPTA

Realistic sampling of amino acid geometries for a multipolar polarizable force field

(2015) Timothy J. Hughes et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Prediction of conformationally dependent atomic multipole moments in carbohydrates

(2015) Salvatore Cardamone et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Transferable kriging machine learning models for the multipolar electrostatics of helical deca-alanine

(2015) Timothy L. Fletcher et al.   THEORETICAL CHEMISTRY ACCOUNTS

Prediction of Intramolecular Polarization of Aromatic Amino Acids Using Kriging Machine Learning

(2014) Timothy L. Fletcher 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

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

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

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

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

Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning

(2009) Chris M. Handley et al.   Journal of Chemical Theory and Computation

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

Properties and 3D Structure of Liquid Water:  A Perspective from a High-Rank Multipolar Electrostatic Potential

(2008) Steven Y. Liem et al.   Journal of Chemical Theory and Computation