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

Optimization Algorithms in Optimal Predictions of Atomistic Properties by Kriging

(2016) Nicodemo Di Pasquale et al.   Journal of Chemical Theory and Computation

QCTFF: On the construction of a novel protein force field

(2015) Paul L. A. Popelier   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

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

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

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

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

(2015) Matthias Rupp et al.   Journal of Physical Chemistry 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

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

Hydrogen-Bond Cooperative Effects in Small Cyclic Water Clusters as Revealed by the Interacting Quantum Atoms Approach

(2013) José Manuel Guevara-Vela et al.   CHEMISTRY-A EUROPEAN JOURNAL

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

Unified approach to multipolar polarisation and charge transfer for ions: microhydrated Na+

(2013) Matthew J. L. Mills et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

A water potential based on multipole moments trained by machine learning — Reducing maximum energy errors

(2010) Glenn I. Hawe et al.   CANADIAN JOURNAL OF CHEMISTRY

Towards an ab initio flexible potential for water, and post-harmonic quantum vibrational analysis of water clusters

(2010) Yimin Wang et al.   CHEMICAL PHYSICS LETTERS

Properties of liquid water from a systematic refinement of a high-rank multipolar electrostatic potential

(2010) Majeed S. Shaik et al.   JOURNAL OF CHEMICAL PHYSICS

Towards the complete understanding of water by a first-principles computational approach

(2009) Krzysztof Szalewicz et al.   CHEMICAL PHYSICS 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

What ice can teach us about water interactions: a critical comparison of the performance of different water models

(2008) C. Vega et al.   FARADAY DISCUSSIONS

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

Subset selection from large datasets for Kriging modeling

(2008) Gijs Rennen   STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION