Active learning in Gaussian process interpolation of potential energy surfaces

Interpolation of intermolecular potentials using Gaussian processes

(2017) Elena Uteva et al.   JOURNAL OF CHEMICAL PHYSICS

Construction of reactive potential energy surfaces with Gaussian process regression: active data selection

(2017) Yafu Guan et al.   MOLECULAR PHYSICS

Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS

(2016) Alexander J. Cresswell et al.   FARADAY DISCUSSIONS

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

Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS

(2016) Alexander J. Cresswell et al.   FARADAY DISCUSSIONS

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Machine Learning Estimates of Natural Product Conformational Energies

(2014) Matthias Rupp et al.   PLoS Computational Biology

Potential Energy Surfaces Fitted by Artificial Neural Networks

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

Active Learning Through Sequential Design, With Applications to Detection of Money Laundering

(2009) Xinwei Deng et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION