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Scientific Reports|October 11, 2017
Geometry Optimization with Machine Trained Topological AtomsFrançois Zielinski, Peter I Maxwell, Timothy L Fletcher, et al.Journal of Computational Chemistry|September 27, 2022
Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulationMatthew J Burn, Paul L A PopelierJournal of Chemical Information and Modeling|January 28, 2014
Theoretical prediction of hydrogen-bond basicity pKBHX using quantum chemical topology descriptorsAnthony J Green, Paul L A PopelierPhysical Chemistry Chemical Physics : PCCP|September 3, 2024
Transfer learning of hyperparameters for fast construction of anisotropic GPR models: design and application to the machine-learned force field FFLUXBienfait K Isamura, Paul L A PopelierThe Journal of Physical Chemistry. A|October 11, 2024
Modeling Many-Body Interactions in Water with Gaussian Process RegressionYulian T Manchev, Paul L A PopelierThe Journal of Physical Chemistry. A|September 20, 2008
Role of short-range electrostatics in torsional potentialsMichael G Darley, Paul L A PopelierThe Journal of Physical Chemistry. A|February 6, 2010
Potential energy surfaces fitted by artificial neural networksChris M Handley, Paul L A PopelierThe Journal of Chemical Physics|August 11, 2020
Creating Gaussian process regression models for molecular simulations using adaptive samplingMatthew J Burn, Paul L A PopelierJournal of Chemical Theory and Computation|December 2, 2015
Properties and 3D Structure of Liquid Water: A Perspective from a High-Rank Multipolar Electrostatic PotentialSteven Y Liem, Paul L A PopelierJournal of Molecular Modeling|July 12, 2018
MP2-IQA: upscaling the analysis of topologically partitioned electron correlationArnaldo F Silva, Paul L A PopelierPageof 15