activity
20152019
most citedSymmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems

322 citations · 324 across the 3 of their papers we have counts for

collaborators

5 papers

physics.chem-ph2019

Inexpensive modelling of quantum dynamics using path integral generalized Langevin equation thermostats

Venkat Kapil, David M. Wilkins, Jinggang Lan +1

The properties of molecules and materials containing light nuclei are affected by their quantum mechanical nature. Modelling these quantum nuclear effects accurately requires compu…

physics.chem-ph20191 cited

Atomic-scale representation and statistical learning of tensorial properties

Andrea Grisafi, David M. Wilkins, Michael J. Willatt +1

This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial…

cond-mat.mtrl-sci2017322 cited

Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems

Andrea Grisafi, David M. Wilkins, Gábor Csányi +1

Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding e…

physics.chem-ph2017

Solvent Fluctuations and Nuclear Quantum Effects Modulate the Molecular Hyperpolarizability of Water

Chungwen Liang, Gabriele Tocci, David Wilkins +3

Second-Harmonic Scatteringh (SHS) experiments provide a unique approach to probe non-centrosymmetric environments in aqueous media, from bulk solutions to interfaces, living cells…

physics.chem-ph20151 cited

A Theoretical Investigation Into Energy Transfer In Photosynthetic Open Quantum Systems

David M. Wilkins

This thesis looks at the electronic energy transfer in the Fenna-Matthews-Olson complex, in which evidence of long-lived coherence has been observed in 2-dimensional infrared exper…