Outputs (9)

Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations (2023)
Journal Article
Broad, J., Wheatley, R. J., & Graham, R. S. (2023). Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations. Journal of Chemical Theory and Computation, 19(13), 4322-4333. https://doi.org/10.1021/acs.jctc.3c00113

A strategy is presented to implement Gaussian process potentials in molecular simulations through parallel programming. Attention is focused on the three-body nonadditive energy, though all algorithms extend straightforwardly to the additive energy.... Read More about Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations.

Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions (2022)
Journal Article
Graham, R. S., & Wheatley, R. J. (2022). Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions. Chemical Communications, 58(49), 6898-6901. https://doi.org/10.1039/d2cc01820a

Prediction of thermophysical properties from molecular principles requires accurate potential energy surfaces (PES). We present a widely-applicable method to produce first-principles PES from quantum chemistry calculations. Our approach accurately in... Read More about Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions.

Gaussian Process Models of Potential Energy Surfaces with Boundary Optimisation (2021)
Journal Article
Broad, J., Preston, S., Wheatley, R. J., & Graham, R. S. (2021). Gaussian Process Models of Potential Energy Surfaces with Boundary Optimisation. Journal of Chemical Physics, 155(14), Article 144106. https://doi.org/10.1063/5.0063534

A strategy is outlined to reduce the number of training points required to model intermolecular potentials using Gaussian processes, without reducing accuracy. An asymptotic function is used at long range and the cross-over distance between this mode... Read More about Gaussian Process Models of Potential Energy Surfaces with Boundary Optimisation.

Gaussian process models of potential energy surfaces with boundary optimization (2021)
Journal Article
Broad, J., Preston, S., Wheatley, R. J., & Graham, R. S. (2021). Gaussian process models of potential energy surfaces with boundary optimization. Journal of Chemical Physics, 155(14), Article 144106. https://doi.org/10.1063/5.0063534

A strategy is outlined to reduce the number of training points required to model intermolecular potentials using Gaussian processes, without reducing accuracy. An asymptotic function is used at a long range, and the crossover distance between this mo... Read More about Gaussian process models of potential energy surfaces with boundary optimization.

Active learning in Gaussian process interpolation of potential energy surfaces (2018)
Journal Article
Uteva, E., Graham, R. S., Wilkinson, R. D., & Wheatley, R. J. (2018). Active learning in Gaussian process interpolation of potential energy surfaces. Journal of Chemical Physics, 149(17), 174114. https://doi.org/10.1063/1.5051772

© 2018 Author(s). Three active learning schemes are used to generate training data for Gaussian process interpolation of intermolecular potential energy surfaces. These schemes aim to achieve the lowest predictive error using the fewest points and th... Read More about Active learning in Gaussian process interpolation of potential energy surfaces.

Interpolation of intermolecular potentials using Gaussian processes (2017)
Journal Article
Uteva, E., Graham, R. S., Wilkinson, R. D., & Wheatley, R. J. (2017). Interpolation of intermolecular potentials using Gaussian processes. Journal of Chemical Physics, 147(16), Article 161706. https://doi.org/10.1063/1.4986489

A procedure is proposed to produce intermolecular potential energy surfaces from limited data. The procedure involves generation of geometrical configurations using a Latin hypercube design, with a maximin criterion, based on inverse internuclear dis... Read More about Interpolation of intermolecular potentials using Gaussian processes.

Palladium nanoparticles in catalytic carbon nanoreactors: the effect of confinement on Suzuki-Miyaura reactions (2015)
Journal Article
Cornelio, B., Saunders, A., Solomonsz, W., Laronze-Cochard, M., Fontana, A., Sapi, J., Khlobystov, A. N., & Rance, G. A. (in press). Palladium nanoparticles in catalytic carbon nanoreactors: the effect of confinement on Suzuki-Miyaura reactions. Journal of Materials Chemistry, 3, https://doi.org/10.1039/C4TA06953F

We explore the construction and performance of a range of catalytic nanoreactors based on palladium nanoparticles encapsulated in hollow graphitised nanofibres. The optimum catalytic material, with small palladium nanoparticles located almost exclusi... Read More about Palladium nanoparticles in catalytic carbon nanoreactors: the effect of confinement on Suzuki-Miyaura reactions.

Evaluating the effects of carbon nanoreactor diameter and internal structure on the pathways of the catalytic hydrosilylation reaction (2014)
Journal Article
Solomonsz, W. A., Rance, G. A., & Khlobystov, A. N. (2014). Evaluating the effects of carbon nanoreactor diameter and internal structure on the pathways of the catalytic hydrosilylation reaction. Small, 10(9),

Three different types of carbon nanoreactors, double-walled nanotubes (DWNT), multi-walled nanotubes (MWNT) and graphitised carbon nanofibers (GNF) have been appraised for the first time as containers for the reactions of phenylacetylene hydrosilylat... Read More about Evaluating the effects of carbon nanoreactor diameter and internal structure on the pathways of the catalytic hydrosilylation reaction.

Palladium nanoparticles on carbon nanotubes as catalysts of cross-coupling reactions (2013)
Journal Article
Cornelio, B., Rance, G. A., Laronze-Cochard, M., Fontana, A., Sapi, J., & Khlobystov, A. N. (in press). Palladium nanoparticles on carbon nanotubes as catalysts of cross-coupling reactions. Journal of Materials Chemistry, 1, https://doi.org/10.1039/C3TA11530E

The macroscopic properties of composite nanotube-nanoparticle superstructures are determined by a complex interplay of structural parameters at the nanoscale. The catalytic performance of different carbon nanotube-palladium nanoparticle catalysts, wh... Read More about Palladium nanoparticles on carbon nanotubes as catalysts of cross-coupling reactions.