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Classification of platinum nanoparticle catalysts using machine learning

Journal of applied physics, 2020-07, Vol.128 (1)

2020

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Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning

Nanoscale, 2019-12, Vol.11 (48), p.23165-23172

2019

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Precursor-mediated crystallization process in suspensions of hard spheres

Physical review letters, 2010-07, Vol.105 (2), p.025701-025701, Article 025701

2010

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Nanoinformatics, and the big challenges for the science of small things

Nanoscale, 2019-11, Vol.11 (41), p.1919-1921

2019

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Simulating the fabrication of aluminium oxide tunnel junctions

npj quantum information, 2021-01, Vol.7 (1), p.1-12, Article 12

2021

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Vacancy induced formation of nanoporous silicon, carbon and silicon carbide

Physical chemistry chemical physics : PCCP, 2019-03, Vol.21 (12), p.6517-6524

2019

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Stability and Transformations of Heated Gold Nanorods

Journal of physical chemistry. C, 2011-03, Vol.115 (11), p.4375-4380

2011

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HRMC_2.1: Hybrid Reverse Monte Carlo method with silicon, carbon, germanium and silicon carbide potentials

Computer physics communications, 2014-06, Vol.185 (6), p.1854-1855

2014

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Hybrid Reverse Monte Carlo and electron phase contrast image simulations of amorphous silicon with and without paracrystals

Molecular simulation, 2016-05, Vol.42 (6-7), p.522-530

2016

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Ideality versus Reality: Emergence of the Chui Icosahedron

Journal of physical chemistry. C, 2008-09, Vol.112 (38), p.14848-14852

2008

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Modeling of structure and porosity in amorphous silicon systems using Monte Carlo methods

The Journal of chemical physics, 2007-06, Vol.126 (21), p.214705-214705

2007

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HRMC_1.1: Hybrid Reverse Monte Carlo method with silicon and carbon potentials

Computer physics communications, 2011-02, Vol.182 (2), p.542-542

2011

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A theoretical study of size and temperature dependent morphology transformations in gold nanoparticles

Chemical physics letters, 2009-05, Vol.474 (1), p.115-118

2009

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The structure of disordered carbon solids studied using a hybrid reverse Monte Carlo algorithm

Journal of physics. Condensed matter, 2005-05, Vol.17 (17), p.2605-2616

2005

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Vacancy induced formation of nanoporous silicon, carbon and silicon carbideElectronic supplementary information (ESI) available: Mean third peak heights and mean porosity changes for each material at each density or strain, along with their standard deviations. All bond length and bond angle distribution data for the three SiC polytypes. See DOI: 10.1039/c8cp06649c

2019-03

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HRMC_2.0: Hybrid Reverse Monte Carlo method with silicon, carbon and germanium potentials

Computer physics communications, 2013-08, Vol.184 (8), p.1946-1957

2013

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Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learningElectronic supplementary information (ESI) available: Further similarity maps encoded with structural features; testing of t-SNE hyper-parameters and protocols; hyper-parameters of regressors. See DOI: 10.1039/C9NR03940F

Nanoscale, 2019-12, Vol.11 (48), p.23165-23172

2019

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Constructing ab initio models of ultra-thin Al-AlOx-Al barriers

Molecular simulation, 2016-05, Vol.42 (6-7), p.542-548

2016

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Investigation of bonding within ab initio models of GeAsSe glasses

Chemical physics letters, 2013-06, Vol.575, p.97-100

2013

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Constructing ab initio models of ultra-thin Al-AlO sub(x)-Al barriers

Molecular simulation, 2016-01, Vol.42 (6-7), p.542-548

2016

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Crystallization in suspensions of hard spheres: a Monte Carlo and molecular dynamics simulation study: Colloidal suspensions

Journal of physics. Condensed matter, 2011, Vol.23 (19)

2011

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Microstructure of an industrial char by diffraction techniques and Reverse Monte Carlo modelling

Carbon (New York), 2004, Vol.42 (12), p.2457-2469

2004

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Structural analysis of carbonaceous solids using an adapted reverse Monte Carlo algorithm

Carbon (New York), 2003, Vol.41 (12), p.2403-2411

2003

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HRMC: Hybrid Reverse Monte Carlo method with silicon and carbon potentials

Computer physics communications, 2008-05, Vol.178 (10), p.777-787

2008

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Constructing ab initio models of ultra-thin Al–AlO x –Al barriers

Molecular simulation, 2016-05, Vol.42 (6-7), p.542-548

2016

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Classification of platinum nanoparticle catalysts using machine learning

Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning

Precursor-mediated crystallization process in suspensions of hard spheres

Nanoinformatics, and the big challenges for the science of small things

Simulating the fabrication of aluminium oxide tunnel junctions

Vacancy induced formation of nanoporous silicon, carbon and silicon carbide

Stability and Transformations of Heated Gold Nanorods

HRMC_2.1: Hybrid Reverse Monte Carlo method with silicon, carbon, germanium and silicon carbide potentials

Hybrid Reverse Monte Carlo and electron phase contrast image simulations of amorphous silicon with and without paracrystals

Ideality versus Reality: Emergence of the Chui Icosahedron

Modeling of structure and porosity in amorphous silicon systems using Monte Carlo methods

HRMC_1.1: Hybrid Reverse Monte Carlo method with silicon and carbon potentials

A theoretical study of size and temperature dependent morphology transformations in gold nanoparticles

The structure of disordered carbon solids studied using a hybrid reverse Monte Carlo algorithm

HRMC_2.0: Hybrid Reverse Monte Carlo method with silicon, carbon and germanium potentials

Constructing ab initio models of ultra-thin Al-AlOx-Al barriers

Investigation of bonding within ab initio models of GeAsSe glasses

Constructing ab initio models of ultra-thin Al-AlO sub(x)-Al barriers

Crystallization in suspensions of hard spheres: a Monte Carlo and molecular dynamics simulation study: Colloidal suspensions

Microstructure of an industrial char by diffraction techniques and Reverse Monte Carlo modelling

Structural analysis of carbonaceous solids using an adapted reverse Monte Carlo algorithm

HRMC: Hybrid Reverse Monte Carlo method with silicon and carbon potentials

Constructing ab initio models of ultra-thin Al–AlO x –Al barriers

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