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George Opletal
George Opletal
Data61 - CSIRO
Verified email at data61.csiro.au
Title
Cited by
Cited by
Year
Precursor-mediated crystallization in suspensions of hard spheres
T Schilling, H Schope, M Oettel, G Opletal, I Snook
Physical Review Letters 105 (2), 1-4, 2010
221*2010
Study of the Initial Stage of Solid Electrolyte Interphase Formation upon Chemical Reaction of Lithium Metal and N-Methyl-N-Propyl-Pyrrolidinium-Bis …
A Budi, A Basile, G Opletal, AF Hollenkamp, AS Best, RJ Rees, AI Bhatt, ...
The Journal of Physical Chemistry C 116 (37), 19789-19797, 2012
2092012
Hybrid approach for generating realistic amorphous carbon structure using metropolis and reverse Monte Carlo
G Opletal, T Petersen, B O'Malley, I Snook, DG McCulloch, NA Marks, ...
Molecular Simulation 28 (10-11), 927-938, 2002
1552002
Structural analysis of carbonaceous solids using an adapted reverse Monte Carlo algorithm
T Petersen, I Yarovsky, I Snook, DG McCulloch, G Opletal
Carbon 41 (12), 2403-2411, 2003
782003
Microstructure of an industrial char by diffraction techniques and Reverse Monte Carlo modelling
T Petersen, I Yarovsky, I Snook, DG McCulloch, G Opletal
Carbon 42 (12-13), 2457-2469, 2004
772004
Nanoinformatics, and the big challenges for the science of small things
AS Barnard, B Motevalli, AJ Parker, JM Fischer, CA Feigl, G Opletal
Nanoscale 11 (41), 19190-19201, 2019
712019
Structural modelling of silicon carbide-derived nanoporous carbon by hybrid reverse Monte Carlo simulation
AH Farmahini, G Opletal, SK Bhatia
The Journal of Physical Chemistry C 117 (27), 14081-14094, 2013
652013
The structure of disordered carbon solids studied using a hybrid reverse Monte Carlo algorithm
G Opletal, TC Petersen, DG McCulloch, IK Snook, I Yarovsky
Journal of Physics: Condensed Matter 17 (17), 2605, 2005
582005
Crystallization in suspensions of hard spheres: a Monte Carlo and molecular dynamics simulation study
T Schilling, S Dorosz, HJ Schöpe, G Opletal
Journal of Physics: Condensed Matter 23 (19), 194120, 2011
462011
HRMC: Hybrid Reverse Monte Carlo method with silicon and carbon potentials
G Opletal, TC Petersen, B O'Malley, IK Snook, DG McCulloch, I Yarovsky
Computer Physics Communications 178 (10), 777-787, 2008
432008
Classification of platinum nanoparticle catalysts using machine learning
AJ Parker, G Opletal, AS Barnard
Journal of Applied Physics 128 (1), 2020
312020
Stability and transformations of heated gold nanorods
G Opletal, G Grochola, YH Chui, IK Snook, SP Russo
The Journal of Physical Chemistry C 115 (11), 4375-4380, 2011
312011
Dynamic self-assembly of detonation nanodiamond in water
SLY Chang, P Reineck, D Williams, G Bryant, G Opletal, SA El-Demrdash, ...
Nanoscale 12 (9), 5363-5367, 2020
302020
HRMC_2. 0: Hybrid Reverse Monte Carlo method with silicon, carbon and germanium potentials
G Opletal, TC Petersen, IK Snook, SP Russo
Computer Physics Communications 184 (8), 1946-1957, 2013
302013
Chiminey: Reliable computing and data management platform in the cloud
II Yusuf, IE Thomas, M Spichkova, S Androulakis, GR Meyer, DW Drumm, ...
2015 IEEE/ACM 37th IEEE International Conference on Software Engineering 2 …, 2015
282015
Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning
AS Barnard, G Opletal
Nanoscale 11 (48), 23165-23172, 2019
272019
Modeling of structure and porosity in amorphous silicon systems using Monte Carlo methods
G Opletal, TC Petersen, IK Snook, DG McCulloch
The Journal of chemical physics 126 (21), 2007
262007
Simulating the fabrication of aluminium oxide tunnel junctions
MJ Cyster, JS Smith, N Vogt, G Opletal, SP Russo, JH Cole
npj quantum information 7 (1), 12, 2021
242021
Dynamic evolution of specific catalytic sites on Pt nanoparticles
H Barron, G Opletal, RD Tilley, AS Barnard
Catalysis Science & Technology 6 (1), 144-151, 2016
242016
Selecting machine learning models for metallic nanoparticles
AS Barnard, G Opletal
Nano Futures 4 (3), 035003, 2020
232020
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