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Amanda J. Parker
Amanda J. Parker
Research Fellow, The Australian National University
Verified email at anu.edu.au - Homepage
Title
Cited by
Cited by
Year
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
Selecting Appropriate Clustering Methods for Materials Science Applications of Machine Learning
AJ Parker, AS Barnard
Advanced Theory and Simulations 2 (12), 1970040, 2019
422019
On the bonding of Ga2, structures of Gan clusters and the relation to the bulk structure of gallium
N Gaston, AJ Parker
Chemical Physics Letters 501 (4-6), 375-378, 2011
372011
Classification of platinum nanoparticle catalysts using machine learning
AJ Parker, G Opletal, AS Barnard
Journal of Applied Physics 128 (1), 014301, 2020
312020
The representative structure of graphene oxide nanoflakes from machine learning
B Motevalli, AJ Parker, B Sun, AS Barnard
Nano Futures 3 (4), 045001, 2019
292019
Molecular mechanisms of plastic deformation in sphere-forming thermoplastic elastomers
AJ Parker, J Rottler
Macromolecules 48 (22), 8253-8261, 2015
252015
Classifying and predicting the electron affinity of diamond nanoparticles using machine learning
CA Feigl, B Motevalli, AJ Parker, B Sun, AS Barnard
Nanoscale Horizons 4 (4), 983-990, 2019
182019
Molecular dynamics simulations of star polymeric molecules with diblock arms, a comparative study
WC Swope, AC Carr, AJ Parker, J Sly, RD Miller, JE Rice
Journal of chemical theory and computation 8 (10), 3733-3749, 2012
182012
Machine learning reveals multiple classes of diamond nanoparticles
AJ Parker, AS Barnard
Nanoscale Horizons 5 (10), 1394-1399, 2020
162020
Nonlinear Mechanics of Triblock Copolymer Elastomers: From Molecular Simulations to Network Models
AJ Parker, J Rottler
ACS Macro Letters 6 (8), 786-790, 2017
162017
Using soft potentials for the simulation of block copolymer morphologies
AJ Parker, J Rottler
Macromolecular Theory and Simulations 23 (6), 401-409, 2014
152014
Accurate prediction of binding energies for two‐dimensional catalytic materials using machine learning
J Melisande Fischer, M Hunter, M Hankel, DJ Searles, AJ Parker, ...
ChemCatChem 12 (20), 5109-5120, 2020
142020
Water soluble, biodegradable amphiphilic polymeric nanoparticles and the molecular environment of hydrophobic encapsulates: Consistency between simulation and experiment
RD Miller, RM Yusoff, WC Swope, JE Rice, AC Carr, AJ Parker, J Sly, ...
Polymer 79, 255-261, 2015
122015
Entropic Network Model for Star Block Copolymer Thermoplastic Elastomers
AJ Parker, J Rottler
Macromolecules 51 (23), 10021-10027, 2018
102018
The pure and representative types of disordered platinum nanoparticles from machine learning
AJ Parker, B Motevalli, G Opletal, AS Barnard
Nanotechnology 32 (9), 095404, 2020
82020
Unsupervised structure classes vs. supervised property classes of silicon quantum dots using neural networks
AJ Parker, AS Barnard
Nanoscale Horizons 6 (3), 277-282, 2021
62021
Interfacial Informatics
JM Fischer, AJ Parker, AS Barnard
Journal of Physics: Materials, 2021
22021
Avoiding biases and maximising efficiency with active learning directed simulations of small molecule surface binding
A Parker, AS Barnard
International Conference on Nanostructured Materials (NANO 2020), 41, 2020
2020
Microscopic origins of the mechanical response of nanostructured elastomeric materials
AJ Parker
University of British Columbia, 2017
2017
Microscopic deformation mechanisms in model thermoplastic elastomers by molecular dynamics simulation
A Parker, J Rottler
APS Meeting Abstracts, 2016
2016
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Articles 1–20