| 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 | 71 | 2019 |
| Selecting Appropriate Clustering Methods for Materials Science Applications of Machine Learning AJ Parker, AS Barnard Advanced Theory and Simulations 2 (12), 1970040, 2019 | 42 | 2019 |
| 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 | 37 | 2011 |
| Classification of platinum nanoparticle catalysts using machine learning AJ Parker, G Opletal, AS Barnard Journal of Applied Physics 128 (1), 2020 | 31 | 2020 |
| The representative structure of graphene oxide nanoflakes from machine learning B Motevalli, AJ Parker, B Sun, AS Barnard Nano Futures 3 (4), 045001, 2019 | 30 | 2019 |
| Molecular mechanisms of plastic deformation in sphere-forming thermoplastic elastomers AJ Parker, J Rottler Macromolecules 48 (22), 8253-8261, 2015 | 26 | 2015 |
| 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 | 18 | 2019 |
| 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 | 18 | 2012 |
| Nonlinear Mechanics of Triblock Copolymer Elastomers: From Molecular Simulations to Network Models AJ Parker, J Rottler ACS Macro Letters 6 (8), 786-790, 2017 | 17 | 2017 |
| Machine learning reveals multiple classes of diamond nanoparticles AJ Parker, AS Barnard Nanoscale Horizons 5 (10), 1394-1399, 2020 | 16 | 2020 |
| Using soft potentials for the simulation of block copolymer morphologies AJ Parker, J Rottler Macromolecular Theory and Simulations 23 (6), 401-409, 2014 | 15 | 2014 |
| 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 | 14 | 2020 |
| 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 | 12 | 2015 |
| Entropic Network Model for Star Block Copolymer Thermoplastic Elastomers AJ Parker, J Rottler Macromolecules 51 (23), 10021-10027, 2018 | 10 | 2018 |
| 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 | 8 | 2020 |
| 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 | 6 | 2021 |
| Interfacial Informatics JM Fischer, AJ Parker, AS Barnard Journal of Physics: Materials, 2021 | 2 | 2021 |
| Data-Driven Design of Classes of Ruthenium Nanoparticles Using Multitarget Bayesian Inference JYC Ting, AJ Parker, AS Barnard Chemistry of Materials 35 (2), 728-738, 2023 | 1 | 2023 |
| Online Meta-learned Gradient Norms for Active Learning in Science and Technology H Dong, AS Barnard, AJ Parker Machine Learning: Science and Technology, 2024 | | 2024 |
| Superior Prediction of Graphene Nanoflake Properties with Unbiased Graph Embedding N Hu, A Parker | | 2023 |
