The ONETEP linear-scaling density functional theory program JCA Prentice, J Aarons, JC Womack, AEA Allen, L Andrinopoulos, ... The Journal of chemical physics 152 (17), 2020 | 128 | 2020 |
QUBEKit: Automating the derivation of force field parameters from quantum mechanics JT Horton, AEA Allen, LS Dodda, DJ Cole Journal of chemical information and modeling 59 (4), 1366-1381, 2019 | 96 | 2019 |
Linear atomic cluster expansion force fields for organic molecules: beyond rmse DP Kovács, C Oord, J Kucera, AEA Allen, DJ Cole, C Ortner, G Csányi Journal of chemical theory and computation 17 (12), 7696-7711, 2021 | 93 | 2021 |
Harmonic force constants for molecular mechanics force fields via Hessian matrix projection AEA Allen, MC Payne, DJ Cole Journal of chemical theory and computation 14 (1), 274-281, 2018 | 72 | 2018 |
Machine learning of material properties: Predictive and interpretable multilinear models AEA Allen, A Tkatchenko Science advances 8 (18), eabm7185, 2022 | 34 | 2022 |
Atomic permutationally invariant polynomials for fitting molecular force fields AEA Allen, G Dusson, C Ortner, G Csányi Machine Learning: Science and Technology 2 (2), 025017, 2021 | 33 | 2021 |
Development and validation of the quantum mechanical bespoke protein force field AEA Allen, MJ Robertson, MC Payne, DJ Cole ACS omega 4 (11), 14537-14550, 2019 | 28* | 2019 |
Modelling flexible protein–ligand binding in p38α MAP kinase using the QUBE force field JT Horton, AEA Allen, DJ Cole Chemical communications 56 (6), 932-935, 2020 | 8 | 2020 |
Learning together: Towards foundational models for machine learning interatomic potentials with meta-learning AEA Allen, N Lubbers, S Matin, J Smith, R Messerly, S Tretiak, K Barros arXiv preprint arXiv:2307.04012, 2023 | 3 | 2023 |
Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment S Matin, AEA Allen, J Smith, N Lubbers, RB Jadrich, R Messerly, ... Journal of Chemical Theory and Computation, 2024 | 2 | 2024 |
Toward transferable empirical valence bonds: Making classical force fields reactive AEA Allen, G Csányi The Journal of Chemical Physics 160 (12), 2024 | | 2024 |
Tensor Sensitivity and long-range Coulomb interactions improve the accuracy and extensibility of Machine Learning Potentials S Matin, B Han, J Smith, A Allen, N Lubbers, A Habib, N Fedik, X Li, ... Bulletin of the American Physical Society, 2024 | | 2024 |
Molecular dynamics of high pressure tin phases: Empirical and machine learned interatomic potentials MA Cusentino, B Nebgen, KM Barros, JS Smith, JD Shimanek, A Allen, ... AIP Conference Proceedings 2844 (1), 2023 | | 2023 |
Modeling Excited-State Dynamics for Polariton Chemistry with Hierarchically Interacting Particle Neural Network X Li, Y Zhang, S Tretiak, K Barros, N Lubbers, A Allen, B Nebgen, S Matin APS March Meeting Abstracts 2023, T60. 005, 2023 | | 2023 |
Learning Together: Training Interatomic Potentials to Multiple Datasets A Allen APS March Meeting Abstracts 2023, Q53. 004, 2023 | | 2023 |
Molecular Dynamics of High Pressure Tin Phases II: Machine Learned Interatomic Potential Development. MA Cusentino, B Nebgen, KM Barros, JD Shimanek, A Allen, A Thompson, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
Molecular Dynamics of High Pressure Tin Phases I: Strength and deformation evaluations of empirical potentials. J Lane, MA Cusentino, KM Barros, J Shimanek, A Allen, A Thompson Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
Molecular Dynamics of High Pressure Tin Phases II: Machine Learned Interatomic Potential Development M Alice, B Nebgen, K Barros, J Shimanek, A Allen, A Thompson, S Fensin, ... APS Shock Compression of Condensed Matter Meeting Abstracts, W03. 002, 2022 | | 2022 |
Quantum Mechanically Derived Biomolecular Force Fields A Allen | | 2019 |