| A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer TW Ko, JA Finkler, S Goedecker, J Behler Nature communications 12 (1), 398, 2021 | 422 | 2021 |
| Neural network potentials: A concise overview of methods E Kocer, TW Ko, J Behler Annual review of physical chemistry 73 (1), 163-186, 2022 | 205 | 2022 |
| General-purpose machine learning potentials capturing nonlocal charge transfer TW Ko, JA Finkler, S Goedecker, J Behler Accounts of Chemical Research 54 (4), 808-817, 2021 | 107 | 2021 |
| Recent advances and outstanding challenges for machine learning interatomic potentials TW Ko, SP Ong Nature Computational Science 3 (12), 998-1000, 2023 | 41 | 2023 |
| Accurate fourth-generation machine learning potentials by electrostatic embedding TW Ko, JA Finkler, S Goedecker, J Behler Journal of Chemical Theory and Computation 19 (12), 3567-3579, 2023 | 32 | 2023 |
| Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling J Qi, TW Ko, BC Wood, TA Pham, SP Ong npj Computational Materials 10 (1), 43, 2024 | 17 | 2024 |
| Exploring the compositional ternary diagram of Ge/S/Cu glasses for resistance switching memories N Onofrio, TW Ko The Journal of Physical Chemistry C 123 (14), 9486-9495, 2019 | 7 | 2019 |
| Crash testing machine learning force fields for molecules, materials, and interfaces: Model analysis in the tea challenge 2023 I Poltavsky, A Charkin-Gorbulin, M Puleva, GC Fonseca, I Batatia, ... | 6 | 2024 |
| Roadmap for the development of machine learning-based interatomic potentials YW Zhang, V Sorkin, ZH Aitken, A Politano, J Behler, A Thompson, TW Ko, ... Modelling and Simulation in Materials Science and Engineering, 2024 | | 2024 |
| Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials TW Ko, SP Ong arXiv preprint arXiv:2409.00957, 2024 | | 2024 |
| Superionic surface Li-ion transport in carbonaceous materials J Zhou, S Wang, C Wu, J Qi, H Wan, S Lai, S Feng, TW Ko, Z Liang, ... arXiv preprint arXiv:2405.16835, 2024 | | 2024 |
| Multi-fidelity Approach to Data Efficient Construction of Graph Neural Network Interatomic Potentials TW Ko, SP Ong Bulletin of the American Physical Society, 2024 | | 2024 |
| (Invited) Machine Learning for Solid-State Batteries – Progress Versus Hype SP Ong, J Qi, C Chen, MLH Chandrappa, TW Ko Electrochemical Society Meeting Abstracts 243, 1036-1036, 2023 | | 2023 |
| Development of a Generally Applicable Machine Learning Potential with Accurate Long-Range Electrostatic Interactions TW Ko | | 2022 |
| Investigation of global charge distributions for constructing non-local machine learning potentials TW Ko, J Finkler, SA Goedecker, J Behler APS March Meeting Abstracts 2021, P22. 010, 2021 | | 2021 |
| Atomic view of chalcogenide-based resistance switching memories TW Ko Hong Kong Polytechnic University, 2018 | | 2018 |
| Section 6–Graph Deep Learning Potentials for Atomistic Simulations TW Ko, SP Ong Roadmap for the development of machine learning-based interatomic potentials, 0 | | |
Jörg BehlerRuhr-Universität BochumVerified email at ruhr-uni-bochum.de
