Follow
Tsz Wai Ko
Title
Cited by
Cited by
Year
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
3482021
Neural network potentials: A concise overview of methods
E Kocer, TW Ko, J Behler
Annual review of physical chemistry 73 (1), 163-186, 2022
1452022
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
932021
Recent advances and outstanding challenges for machine learning interatomic potentials
TW Ko, SP Ong
Nature Computational Science 3 (12), 998-1000, 2023
202023
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
202023
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
72019
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
52024
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
The system can't perform the operation now. Try again later.
Articles 1–13