Search Results - "Ko, Tsz Wai"

Neural Network Potentials: A Concise Overview of Methods by Kocer, Emir, Ko, Tsz Wai, Behler, Jörg

“…In the past two decades, machine learning potentials (MLPs) have reached a level of maturity that now enables applications to large-scale atomistic simulations…”
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A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer by Ko, Tsz Wai, Finkler, Jonas A., Goedecker, Stefan, Behler, Jörg

“…Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science…”
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General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer by Ko, Tsz Wai, Finkler, Jonas A, Goedecker, Stefan, Behler, Jörg

“…Conspectus The development of first-principles-quality machine learning potentials (MLP) has seen tremendous progress, now enabling computer simulations of…”
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Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling by Qi, Ji, Ko, Tsz Wai, Wood, Brandon C., Pham, Tuan Anh, Ong, Shyue Ping

“…Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond that accessible by ab initio methods and play an…”
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Exploring the Compositional Ternary Diagram of Ge/S/Cu Glasses for Resistance Switching Memories by Onofrio, Nicolas, Ko, Tsz Wai

“…Amorphous semiconductors with tailored ionic and electronic conductivities are central to the operation of emerging resistive memory. However, because of the…”
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Recent advances and outstanding challenges for machine learning interatomic potentials by Ko, Tsz Wai, Ong, Shyue Ping

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Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding by Ko, Tsz Wai, Finkler, Jonas A., Goedecker, Stefan, Behler, Jörg

“…In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in…”
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Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials by Ko, Tsz Wai, Ong, Shyue Ping

“…Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations because of their ability to reproduce ab initio…”
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Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding by Ko, Tsz Wai, Finkler, Jonas A, Goedecker, Stefan, Behler, Jörg

“…J. Chem. Theory Comput., 2023 In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic…”
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Neural Network Potentials: A Concise Overview of Methods by Kocer, Emir, Ko, Tsz Wai, Behler, Jörg

“…In the past two decades, machine learning potentials (MLP) have reached a level of maturity that now enables applications to large-scale atomistic simulations…”
Get full text

Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling by Qi, Ji, Ko, Tsz Wai, Wood, Brandon C, Pham, Tuan Anh, Ong, Shyue Ping

“…Machine learning interatomic potentials (MLIPs) enable the accurate simulation of materials at larger sizes and time scales, and play increasingly important…”
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A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer by Ko, Tsz Wai, Finkler, Jonas A, Goedecker, Stefan, Behler, Jörg

“…Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science…”
Get full text

Superionic surface Li-ion transport in carbonaceous materials by Zhou, Jianbin, Wang, Shen, Wu, Chaoshan, Qi, Ji, Wan, Hongli, Lai, Shen, Feng, Shijie, Ko, Tsz Wai, Liang, Zhaohui, Zhou, Ke, Harpak, Nimrod, Solan, Nick, Liu, Mengchen, Hui, Zeyu, Ai, Paulina J, Griffith, Kent, Wang, Chunsheng, Ong, Shyue Ping, Yao, Yan, Liu, Ping

“…Unlike Li-ion transport in the bulk of carbonaceous materials, little is known about Li-ion diffusion on their surface. In this study, we have discovered an…”
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