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Taehee Ko
Taehee Ko
School of Computational Sciences, Korea Institute for Advanced Study
Correu electrònic verificat a kias.re.kr - Pàgina d'inici
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Using machine learning to go beyond potential energy surface benchmarking for chemical reactivity
X Guan, JP Heindel, T Ko, C Yang, T Head-Gordon
Nature Computational Science 3 (11), 965-974, 2023
122023
Implementation of the density-functional theory on quantum computers with linear scaling with respect to the number of atoms
T Ko, X Li, C Wang
arXiv preprint arXiv:2307.07067, 2023
72023
Stochastic algorithms for self-consistent calculations of electronic structures
T Ko, X Li
Mathematics of Computation 92 (342), 1693-1728, 2023
72023
A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization With Non-isolated Local Minima
T Ko, X Li
Journal of Machine Learning 2 (Issue 2), 138-160, 2023
62023
Random coordinate descent: A simple alternative for optimizing parameterized quantum circuits
Z Ding, T Ko, J Yao, L Lin, X Li
Physical Review Research 6 (3), 033029, 2024
52024
Using diffusion maps to analyze reaction dynamics for a hydrogen combustion benchmark dataset
T Ko, JP Heindel, X Guan, T Head-Gordon, DB Williams-Young, C Yang
Journal of Chemical Theory and Computation 19 (17), 5872-5885, 2023
42023
Ground-State Energy and Related Properties Estimation in Quantum Chemistry with Linear Dependence on the Number of Atoms
T Ko, X Li, C Wang
2024 IEEE International Conference on Quantum Computing and Engineering (QCE …, 2024
2024
Quantum random power method for ground state computation
T Ko, H Park, S Choi
arXiv preprint arXiv:2408.08556, 2024
2024
Beyond potential energy surface benchmarking: a complete application of machine learning to chemical reactivity
X Guan, J Heindel, T Ko, C Yang, T Head-Gordon
arXiv preprint arXiv:2306.08273, 2023
2023
Stochastic Algorithms for Some Problems in Chemistry and Machine Learning
T Ko
The Pennsylvania State University, 2023
2023
En aquests moments el sistema no pot dur a terme l'operació. Torneu-ho a provar més tard.
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