Yaoguang Zhai

University of California, San Diego — United States

Materials Science · Physical Sciences

6h-index198citations21works8.12yr avg
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Research Topics

Machine Learning in Materials Science(13), Quantum, superfluid, helium dynamics(8), Protein Structure and Dynamics(7), Spectroscopy and Quantum Chemical Studies(6), Quantum many-body systems(4)

Publications21 total

Constraints on the location of the liquid–liquid critical point in water
Nature Physics·2025· 27 cited
Pinpointing the Location of the Elusive Liquid-Liquid Critical Point in Water
2024· 1 citedOpen Access
Pinpointing the Location of the Elusive Liquid-Liquid Critical Point in Water
2024· 4 citedOpen Access
Many-body interactions and deep neural network potentials for water
The Journal of Chemical Physics·2024· 20 citedOpen Access
Sample-and-Bound for Non-convex Optimization
Proceedings of the AAAI Conference on Artificial Intelligence·2024· 2 citedOpen Access
Many-body interactions and deep neural network potentials for water
2024· 3 citedOpen Access
Sample-and-Bound for Non-Convex Optimization
arXiv (Cornell University)·2024Open Access
A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?
The Journal of Chemical Physics·2023· 75 cited
A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?
2023Open Access
A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?
2023Open Access
A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?
2023· 2 citedOpen Access
A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing properties or learning the physics of many-body interactions?
2022Open Access
Monte Carlo Tree Descent for Black-Box Optimization
arXiv (Cornell University)·2022· 1 citedOpen Access
A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing properties or learning the underlying physics?
2022· 2 citedOpen Access
A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing properties or learning the underlying physics?
2022· 2 citedOpen Access
Active learning of many-body configuration space: Application to the Cs+–water MB-nrg potential energy function as a case study
The Journal of Chemical Physics·2020· 26 cited
Active Learning of Many-Body Configuration Space: Application to the Cs+–water MB-nrg Potential Energy Function as a Case Study
2020· 13 citedOpen Access
Active Learning of Many-Body Configuration Space: Application to the Cs+–water MB-nrg Potential Energy Function as a Case Study
2020· 5 citedOpen Access
Aeroelastic Stability Assessment of an Industrial Compressor Blade Including Mistuning Effects
Journal of Turbomachinery·2012· 11 cited
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Frequent Co-authors

Francesco Paesani(16), Sigbjørn Løland Bore(11), Alessandro Caruso(10), Zhishang Luo(6), Sicun Gao(6), Francesco Sciortino(3), Etienne Palos(2), Ronnie Bladh(2), Richa Rashmi(1), Alessandro Caruso(1), Göran Dyverfeldt(1), Go ̈ran Dyverfeldt(1), Rashmi Richa(1), Zhizhen Qin(1), Zhizhen Qin(1)