Cai‐Zhuang Wang

United States Department of Energy — United States

Materials Science · Physical Sciences

58h-index12.4kcitations575works3.22yr avg
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Research Topics

Graphene research and applications(70), Advanced Chemical Physics Studies(61), High-pressure geophysics and materials(53), Machine Learning in Materials Science(53), Surface and Thin Film Phenomena(50)

Publications575 total

Multimode Jahn-Teller Effect in Negatively Charged Nitrogen-Vacancy Center in Diamond
arXiv (Cornell University)·2025Open Access
Substrate-adaptive sacrificial corrosion strategy enables 700 mV oxygen evolution window for enhanced seawater electrolysis
Nature Communications·2025· 1 citedOpen Access
Pressure-Induced B1 to B2 Phase Transition in CeN Studied by ab initio Correlation Matrix Renormalization Theory Calculations
arXiv (Cornell University)·2025Open Access
exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
The Journal of Open Source Software·2025· 1 citedOpen Access
Machine learning guided search for energetically favorable metal borocarbide ternary compounds
Journal of Alloys and Compounds·2025
Ab initio calculation of atomic solid hydrogen phases based on Gutzwiller many-body wave functions
arXiv (Cornell University)·2025Open Access
exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials
arXiv (Cornell University)·2025Open Access
Developing a neural network machine learning interatomic potential for molecular dynamics simulations of La–Si–P systems
The Journal of Chemical Physics·2025· 2 cited
Discovery of metastable P–Si–Sn ternary compounds aided by machine learning
Physica Scripta·2025
Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications
International Journal of Hydrogen Energy·2025· 6 cited
Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing
arXiv (Cornell University)·2025Open Access
exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
arXiv (Cornell University)·2025Open Access
Developing a Neural Network Machine Learning Interatomic Potential for Molecular Dynamics Simulations of La-Si-P Systems
arXiv (Cornell University)·2025Open Access
Intentional Replacement Acceleration Layer Strategy Achieves Superior Water/Seawater Electrolysis at Industrial‐Level Current Densities
Advanced Functional Materials·2025· 3 cited
Quantum-Classical Embedding via Ghost Gutzwiller Approximation for Enhanced Simulations of Correlated Electron Systems
arXiv (Cornell University)·2025Open Access
Substrate-Adaptive Sacrificial Corrosion Strategy Enables 700 mV Oxygen Evolution Window for Chloride-Resistant Seawater Electrolysis
2025Open Access
Search for Stable and Low-Energy Ce–Co–Cu Ternary Compounds Using Machine Learning
Inorganic Chemistry·2025· 1 citedOpen Access
Origins of structural and electronic discrepancies in elemental chalcogen glass
Journal of Non-Crystalline Solids·2025
Unveiling the nature of Ga-based chalcogenides for electrical switching selectors
Journal of Alloys and Compounds·2025· 1 cited
Adaptive variational quantum dynamics simulations with compressed circuits and fewer measurements
Physical review. B./Physical review. B·2025· 4 citedOpen Access
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Frequent Co-authors

Kai‐Ming Ho(33), M. Hupalo(8), Yongxin Yao(7), Zhong-Yi Lu(6), Xiaojie Liu(6), Michael C. Tringides(6), Tzu-Liang Chan(5), Alexandre A. Shvartsburg(4), Martin F. Jarrold(4), Shiwei Lin(4), M. J. Kramer(4), Wen‐Cai Lu(4), Kai-Ming Ho(4), Beilin Ye(3), Tongqi Wen(3), Manh Cuong Nguyen(3), M. C. Tringides(3), Songyou Wang(3), Xin Zhao(3), Yamei Zeng(3)