8 papers
A Unified Generative Framework for Scalable Chemical Reaction Network Exploration
Zechang Sun, Chenxi Hu, Kailai Lin +6
Chemical reaction networks (CRNs) are crucial for understanding reaction mechanisms and guiding chemical synthesis, yet the computational exploration remains limited by the combina…
Quantum Many-Body Simulations of Catalytic Metal Surfaces
Changsu Cao, Hung Q. Pham, Zhen Guo +5
Quantum simulations of metal surfaces are critical for catalytic innovation. Yet existing methods face a cost-accuracy dilemma: density functional theory is efficient but system-de…
Hessian-informed machine learning interatomic potential towards bridging theory and experiments
Bangchen Yin, Jian Ouyang, Zhen Fan +7
Local curvature of potential energy surfaces is critical for predicting certain experimental observables of molecules and materials from first principles, yet it remains far beyond…
Digital adiabatic evolution is universally accurate
Yangyu Lu, Yifei Huang, Dong An +3
Adiabatic evolution is a central paradigm in quantum physics. Digital simulations of adiabatic processes are generally viewed as costly, since algorithmic errors typically accumula…
Solving the Hubbard model with Neural Quantum States
Yuntian Gu, Wenrui Li, Heng Lin +9
The rapid development of neural quantum states (NQS) has established it as a promising framework for studying quantum many-body systems. In this work, by leveraging the cutting-edg…
ByteQC: GPU-Accelerated Quantum Chemistry Package for Large-Scale Systems
Zhen Guo, Zigeng Huang, Qiaorui Chen +7
Applying quantum chemistry algorithms to large-scale systems requires substantial computational resources scaled with the system size and the desired accuracy. To address this, Byt…