activity
20242026
most citedExtending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials

9 citations · 12 across the 7 of their papers we have counts for

collaborators

7 papers

quant-ph2026

Quantum Mechanical Studies of Photodissociation Dynamics on Quantum Computers

Zikun Zhuang, Chengdong Yang, Yuchen Wang +2

Theoretical quantum dynamics calculations scale deeply with system size, rendering classical calculations intractable for complex systems. While quantum computing offers a natural…

physics.chem-ph2026

Monomeric machine learning potential for general covalent molecules: linear alkanes as an example

Xinze Li, Ruitao Ma, Chen Qu +2

Machine-learning potentials (MLPs) have become important tools for modern molecular simulations. However, developing models that simultaneously achieve high accuracy and high compu…

physics.chem-ph2026

Rigorous Quantum Thermodynamics from Entropic Path Integral Coarse-Graining

Jing Shen, Ziyan Ye, Ming-Zheng Du +5

Nuclear quantum effects (NQEs) remain a major challenge for molecular simulations, as rigorous treatment requires imaginary-time path-integral methods with heavy computational over…

physics.chem-ph2025

On the role of nuclear quantum effects on the stability of peptides

Jing Shen, Ming-Zheng Du, Dong H. Zhang +2

Nuclear quantum effects (NQEs) arising from the light mass of hydrogen can influence the structure and stability of hydrogen-bonded biomolecules, yet their role in determining pept…

physics.chem-ph2025★ 3 cited

Interaction-Region Decoupling through Structured Absorbing Potentials: A Framework for Scalable Time-Dependent Quantum Dynamics Calculations

Yuegu Fang, Jiayu Huang, Dong H. Zhang

Accurate quantum mechanical treatment of molecular reactions remains a longstanding challenge, especially for reactions involving deep potential wells and long-lived intermediate c…

physics.chem-ph2024★ 9 cited

Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials

Qi Yu, Ruitao Ma, Chen Qu +6

Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN)…

physics.chem-ph2024

Quantum mechanical deconstruction of vibrational energy transfer rate and pathways modified by collective vibrational strong coupling

Qi Yu, Dong H. Zhang, Joel M. Bowman

Recent experiments have demonstrated that vibrational strong coupling (VSC) between molecular vibrations and the optical cavity field can modify vibrational energy transfer (VET) p…