6 papers
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…
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…
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…
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…
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)…
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…