difficult to access experimentally in many circumstances, making theory and simulations essential.
A first part of the project focuses on quantum statistical effects with Path Integral Molecular Dynamics (PIMD) for atomic level simulations, especially under exotic conditions. In particular, the project builds on a unified “middle” thermostat and barostat scheme developed by the Jian Liu group, which enables significantly more efficient sampling in constant-temperature and constant-pressure ensembles than conventional PIMD algorithms. The project also extends these ideas to systems involving multiple electronic states through the Multi-Electronic-State PIMD (MES-PIMD) framework, allowing nonadiabatic effects to be incorporated into simulations of thermodynamic observables in both adiabatic and diabatic representations.
A second part of the project focuses on nonadiabatic quantum molecular dynamics for electronically nonadiabatic systems. The student will contribute to the further development and application of the recently developed nonadiabatic field (NaF) approach. NaF provides a physically correct picture of nuclear and electronic motion when nonadiabatic coupling plays a role as well as when it fades away, overcoming known limitations of all conventional Ehrenfest-based and surface-hopping methods, especially for systems where the nonadiabatic coupling region is wide and when the temperature is low.
The project offers a unified framework for treating nuclear and electronic degrees of freedom and naturally incorporates nuclear quantum effects based on rigorous coordinate-momentum phase space and path integral formulations of quantum mechanics. Applications will involve representative molecular and condensed-phase systems relevant to chemical reactivity, photochemistry, and energy conversion. Throughout the PhD, the student will gain strong training in statistical mechanics, quantum chemistry, molecular simulation, and scientific programming, and will work at the interface of theory, algorithms, and chemical interpretation. Opportunities to explore connections with electronic structure methods and machine learning techniques will also be available, depending on the student’s interests.
Contact:
Prof. Jian Liu
Email: jianliupku@pku.edu.cn
Website: http://jianliugroup.pku.edu.cn/
Website: https://jianliugroup-pku.github.io/
