香港大学化学系理论化学讲席教授陈冠华课题组现招收2至3名博士研究生。录取学生将参与香港大学—加州理工学院联合研究项目，研究方向为基于多尺度建模与机器学习的下一代高性能固态电解质设计。该项目结合物理驱动的建模方法、先进的机器学习算法以及实验数据，深入探究锂离子在聚合物基复合电解质中的传输机制，并探索优化策略，致力于开发具备高导电性与稳定性的新型材料，为下一代锂离子电池提供可靠的解决方案。

课题组配备丰富的科研资源，拥有30余张高性能GPU（如A100、A800等）及近30个高性能CPU计算节点，可充分满足博士生在多尺度建模与机器学习等方向上的计算需求，积极支持学生开展创新性科研工作。所有录取博士生均可获得奖学金资助，目前资助金额为每月18,760港币。诚邀在相关领域具有良好学术背景、并对材料模拟与机器学习研究充满热情的优秀学生加入本课题组，共同开展前沿科学探索。

开发和应用多尺度建模方法，研究锂离子在聚合物基复合电解质中的溶剂化和传输机制。构建基于物理机制的代理函数以快速预测离子传输性能，并结合机器学习优化固态电解质的设计。

● 使用分子动力学模拟（MD）和量子化学计算（QC）研究锂离子在聚合物基电解质中的溶剂化结构及动力学行为；

● 构建粗粒化模型及基于物理机制的代理函数，加速离子传输性能的预测；

● 开发机器学习模型，提取潜在特征并优化电解质材料；

● 与高通量实验生成的数据结合，验证模拟结果并指导实验设计。

招生单位：香港大学化学系

申请条件：需满足香港大学博士研究生入学要求（如雅思成绩、GPA等）。

申请材料：个人简历、成绩单、研究计划、推荐信（2封及以上）。

截止日期：欢迎尽早申请，招生名额有限，录满为止。

有意申请者请将申请材料发送至胡老师邮箱ziyang1@hku.hk，邮件标题请注明“PhD Application of [SURNAME], [Given Name]”，如“PhD Application of SHEN, Qing”。

Professor GuanHua Chen, Chair Professor of Theoretical Chemistry in the Department of Chemistry at The University of Hong Kong (HKU), is currently seeking to recruit 2 to 3 PhD students. Successful candidates will participate in a joint research project between HKU and the California Institute of Technology (Caltech). The project focuses on the design of next-generation high-performance solid-state electrolytes, using a combination of multi-scale modelling and machine learning. By integrating physics-driven modelling, advanced machine learning algorithms, and experimental data, the project aims to uncover the ion transport mechanisms of lithium ions in polymer-based composite electrolytes and to develop optimisation strategies for new materials with high ionic conductivity and stability, ultimately contributing to the advancement of next-generation lithium-ion batteries.

The group is equipped with extensive computational resources, including over 30 high-performance GPU cards (such as A100 and A800) and nearly 30 high-performance CPU nodes. These resources fully support the computational needs of research in multi-scale modelling and machine learning, fostering an environment conducive to innovative doctoral research. All admitted PhD students will receive full scholarship support, currently set at HKD 18,760 per month. Talented and motivated candidates with relevant academic backgrounds and a strong interest in materials simulation and machine learning are warmly encouraged to apply.

To develop and apply multi-scale modelling approaches to investigate the solvation and transport mechanisms of lithium ions in polymer-based composite electrolytes. The project further aims to construct physics-informed surrogate models for rapid prediction of ion transport performance and to incorporate machine learning methods for the design and optimisation of solid-state electrolytes.

● Employ molecular dynamics (MD) simulations and quantum chemistry (QC) calculations to study solvation structures and dynamical behaviours of lithium ions in polymer electrolytes;

● Develop coarse-grained models and physics-based surrogate functions to accelerate the prediction of ionic transport properties;

● Construct and train machine learning models to identify key material features and optimise electrolyte composition;

● Integrate high-throughput experimental data to validate simulation results and guide experimental design.

Background: Applicants should hold a Bachelor’s or Master’s degree in Chemistry, Materials Science, Physics, Computational Chemistry, Computational Materials Science, or a related field.

Skills:

● Prior knowledge in polymer chemistry/physics is preferred;

● Familiarity with molecular dynamics software (e.g., LAMMPS, GROMACS) or quantum chemistry packages (e.g., Gaussian, VASP);

● Proficiency in at least one programming language (e.g., Python, C++, or Fortran);

● Experience in machine learning model development (e.g., JAX, PyTorch) is a plus.

Research Competence:

A strong interest in solid-state electrolyte research; ability to conduct independent research; collaborative mindset; and solid command of written and spoken English.

Host Department: Department of Chemistry, The University of Hong Kong

Entry Requirements: Applicants must meet the PhD admission criteria of HKU, including English language proficiency (e.g., IELTS) and academic performance (e.g., GPA).

Application Materials: CV, academic transcripts, research proposal, and at least two letters of recommendation.

Deadline: Applications are reviewed on a rolling basis. Early submission is strongly encouraged as places are limited and offers will be made until the positions are filled.

Interested applicants should send their application materials to Dr Hu: ziyang1@hku.hk.

Email subject: “PhD Application of [SURNAME], [Given Name]”, e.g., “PhD Application of SMITH, John”.