联系方式
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”。
PhD Opportunities in Theoretical Chemistry – Prof GuanHua Chen’s Research Group, Department of Chemistry, The University of Hong Kong
Overview
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.
Research Objectives
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.
Research Topics
• 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.
Eligibility and Requirements
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.
Application Information
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.
Contact
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”.
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