Zhiwen Zhong @ KCL

Zhiwen Zhong

Zhiwen Zhong, PhD ↗

Computational Scientist and Postdoctoral Research Associate
Martin-Martinez Lab, King's College London
Working with Dr Francisco J. Martin-Martinez
Department of Chemistry
King's Institute for Artificial Intelligence
Atomistic Simulation | Scientific Software | AI for Science

Introduction

Computational scientist working at the intersection of atomistic simulation, scientific software, and machine learning for materials discovery.

I develop automated computational workflows that combine molecular modelling, molecular dynamics, electronic-structure calculations, descriptor extraction, and machine-learning methods. My current work focuses on polymer and biomaterial systems, with the broader goal of building scalable AI4Science platforms for molecular and materials property prediction.

Biography

I am a Postdoctoral Research Associate at King’s College London, working with Dr Francisco J. Martin-Martinez on computational methods for polymer and biomaterial discovery.

I completed my PhD in Computational Physics at King’s College London under the supervision of Prof. Christian D. Lorenz and Prof. Martin B. Ulmschneider. My doctoral research used multiscale molecular simulations to investigate membrane-protein structure, dynamics, and disease-associated molecular mechanisms.

Before joining King’s, I received an MSc in Biochemistry and Molecular Biology from the University of Science and Technology of China and a BSc from Anhui University.

Current Focus

My current research centres on developing automated atomistic simulation workflows for polymer and materials discovery.

Key areas include:

My longer-term objective is to integrate DFT, molecular dynamics, and machine-learning interatomic potentials into scalable platforms for computational materials discovery.

iPHASimulator

iPHASimulator is an automated molecular simulation workflow for polyhydroxyalkanoate materials discovery.

The workflow is being developed to support:

The project is evolving from a simulation pipeline into a broader materials-discovery platform that can connect atomistic simulations with DFT calculations, machine-learning models, and active-learning modules.

Research Direction

My work is organised around three complementary capabilities:

Research credibility

Publishing rigorous studies that demonstrate scientific understanding of molecular mechanisms and material behaviour.

Engineering capability

Building reusable, documented, and reproducible computational workflows rather than relying on isolated analysis scripts.

AI4Science transition

Extending simulation workflows with DFT-generated data, high-throughput descriptor extraction, machine-learning interatomic potentials, and active learning.

Publications

Mechanistic Disruption of the TREM2–DAP12 Transmembrane Complex by Alzheimer’s Disease Mutations: A Multiscale Simulation Study

Zhiwen Zhong, Martin B. Ulmschneider, and Christian D. Lorenz.
Journal of Chemical Information and Modeling, 2025.
DOI: 10.1021/acs.jcim.5c01891

TREM2-DAP12 mutation study

Unravelling the Molecular Dance: Insights into TREM2/DAP12 Complex Formation in Alzheimer’s Disease through Molecular Dynamics

Zhiwen Zhong, Martin B. Ulmschneider, and Christian D. Lorenz.
ACS Omega, 2024.
DOI: 10.1021/acsomega.4c03060

TREM2-DAP12 complex formation study

Crystal Structure of the PDZ4 Domain of MAGI2 in Complex with PBM of ARMS Reveals a Canonical PDZ Recognition Mode

Yanshen Zhang, Zhiwen Zhong, Jin Ye, and Chao Wang.
*Neurochemistry International, 2021.
Article

MAGI2 PDZ4 and ARMS PBM structure

The Protective Effects of Selenium-Enriched Spirulina platensis on Chronic Alcohol-Induced Liver Injury in Mice

Xiang Fu, Zhiwen Zhong, Feng Hu, Yi Zhang, Chunxia Li, Peng Yan, Lixue Feng, Jinglian Shen, and Bei Huang.
*Food & Function, 2018.
DOI: 10.1039/C8FO00477C

Selenium-enriched Spirulina study

* Co-first author.

Technical Skills

Programming and scientific computing

Python, Bash, Jupyter, Git, Linux, HPC, and SLURM

Molecular and materials simulation

GROMACS, OpenMM, MDAnalysis, AmberTools, GAFF2, CHARMM36m, CGenFF, and AutoDock Vina

Data analysis and machine learning

NumPy, Pandas, Matplotlib, scikit-learn, dimensionality reduction, clustering, and molecular descriptor analysis

Structural modelling and visualisation

PyMOL, VMD, molecular docking, and structural bioinformatics

Experimental background

X-ray crystallography, protein purification, cell culture, confocal microscopy, and hydrogen–deuterium exchange mass spectrometry

Selected Research Experience

2026–Present — Automated simulation workflows for polymer and biomaterial discovery

Developing a reusable Python workflow for polymer construction, force-field parameterisation, molecular simulation, high-throughput analysis, and enzyme–polymer modelling.

2021–2025 — Multiscale modelling of the TREM2–DAP12 transmembrane complex

Used coarse-grained and atomistic molecular dynamics, structural analysis, clustering, and dimensionality-reduction methods to investigate complex formation and the effects of disease-associated mutations.

2020–2021 — Structural characterisation of the MAGI2-PDZ4/ARMS-PBM complex

Contributed to protein purification, crystallisation, structural analysis, and interpretation of PDZ-domain recognition.

2017–2018 — Selenium-enriched Spirulina and liver injury

Studied the protective effects of selenium-enriched Spirulina platensis in a mouse model of chronic alcohol-induced liver injury.

Selected Awards

Professional Interests

I am interested in research and engineering roles involving:

Last updated by Zhiwen Zhong in July 2026