Catalogue of Possibilities / Supervisors

Dr Guanchen Li he/him

Multiscale engineering of materials and devices using physics-based models and machine learning

Lecturer

James Watt School of Engineering

Research interests:

Irreversible thermodynamics, Multiphysics simulation, Electrochemical devices, Interface, Optimisation, Machine learning, Quantum thermodynamics, ab initio simulations

Research fields:

Energy conversion and storage, Digital twins, Chemomechanical failure, Battery management in intelligent devices, Micro-battery, Point-of-care health, Wind turbine, Sonochemistry

Mission Priority Areas

Why do you want to join the DiveIn community?

This CDT provides a unique opportunity to do interdisciplinary research. I have several ongoing projects that fit the scope well, such as Batteries for networks, intelligent battery controls, point-of-care diagnosis, battery simulation, wind turbine digital twins, nanoscale heat transfer and ultrasound water treatment.

Personal profile:

I am interested in physics-based modelling and algorithms. My research covers fundamental theories, computational methods and practical applications.

Science:

My group exploits multiphysics/multiscale (atomistic-to-continuum) modelling and data-driven methods to advance the design and manufacture of materials and devices. I aim to connect physics, data science, sensors & controllers, and advanced characterisation in a way that both AI and humans understand.

I am particularly interested in modelling irreversible dissipations across scales — from the quantum to the device level. Applications include electrochemical systems, sonochemical reactors, wind turbines, engine cycles, point-of-care devices and semiconductors.

I seek to leverage my expertise in multiphysics simulation to collaborate closely with experimentalists and AI experts.

CDT projects:

I have a computational physics background with extensive experience in interdisciplinary collaboration. I am keen to supervise collaborative projects that bridge modelling and experiments, or physics and data science. I aim to develop projects that combine theoretical rigour, computational innovation, and experimental validation, fostering AI-assisted discovery and design in physical systems.

Ongoing research projects:

Failure mechanism and design of solid-state batteries (materials + mechanics, experiment + modelling)
Battery network for power grid resilience (battery + communications + AI)
Intelligent design and fabrication of self-powered rapid testing kits for transmitted diseases (BME + battery)
Sonochemical reactors for wastewater treatment (chemistry + environment engineering)
Digital twins of offshore wind turbines for failure detection (renewable energy + AI)
Fast battery parameterisation from multiphysics sensors (physics + AI)
Nanoscale heat transfer simulation based on ab initio simulations and quantum thermodynamics (Physics + quantum technology)

EDI:

My group always support EDI and tries our best to provide opportunities to students in difficult circumstances. I have supported many students’ PhD/scholarship applications from different backgrounds, including those from developing countries, underrepresented races, and countries in war.

https://scholar.google.co.uk/citations?user=7EYZQUEAAAAJ&hl=en&oi=ao

https://www.gla.ac.uk/schools/engineering/staff/guanchenli/