Our team comprises specialists in theoretical condensed matter physics, computational chemistry, software architecture, and AI for science.
Researchers and computational scientists leading DDMMS programs at STFC Daresbury Laboratory.
Alin leads the Data Driven Materials and Molecular Science research group. His work centres on multiscale molecular dynamics algorithms, the architecture and scalable parallelisation of DL_POLY 5, physics-informed machine learning, and transport properties in complex liquids and molten salts.
Elliott focuses on machine learning for atomic systems, equivariant foundation architectures (MACE), training set active learning, and automated atomistic simulation workflows. He is the lead designer and developer of janus-core.
Junwen specializes in first-principles quantum chemistry, nonadiabatic electronic transitions, extended CP2K simulations, and modeling complex catalytic and photoactive interfaces under operational environments.
Researchers, engineers, and graduate scholars who contributed to DDMMS scientific software and research initiatives.
Description of research topics, scientific contributions, or software packages developed while with the group.
Description of research topics, scientific contributions, or software packages developed while with the group.
Academic collaborators, national laboratory partners, and international consortia advancing atomistic simulation and scientific machine learning.
Description of collaborative research topics, joint projects, grants, or shared software development.
Description of collaborative research topics, joint projects, grants, or shared software development.
Description of collaborative research topics, joint projects, grants, or shared software development.
We collaborate closely with major national and international research networks to establish shared atomistic data standards and sustainable scientific infrastructure:
Academic visitors, guest researchers, and sabbatical fellows who have visited the group at Sci-Tech Daresbury to collaborate on atomistic simulations and machine learning.
Description of collaborative research topics, joint projects, visit goals, or simulation topics explored during the visit.
Description of collaborative research topics, joint projects, visit goals, or simulation topics explored during the visit.
We are always enthusiastic to collaborate with motivated graduate students, postdoctoral researchers, and academic visitors who wish to explore machine-learned interatomic potentials, extreme-scale molecular dynamics, or materials for sustainable energy technologies.