Latest News
Participation in the GP-MLFF Summer School in Luxembourg
Many thanks to the organizers for the invitation and for bringing together researchers who are pushing general-purpose MLFFs to a new level. These advances are bringing us closer to understanding how large-scale quantum-mechanical datasets can contribute to the development of more transferable and accurate MLFFs. The event also highlighted that, despite the remarkable progress in this field, many open challenges remain. Some of the most exciting directions include:- Biomolecular simulations at realistic ionic concentrations with explicit solvation
- Complex structural transitions, such as those occurring in RNA systems
- Accurate prediction of electronic properties using GP-MLFFs
- More efficient strategies for generating high-quality QM datasets
- Modeling of organic–inorganic interfaces
AI in Science Working Groups (AISWGs)
Delighted to have been selected as a member of the AI in Science Working Groups (AISWGs) within the SCIANCE EU Project. SCIANCE was created to coordinate AI-enabled scientific research in Europe, ensuring that priorities and infrastructure upgrades reflect the needs of the scientific community. The working groups bring together leading AI experts from across Europe. I will be contributing to the “Industry, Innovation & Frugal AI” working group, drawing on my experience in developing interdisciplinary scientific projects using AI.
Member of the QUANTUM 100 - UNESCO IYQ 2025
I would like to thank the committee of the International Year of Quantum Science and Technology for selecting me as one of the Quantum 100. This is a great honor that recognizes not only my dedication to scientific work over the past ten years, but also my commitment to mentoring the next generation of quantum scientists and to promoting the exchange of knowledge between academia and society. More information can be found at this link.Press release from TU Dresden.
News from the Chair of Materials Science and Nanotechnology.
©Sven Ellger/TUD
Recent Publication
Towards the design of artificial sensing materials via quantum-informed explainable AI
L. Chen, L. Medrano Sandonas*, S. Huang, A. Croy, G. Cuniberti.J. Cheminform., 18, 75, (2026)
