Machine Learning in Quantum-Dot Computational Chemistry: Challenges and Opportunities
Ivan Infante a
a BCMaterials, Basque Center for Materials, Applications, and Nanostructures, UPV/EHU Science Park, Leioa, Spain
Proceedings of MATSUS Spring 2026 Conference (MATSUSSpring26)
H2 Halide perovskites for quantum technologies
Barcelona, Spain, 2026 March 23rd - 27th
Organizers: Quinten Akkerman, Simon Boehme and Maksym Kovalenko
Invited Speaker, Ivan Infante, presentation 705
Publication date: 15th December 2025

Metal halide perovskite quantum dots (PQDs) continue to challenge computational chemistry due to their structural softness and highly dynamic surface chemistry. In our group, we combine high-level DFT with molecular dynamics to generate next-generation machine-learning force fields (MLFFs) capable of capturing these complexities across realistic time and length scales. By curating cross-material datasets and integrating active learning, we train transferable MLFFs that describe ligand binding, surface reconstruction, and defect dynamics with near-DFT accuracy. These models unlock nanosecond molecular dynamics for CsPbBr₃ PQDs, providing statistically meaningful insight into the mechanistic processes that occur at PQD surfaces and their associated energy landscapes. We complement this with interpretable electronic-structure workflows that directly link structural motifs to emergent optoelectronic properties. This talk will show how machine learning enhanced simulations are reshaping our understanding of halide-perovskite QDs and highlight the methodological advances needed to achieve predictive, chemically aware MLFFs for complex, technologically relevant nanomaterials.

© FUNDACIO DE LA COMUNITAT VALENCIANA SCITO
We use our own and third party cookies for analysing and measuring usage of our website to improve our services. If you continue browsing, we consider accepting its use. You can check our Cookies Policy in which you will also find how to configure your web browser for the use of cookies. More info