About the speaker:
Alexandre Tkatchenko is a Professor and former Department Head (2020-2025) at the University of Luxembourg, and a Distinguished Visiting Professor at TU Berlin. His group develops AI and first-principles models for complex materials. With over 600 talks, ~260 publications (h-index 95, 58k+ citations), and tenure as Top 1% ISI Highly Cited Researcher, he serves on journals like Science Advances and PRL. A recipient of numerous awards including APS Fellowship, Dirac Medal, and 5 ERC Grants, he co-founded Quastify GmbH.

Abstract:
The convergence between accurate quantum-mechanical (QM) models (and codes) with efficient machine learning (ML) methods seems to promise a paradigm shift in all-atom simulations. Many challenging applications are now being tackled by increasingly powerful QM/ML methodologies (https://doi.org/10.1021/acs.chemrev.0c01111; https://doi.org/10.1021/acs.chemrev.1c00107). These include modeling covalent materials, molecules, molecular crystals, surfaces, and even whole proteins under physiological conditions (https://www.science.org/doi/abs/10.1126/sciadv.adn4397; https://doi.org/10.1021/jacs.5c09558). In this talk, I attempt to provide a reality check on these recent advances and on the developments required to enable fully predictive dynamics of complex functional (bio)molecular and material systems. Multiple challenges are highlighted -- in particular transferability in chemical space and interatomic interactions -- that should enable this field to grow for the foreseeable future.

Inviter: 任新国（renxg@iphy.ac.cn）

Contact: 胡颖（8264 9361）