报告题目：Unlocking Schrödinger's dream with Al-driven molecular simulations

报 告 人：Prof. Alexandre TKatchenko, University of Luxembourg

报告时间：2026年3月31日 10：00

报告地点：物理楼W105

内容摘要：The convergence between accurate quantum-mechanical (QM) models (and codes) with efficient machine learning (ML) methods seem to promise a paradigm shift in all-atom simulations. Many challenging applications are now being tackled by increasingly powerful QM/ML methodologies [1-2]. These include modeling covalent materials, molecules, molecular crystals, surfaces, and even whole proteins under physiological conditions [3-4]. 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.

参考文献：

[1] Chem. Rev. 121, 10142 (2021), https://doi.org/10.1021/acs.chemrev.0c01111

[2] Chem. Rev. 121, 9816 (2021), https://doi.org/10.1021/acs.chemrev.1c00107

[3] Sci. Adv. 10, eadn4397 (2024), https://www.science.org/doi/abs/10.1126/sciadv.adn4397

[4] J. Am. Chem. Soc. 147, 33723 (2025), https://doi.org/10.1021/jacs.5c09558

报告人简介：Tkatchenko教授自2020年起担任卢森堡大学物理与材料科学系主任及理论化学物理教授。团队开发了精确和高效的第一性原理计算模型来广泛研究复杂材料，旨在定性理解和定量预测其在原子尺度及更大尺度上的结构、内聚力、电子和光学性质。Tkatchenko教授在著名期刊上发表了200多篇文章（h指数80，引用超过35000次）；担任Sci Adv.，Phys. Rev. Lett.和J. Phys. Chem. Lett.编委会成员；担任美国物理学会APS Fellow；获得德国物理学会Gerhard ErtI青年研究员奖，世界理论与计算化学家协会(WATOO) Dirac奖章，国际非共价相互作用会议(ICNI) van der Waals奖。