报告题目：Quantum Molecular Simulations with Machine Learning Force Fields
报 告 人：Dr. Adil Kabylda, University of Luxembourg
报告时间：2026年3月31日 11：00
报告地点：物理楼W105
内容摘要：Machine learning force fields (MLFFs) promise to bridge the gap between quantum-mechanical accuracy and the computational efficiency needed to simulate realistic (bio)molecular systems [1], Yet their predictive power is often limited by the quality and coverage of training data, as well as by locality assumptions that miss the long-range effects governing molecular structure and dynamics, In this talk, I will present two contributions aimed at addressing these limitations, First, I will present SO3LR [2], a pretrained MLFF that couples an SO(3)-equivariant neural network with universal pairwise potentials for long-range electrostatics and dispersion. Second, I will introduce QCell [3], a quantum-mechanical dataset of ~0.5M diverse molecular fragments extending chemical space coverage of cellular components, designed to provide the breadth of data needed to train truly general-purpose models, Selected examples will illustrate how these advances enable simulations of complex (bio)molecular systems with near-ab initio accuracy. I will conclude with a short hands-on tutorial on using SO3LR and a discussion of current limitations and future directions.
参考文献：
[1] Chem. Rev. 121, 9816 (2021), https://doi.org/10.1021/acs.chemrev.1c00107
[2] J. Am. Chem. Soc. 147, 33723 (2025), https://pubs.acs.org/doi/10.1021/jacs.5c09558
[3] AI Sci. (2026), https://doi.org/10.1088/3050-287X/ae5267
报告人简介： Adil Kabylda was born and raised in Paviodar, Kazakhstan, and received his B,Sc. and M.Sc. degrees in Chemistry (summa cum laude) from Moscow State University in 2021. He completed his Ph.D. in Physics at the University of Luxembourg with Prof. Alexandre Tkatchenko in 2026. During his Ph.D. studies, he developed general-purpose machine-learning force fields for quantum-accurate simulations of complex (bio)molecular systems. Honors include gold medal at the 47th International Chemistry Olympiads (2015), a Yandex Silver Medal (2020), an FNR AFR Ph.D, Fellowship (2021), and selection as a Fellow for the 71st Lindau Nobel Laureate Meeting (2022) and the 56th International Achievement Summit (2026).
