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DTSTAMP:20260624T171341Z
LOCATION:Bldg. 8 - B 102
DTSTART;TZID=Europe/Stockholm:20260629T140000
DTEND;TZID=Europe/Stockholm:20260629T143000
UID:submissions.pasc-conference.org_PASC26_sess111_msa184@linklings.com
SUMMARY:Quantum (Bio) Molecular Simulations with Machine Learning Force Fi
 elds
DESCRIPTION:Adil Kabylda (University of Luxembourg)\n\nMachine learning fo
 rce fields (MLFFs) promise to bridge the gap between quantum-mechanical ac
 curacy 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 dynamic
 s. In this talk, I will present two contributions aimed at addressing thes
 e limitations. First, I will present SO3LR [2], a pretrained MLFF that cou
 ples an SO(3)-equivariant neural network with universal pairwise potential
 s for long-range electrostatics and dispersion. Second, I will introduce Q
 Cell [3], a quantum-mechanical dataset of ~0.5M diverse molecular fragment
 s extending chemical space coverage of cellular components, designed to pr
 ovide the breadth of data needed to train truly general-purpose models. Se
 lected examples will illustrate how these advances enable simulations of c
 omplex (bio)molecular systems with near-ab initio accuracy. I will conclud
 e with a discussion of current limitations and future directions.\n\n[1] U
 nke et al., Chem. Rev. 2021, 121, 16, 10142; https://doi.org/10.1021/acs.c
 hemrev.0c01111\n[2] Kabylda et al., J. Am. Chem. Soc. 2025, 147, 37, 33723
 ; https://doi.org/10.1021/jacs.5c09558\n[3] Kabylda et al., arXiv:2510.099
 39 2026; https://doi.org/10.48550/arXiv.2510.09939\n\nDomain: Chemistry an
 d Materials, Physics, Computational Methods and Applied Mathematics\n\nSes
 sion Chairs: Michał Sanocki (Technical University of Munich); Ian Störmer 
 (Technical University of Munich); and Philip Loche (Technical University o
 f Munich, EPFL)\n\n
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