Speaker: Adil Kabylda (Department of Physics, FSTM, University of Luxembourg)
Title: Machine Learning Force Fields for Large Molecules.
Time: Wednesday, 2024.05.08, 10:00 a.m. (CET)
Place: fully virtual (contact Jakub Lengiewicz or Elisa GÓMEZ DE LOPE to register)
Abstract: Machine Learning Force Fields (MLFFs) enable the modeling of chemical systems by combining ab initio accuracy with the efficiency of classical force fields. Despite the great success of ML methods, extending their applicability to larger molecules poses a challenge, partly due to the rapid growth of the dimensionality of the descriptor. State-of-the-art descriptors include non-essential degrees of freedom or neglect long-range interactions by imposing a cut-off radius. Thus, finding a way to accurately describe both short- and long-range interactions without significantly increasing descriptor size is a critical step required to advance ML modeling.
In this seminar, I will discuss recent progress and challenges for next-generation MLFFs. Specifically, I will focus on global MLFFs that can efficiently model large and flexible molecules without resorting to any potentially uncontrolled approximation [1]. I will demonstrate the possibility of achieving linear scaling in global MLFFs for large systems through an automated descriptor reduction approach [2]. The studied systems include units of four major types of biomolecules and a supramolecular complex.
[1] doi.org/10.1126/sciadv.adf0873
[2] doi.org/10.1038/s41467-023-39214-w
Adil Kabylda is a PhD Researcher in Theoretical Chemical Physics Group at the University of Luxembourg. He obtained B.Sc. and M.Sc. degrees in Chemistry from Moscow State University in 2021. His research focuses on extending the applicability of Machine Learning Force Fields to larger (bio)molecules, with a particular emphasis on accurately describing long-range interactions.
The aim of the Machine Learning Seminar series is to host presentations on fundamental and methodological advances in data science and machine learning, as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. Therefore, the focus is on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, and Computational Behavioural and Social Sciences. The seminar aims to bring together young and experienced researchers from various disciplines to exchange ideas on Machine Learning techniques. It is currently affiliated with the University of Luxembourg and is run under the auspices of the DTU DRIVEN PRIDE project, funded by the FNR, and the widening participation DRIVEN project, funded by H2020. The seminar also welcomes talks by researchers from a wider collaborative network, including but not limited to early-stage researchers in RAINBOW ITN, as well as current and incoming individual Marie Skłodowska-Curie fellows.
The usual format is as follows: a short presentation (20-30 minutes) followed by a longer discussion (30-40 minutes). The usual time is Wednesdays at 10:00 a.m. (CET). If you are interested in joining, please contact Jakub Lengiewicz. See www.jlengineer.eu/ml-seminar/.
8 май 2024
