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Lars Holdijk

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Scaling Deep Learning Solutions for Transition Path Sampling
Jungyoon Lee
Michael Plainer
Yuanqi Du
Lars Holdijk
Rob Brekelmans
Carla P Gomes
Dominique Beaini
Kirill Neklyudov
Transition path sampling (TPS) is an important method for studying rare events, such as they happen in chemical reactions or protein folding… (voir plus). These events occur so infrequently that traditional simulations are often impractical, and even recent machine-learning approaches struggle to address this issue for larger systems. In this paper, we propose using modern deep learning techniques to improve the scalability of TPS methods significantly. We highlight the need for better evaluations in the existing literature and start by formulating TPS as a sampling problem over an unnormalized target density and introduce relevant evaluation metrics to assess the effectiveness of TPS solutions from this perspective. To develop a scalable approach, we explore several design choices, including a problem-informed neural network architecture, simulated annealing, the integration of prior knowledge into the sampling process, and attention mechanisms. Finally, we conduct a comprehensive empirical study and compare these design choices with other recently developed deep-learning methods for rare event sampling.
2025-03-04
ICLR.cc/2025/Workshop/GEM (publié)
openreview.net
openreview.net