Accelerating Instanton Theory with the Line Integral String Method, Gaussian Process Regression, and Selective Hessian Modeling
Chenghao Zhang, Amke Nimmrich, Axel Gomez, Munira Khalil, and Niranjan Govind

TL;DR
This paper introduces a Gaussian process regression enhanced line integral string method to efficiently compute tunneling rates and splittings in molecular reactions, significantly reducing computational costs while maintaining accuracy.
Contribution
The authors develop a novel surrogate modeling approach that accelerates instanton calculations and reduces force and Hessian evaluations using selective Hessian training and GPU acceleration.
Findings
Achieves order of magnitude speedup in force evaluations
Predicts tunneling rates within 20% of exact values
Accurately computes tunneling splittings in complex molecules
Abstract
We develop a Gaussian process regression enhanced line integral string method to accelerate ring polymer instanton calculations of tunneling rates and tunneling splittings in molecular proton transfer reactions. By exploiting uncertainty estimates from the surrogate representation, we show that the number of force evaluations required to converge an instanton path becomes effectively independent of the number of beads used to discretize the pathway. To reduce the computational overhead associated with training, particularly when Hessian information is included, we implement graphics processing unit accelerated black box matrix matrix multiplication, achieving an order of magnitude speedups relative to standard implementations. For rate calculations, we introduce a selective Hessian training strategy that distinguishes flexible modes strongly coupled to the transferring proton from more…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science · Advanced Chemical Physics Studies · Chemical Reactions and Mechanisms
