2019/11/30 by Alex Albaugh, Todd R. Gingrich · 4 citations
Computer Science · Decision Sciences · Materials Science · Physics and Astronomy · #Computational Drug Discovery Methods #Function (biology) #Generalization #Machine Learning in Materials Science #Measure (data warehouse) #Monte Carlo method #Optimal Experimental Design Methods #Partition (number theory) #Partition function (quantum field theory) #Reciprocal #Sampling (signal processing) #cond-mat.stat-mech
paper · pdf · doi:10.1063/5.0025358
published in The Journal of Chemical Physics 153(20), 204102 (American Institute of Physics) · 16 pages, 6 figures
openalex created_date 2019/12/05 · arxiv created 2020/11/16 · arxiv updated 2020/11/18 · openalex publication_date 2020/11/23 · openalex updated_date 2026/08/05
Reaction rates are a complicated function of molecular interactions, which can be selected from vast chemical design spaces. Seeking the design that optimizes a rate is a particularly challenging problem since the rate calculation for any one design is itself a difficult computation. Toward this end, we demonstrate a strategy based on transition path sampling to generate an ensemble of designs and reactive trajectories with a preference for fast reaction rates. Each step of the Monte Carlo procedure requires a measure of how a design constrains molecular configurations, expressed via the reciprocal of the partition function for the design. Although the reciprocal of the partition function would be prohibitively expensive to compute, we apply Booth's method for generating unbiased estimates of a reciprocal of an integral to sample designs without bias. A generalization with multiple trajectories introduces a stronger preference for fast rates, pushing the sampled designs closer to the optimal design. We illustrate the methodology on two toy models of increasing complexity: escape of a single particle from a Lennard-Jones potential well of tunable depth and escape from a metastable tetrahedral cluster with tunable pair potentials.