Mean First-Passage-Time-Directed Bias Optimization with Gaussian-Kernel Markov Models
Year: 2026
Type: preprint
Abstract: Rare transitions limit the direct simulation of molecular kinetics and complicate the design of enhanced-sampling biases that accelerate specific events without obscuring their mechanism. We develop a data-driven framework in which biased trajectory data are combined using dynamic histogram analysis and represented by either conventional grids or normalized kernel-anchor memberships to construct finite row-stochastic kinetic models. These models are used to adapt a bounded bias by directly minimizing an absorbing mean first-passage time (MFPT) objective, while reactive-current diagnostics independently assess whether accelerated trajectories preserve the reference transition mechanism. We first test the kernel-induced representation in equilibrium free-energy reconstruction and then apply the kinetic optimization framework to progressively more challenging rareevent problems, including membrane permeation, multidimensional barrier crossing, and a metastable molecular rearrangement. The benchmark calculations show that MFPT-directed biasing can efficiently promote target arrival and that soft kernel states provide a flexible alternative to rigid spatial discretization. At the same time, pathway analysis shows that faster arrival does not imply preservation of the underlying transition mechanism. A confounded high-dimensional stress test further exposes the limitations of projected kinetic models when the relevant dynamical coordinates are unresolved.
Source: ChemRxiv
Institutions: University College London, University of Portsmouth
Language: English
Subfield: Physical and Theoretical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214708857