StateWeaver: Tracing State Changes in Adaptive Molecular Dynamics
Authors
Nikhil Maturi (Independent)
Abstract
Adaptive molecular dynamics produces many short simulations that begin from frames of earlier runs. This branching improves sampling, but it makes a simple question hard to answer: when a state-transition probability changes, which simulations produced the change? StateWeaver joins state occupancy and transition comparisons with the simulation lineage. It covers the supplied assignments for free Aβ42, Aβ42 with tramiprosate, and Aβ42 with 3-sulfopropanoic acid: 955 simulations and 1,899,875 assigned frames in each of three Markov-state models. Selecting a transition shows the treatment and baseline probabilities, their difference, a descriptive standard error, event counts, and the runs in which the transition occurs. In the 3-state model, selecting SPA 0→1 reduces 319 simulations to 16 runs containing 209 observed transitions. The difference is +0.10 percentage points with a standard error of 0.18, so it remains uncertain. StateWeaver provides a focused way to move from an ensemble-level difference back to the simulations behind it without turning a descriptive pattern into a biological claim.