Chatipat Lorpaiboon

Zihan Pengmei, Chatipat Lorpaiboon, Spencer C. Guo et al.

Identifying informative low-dimensional features that characterize dynamics in molecular simulations remains a challenge, often requiring extensive manual tuning and system-specific knowledge. Here, we introduce geom2vec, in which pretrained graph neural networks (GNNs) are used as universal geometric featurizers. By pretraining equivariant GNNs on a large dataset of molecular conformations with a self-supervised denoising objective, we obtain transferable structural representations that are useful for learning conformational dynamics without further fine-tuning. We show how the learned GNN representations can capture interpretable relationships between structural units (tokens) by combining them with expressive token mixers. Importantly, decoupling training the GNNs from training for downstream tasks enables analysis of larger molecular graphs (such as small proteins at all-atom resolution) with limited computational resources. In these ways, geom2vec eliminates the need for manual feature selection and increases the robustness of simulation analyses.

Chatipat Lorpaiboon, Jonathan Weare, Aaron R. Dinner

For a transition between two stable states, the committor is the probability that the dynamics leads to one stable state before the other. It can be estimated from trajectory data by minimizing an expression for the transition rate that depends on a lag time. We show that an existing such expression is minimized by the exact committor only when the lag time is a single time step, resulting in a biased estimate in practical applications. We introduce an alternative expression that is minimized by the exact committor at any lag time. Numerical tests on benchmark systems demonstrate that our committor and resulting transition rate estimates are much less sensitive to the choice of lag time. We derive an additional expression for the transition rate, relate the transition rate expression to a variational approach for kinetic statistics based on the mean-squared residual, and discuss further numerical considerations with the aid of a decomposition of the error into dynamic modes.