4 papers
Markov State Models for Tracking Reaction Dynamics on Catalytic Nanoparticles
Caitlin A. McCandler, Chatipat Lorpaiboon, Timothy C. Berkelbach +1
Markov state models (MSMs) are a powerful tool to analyze and coarse-grain complex dynamical data into interpretable kinetic processes. This capability is particularly important in…
Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
Zihan Pengmei, Spencer C. Guo, Chatipat Lorpaiboon +1
Molecular dynamics simulations can generate atomically detailed trajectories of complex systems, but analyzing these dynamics can be challenging when systems lack well-established…
An exact multiple-time-step variational formulation for the committor and the transition rate
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…
Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics
Zihan Pengmei, Chatipat Lorpaiboon, Spencer C. Guo +2
Identifying informative low-dimensional features that characterize dynamics in molecular simulations remains a challenge, often requiring extensive manual tuning and system-specifi…