2 citations · 4 across the 11 of their papers we have counts for
14 papers
LiveCoMS Roundtable: Discussing Markov state models after two decades of development v1.0
Daniel M. Zuckerman, Gregory R. Bowman, Amedeo Caflisch +14
Markov state models (MSMs) play a central role in the analysis of molecular dynamics (MD) simulations for biomolecules, and MSM methodologies have been under continual development…
Machine learning kinetics from molecular dynamics data
Jonathan Weare, Aaron R. Dinner
Most molecular transitions occur on time scales far beyond direct molecular dynamics simulations. The committor, the probability that a configuration reaches a product state before…
Nuclear Quantum Effects as a Denoising Problem
Weizhou Wang, Jonathan Weare, Aaron R. Dinner
Nuclear quantum effects are rigorously captured by imaginary-time path integrals, which map the quantum Boltzmann distribution onto a ring polymer of classical replicas. Yet the nu…
Composing diffusion priors with explicit physical context via generative Gibbs sampling
Weizhou Wang, Jonathan Weare, Aaron R. Dinner
Pretrained diffusion models provide powerful learned priors, but in scientific sampling the target distribution often depends on physical context that is not fully represented by o…
Adaptive tensor train metadynamics for high-dimensional free energy exploration
Nils E. Strand, Siyao Yang, Yuehaw Khoo +1
A key challenge for molecular dynamics simulations is efficient exploration of free energy landscapes over relevant collective variables (CV). Common methods for enhancing sampling…
Quantum statistics from classical simulations via generative Gibbs sampling
Weizhou Wang, Xuanxi Zhang, Jonathan Weare +1
Accurate simulation of nuclear quantum effects is essential for molecular modeling but expensive using path integral molecular dynamics (PIMD). We present GG-PI, a ring-polymer-bas…
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…
Mitigating mode collapse in normalizing flows by annealing with an adaptive schedule: Application to parameter estimation
Yihang Wang, Chris Chi, Aaron R. Dinner
Normalizing flows (NFs) provide uncorrelated samples from complex distributions, making them an appealing tool for parameter estimation. However, the practical utility of NFs remai…
Characterizing nonlinear dynamics by contrastive cartography
Nicolas Romeo, Chris Chi, Aaron R. Dinner +1
The qualitative study of dynamical systems using bifurcation theory is key to understanding systems from biological clocks and neurons to physical phase transitions. Data generated…
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…
Sampling parameters of ordinary differential equations with Langevin dynamics that satisfy constraints
Chris Chi, Jonathan Weare, Aaron R. Dinner
Fitting models to data to obtain distributions of consistent parameter values is important for uncertainty quantification, model comparison, and prediction. Standard Markov chain M…
BAD-NEUS: Rapidly converging trajectory stratification
John Strahan, Chatipat Lorpaiboon, Jonathan Weare +1
An issue for molecular dynamics simulations is that events of interest often involve timescales that are much longer than the simulation time step, which is set by the fastest time…
Elucidating the Role of Filament Turnover in Cortical Flow using Simulations and Representation Learning
Yuqing Qiu, Elizabeth D. White, Edwin M. Munro +2
Cell polarization relies on long-range cortical flows, which are driven by active stresses and resisted by the cytoskeletal network. While the general mechanisms that contribute to…