4 papers · 1 filter
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…
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…
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…
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…