93 citations · 746 across the 53 of their papers we have counts for
59 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 timescales far beyond direct molecular dynamics simulations. The committor, the probability that a configuration reaches a product state before…
Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?
Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun +4
AI weather prediction (AIWP) models rival physics-based models, yet the sources of their unexpected forecast accuracy and the degree of their physical fidelity remain unclear. Here…
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…
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…
AI-boosted rare event sampling to characterize extreme weather
Amaury Lancelin, Alex Wikner, Laurent Dubus +5
Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study using limited observations or complex climate mod…
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…
Improved energies and wave function accuracy with Weighted Variational Monte Carlo
Huan Zhang, Robert J. Webber, Michael Lindsey +2
Neural network parametrizations have increasingly been used to represent the ground and excited states in variational Monte Carlo (VMC) with promising results. However, traditional…
Can AI weather models predict out-of-distribution gray swan tropical cyclones?
Y. Qiang Sun, Pedram Hassanzadeh, Mohsen Zand +3
Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather models and long-term climate…
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…
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data
Huan Zhang, Justin Finkel, Dorian S. Abbot +2
Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however,…
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…
AI can identify Solar System instability billions of years in advance
Dorian S. Abbot, J. D. Laurence-Chasen, Robert J. Webber +2
Rare event schemes require an approximation of the probability of the rare event as a function of system state. Finding an appropriate reaction coordinate is typically the most cha…
Accurate estimates of dynamical statistics using memory
Chatipat Lorpaiboon, Spencer C. Guo, John Strahan +2
Many chemical reactions and molecular processes occur on timescales that are significantly longer than those accessible by direct simulation. One successful approach to estimating…
Randomly sparsified Richardson iteration: A dimension-independent sparse linear solver
Jonathan Weare, Robert J. Webber
Recently, a class of algorithms combining classical fixed point iterations with repeated random sparsification of approximate solution vectors has been successfully applied to eige…
Mercury's chaotic secular evolution as a subdiffusive process
Dorian S. Abbot, Robert J. Webber, David M. Hernandez +2
Mercury's orbit can destabilize, generally resulting in a collision with either Venus or the Sun. Chaotic evolution can cause g1 to decrease to the approximately constant value of…
Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction
John Strahan, Spencer C. Guo, Chatipat Lorpaiboon +2
Understanding dynamics in complex systems is challenging because there are many degrees of freedom, and those that are most important for describing events of interest are often no…
Simple physics and integrators accurately reproduce Mercury instability statistics
Dorian S. Abbot, David M. Hernandez, Sam Hadden +3
The long-term stability of the Solar System is an issue of significant scientific and philosophical interest. The mechanism leading to instability is Mercury's eccentricity being p…
Understanding and eliminating spurious modes in variational Monte Carlo using collective variables
Huan Zhang, Robert J. Webber, Michael Lindsey +2
The use of neural network parametrizations to represent the ground state in variational Monte Carlo (VMC) calculations has generated intense interest in recent years. However, as w…
Predicting rare events using neural networks and short-trajectory data
John Strahan, Justin Finkel, Aaron R. Dinner +1
Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic dynamical systems. When the event is rare in comparison with the timescales of simula…
Computing transition path theory quantities with trajectory stratification
Bodhi P. Vani, Jonathan Weare, Aaron R. Dinner
Transition path theory computes statistics from ensembles of reactive trajectories. A common strategy for sampling reactive trajectories is to control the branching and pruning of…
Revealing the statistics of extreme events hidden in short weather forecast data
Justin Finkel, Edwin P. Gerber, Dorian S. Abbot +1
Extreme weather events have significant consequences, dominating the impact of climate on society. While high-resolution weather models can forecast many types of extreme events on…
Augmented Transition Path Theory for Sequences of Events
Chatipat Lorpaiboon, Jonathan Weare, Aaron R. Dinner
Transition path theory provides a statistical description of the dynamics of a reaction in terms of local spatial quantities. In its original formulation, it is limited to reaction…