93 citations · 205 across the 16 of their papers we have counts for
17 papers
Mapping recrystallization trajectories in GaAs using latent space diffraction analysis
Ellis Rae Kennedy, Kwanghwi Je, Erik Thiede
Recrystallization in disordered solids proceeds through a sequence of local structural rearrangements that are difficult to resolve using conventional diffraction analysis. In amor…
A Transformer-based Model for Rapid Microstructure Inference from Four-Dimensional Scanning Transmission Electron Microscopy Data
Kwanghwi Je, Ellis R. Kennedy, Sungin Kim +2
Properties of crystalline materials are closely linked to microstructure arising from the spatial arrangement, orientation, and phase of nanocrystals. Rapid characterization of cry…
Cryo-EM as a Stochastic Inverse Problem
Diego Sanchez Espinosa, Erik H Thiede, Yunan Yang
Cryo-electron microscopy (Cryo-EM) enables high-resolution imaging of biomolecules, but structural heterogeneity remains a major challenge in 3D reconstruction. Traditional methods…
Error Breakdown and Sensitivity Analysis of Dynamical Quantities in Markov State Models
Yehor Tuchkov, Luke Evans, Sonya M. Hanson +1
Markov state models (MSMs) are widely employed to analyze the kinetics of complex systems. But despite their effectiveness in many applications, MSMs are prone to systematic or sta…
Ensemble reweighting using Cryo-EM particles
Wai Shing Tang, David Silva-Sánchez, Julian Giraldo-Barreto +5
Cryo-electron microscopy (cryo-EM) has recently become a premier method for obtaining high-resolution structures of biological macromolecules. However, it is limited to biomolecula…
Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
Zimu Li, Zihan Pengmei, Han Zheng +3
Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general…
Understanding the Sources of Error in MBAR through Asymptotic Analysis
Xiang Sherry Li, Brian Van Koten, Aaron R. Dinner +1
Multiple sampling strategies commonly used in molecular dynamics, such as umbrella sampling and alchemical free energy methods, involve sampling from multiple thermodynamic states.…
Autobahn: Automorphism-based Graph Neural Nets
Erik Henning Thiede, Wenda Zhou, Risi Kondor
We introduce Automorphism-based graph neural networks (Autobahn), a new family of graph neural networks. In an Autobahn, we decompose the graph into a collection of subgraphs and a…
A Bayesian approach for extracting free energy profiles from cryo-electron microscopy experiments using a path collective variable
Julian Giraldo-Barreto, Sebastian Ortiz, Erik H. Thiede +4
Cryo-electron microscopy (cryo-EM) extracts single-particle density projections of individual biomolecules. Although cryo-EM is widely used for 3D reconstruction, due to its single…
A community-powered search of machine learning strategy space to find NMR property prediction models
Lars A. Bratholm, Will Gerrard, Brandon Anderson +26
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML st…
Integrated VAC: A robust strategy for identifying eigenfunctions of dynamical operators
Chatipat Lorpaiboon, Erik Henning Thiede, Robert J. Webber +2
One approach to analyzing the dynamics of a physical system is to search for long-lived patterns in its motions. This approach has been particularly successful for molecular dynami…
Error bounds for dynamical spectral estimation
Robert J. Webber, Erik H. Thiede, Douglas Dow +2
Dynamical spectral estimation is a well-established numerical approach for estimating eigenvalues and eigenfunctions of the Markov transition operator from trajectory data. Althoug…
The general theory of permutation equivarant neural networks and higher order graph variational encoders
Erik Henning Thiede, Truong Son Hy, Risi Kondor
Previous work on symmetric group equivariant neural networks generally only considered the case where the group acts by permuting the elements of a single vector. In this paper we…
Galerkin Approximation of Dynamical Quantities using Trajectory Data
Erik H. Thiede, Dimitrios Giannakis, Aaron R. Dinner +1
Understanding chemical mechanisms requires estimating dynamical statistics such as expected hitting times, reaction rates, and committors. Here, we present a general framework for…
Stratification as a general variance reduction method for Markov chain Monte Carlo
Aaron R. Dinner, Erik Thiede, Brian Van Koten +1
The Eigenvector Method for Umbrella Sampling (EMUS) belongs to a popular class of methods in statistical mechanics which adapt the principle of stratified survey sampling to the co…
Eigenvector method for umbrella sampling enables error analysis
Erik Thiede, Brian Van Koten, Jonathan Weare +1
Umbrella sampling efficiently yields equilibrium averages that depend on exploring rare states of a model by biasing simulations to windows of coordinate values and then combining…
Sharp entrywise perturbation bounds for Markov chains
Erik Thiede, Brian Van Koten, Jonathan Weare
For many Markov chains of practical interest, the invariant distribution is extremely sensitive to perturbations of some entries of the transition matrix, but insensitive to others…