Workshops

Ultra-Coarse-Graining and Its Applications

How do we build good databases for machine learning of force fields?

Embedding Quantum Regions into Classical Environments

Markov modeling of protein-protein interactions and their effects on protein conformational dynamics

Covariant neural networks for learning graphs and atomic potentials

Beyond the second timescale in all-atom MD: shallow and deep kinetic models for biomolecules

General-purpose Surrogate Methods for Global Optimization and Uncertainty Quantification of Computationally Expensive Nonconvex Models with Some Environmental Applications

Addressing Complexity with Surrogate Models in Computational Catalyst Design for Energy Applications

Simultaneous Learning and Exploring Atomistic Potential Surfaces: Current Progress and Mathematical Challenges

A unified framework for umbrella sampling in reversible and irreversible systems

Learning interactions from microscopic observables

Girsanov reweighting for path ensembles and Markov state models

Coarse grained simulation models and the challenge of representing conformational states, transitions, and responses

Scalable algorithms for kernel-based surrogates in prediction and optimization

Exploration and learning of energy and free energy landscapes

Data-driven characterization and control of complex systems

Towards Universal ML Potential for Organic Molecules

Extending the SOAP descriptor to make alloy GAP potentials

Who needs atoms to design materials?

Open Systems Simulations of Macromolecular Solutes through Adaptive Resolutions Simulations (AdResS)

Incorporating Experimental Data in Long Timescales Macromolecular Simulations

Development of a coarse-grained model for co-translational membrane protein folding

Machine-learning of crystal structure energy landscapes