## Description

Back to topThe dynamical behavior of molecular systems of relevance to chemistry, biophysics and materials science can be numerically simulated using deterministic molecular dynamics algorithms or stochastic algorithms such as Langevin dynamics. Although the systems of interest are composed of large numbers of atoms, collective interactions mean that the long-time evolution is in fact typically dictated by the variations of a small number of collective modes, known as collective variables or reaction coordinates. Intense efforts have recently been invested in automating the definition of collective variables from molecular simulation data using a variety of machine learning techniques. A key mathematical question is to characterize the quality of the dimensionality reduction, for instance by a priori or, even better, a posteriori estimates on the error committed by integrating the dynamics associated with the coarse-grained reduced model. Another important issue is how to incorporate various constraints into the discovery process, such as symmetries (permutation, rotation, translation). This workshop will focus on recent advances in the data driven learning and validation of collective modes and their applications in coarse-grained simulations and enhanced sampling.

This workshop will include **lightning talks and a poster session** for early career researchers (including graduate students). If accepted, you will be asked to do both. In order to propose a lightning session talk and a poster, you must first register for the workshop, and then submit a proposal using the form that will become available on this page after you register. **The registration form should not be used to propose a lightning session talk or poster**.

The deadline for proposing is** April 2, 2024**. If your proposal is accepted, you should plan to attend the event in-person.

## Organizers

Back to top## Speakers

Back to top## Schedule

Back to top**Speaker: **Marina Meila (University of Washington)

**Speaker: **Roberto Covino (Frankfurt Institute for Advanced Studies)

**Speaker: **Karen Palacio-Rodriguez (MPI Biophysics, Frankfurt)

**Speaker: **Andrew Ferguson (University of Chicago)

**Speaker: **Tony Lelièvre (l’École des Ponts ParisTech)

- Luke Evans,
*Model Misspecification and Collective Variables in Simulation-based inference for cryo-EM* - Matteo Carli,
*Nonparametric free energy estimation in high-dimensional CV spaces* - John Maier,
*Designing target-optimized coarse-grained representations for soft materials using attentive message-passing* - Xiaoou Cheng,
*The surprising efficiency of temporal difference learning for rare event prediction* - Shashank Sule,
*Learning collective variables for quantifiably good representations of molecular dynamics* - Eric Beyerle,
*Learning Thermodynamics and Reaction Coordinates for Colloidal Phase Transitions* - Andrea Guljas,
*Machine Learned Molecular Coarse-Graining of Protein Folding and Aggregation* - Carl Henning Hansen,
*Combining Molecular Simulations and Time-Resolved Experimental Data using the Fokker-Planck Equation and Maximum Entropy Reweighting* - Mike Jones,
*A Flow-matching Approach for Generative Backmapping of Biomolecules* - Juno Nam,
*Revealing hidden alchemical degrees of freedom in machine learning interatomic potentials*

**Speaker: **Rafael Gomez-Bombarelli (Massachusetts Institute of Technology (MIT))

**Speaker: **Nick Jackson (University of Illinois)

**Speaker: **Marloes Arts (Genmab)

**Speaker: **Grant Rotskoff (University of Stanford)

**Speaker: **Paraskevi Gkeka (Sanofi France)

- Adolfo Poma,
*GōMartini 3: A Computational Tool to Elucidate Conformational Changes in Proteins* - Chatipat Lorpaiboon,
*Dimensionality reduction for transient and metastable systems using VAMPnets with boundary conditions* - Abhik Ghosh Moulick,
*Comparative Exploration of nucleosome Dynamics using All-Atom and Coarse-Grain Approaches* - Thomas Pigeon,
*Estimating committor function using rare event sampling* - Suemin Lee,
*Unraveling Protein-Ligand Dissociation Kinetics Through State Predictive Information Bottleneck based Enhanced Sampling* - Yaoyi Chen,
*Enhancing Data Efficiency in Coarse-Grained Force Field Parameterization with Machine Learning* - Spencer Guo,
*Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction* - Rutika Patel,
*Conformational dynamics of nucleosomal histone H2B tails revealed by molecular dynamics and Markov state model* - Soojung Yang,
*Learning Collective Variables for Protein Folding with Labeled Data Augmentation through Geodesic Interpolation* - Subarna Sasmal,
*Reaction Coordinates for Conformational Transitions Using Linear Discriminant Analysis on Positions*

**Speaker: **Peter Bolhuis (University of Amsterdam)

**Speaker: **David Aristoff (Colorado State University)

**Speaker: **Rose Yu (University of California, San Diego (UCSD))

**Speaker: **Bernd Ensing (University of Amsterdam)

**Speaker: **Laura Filion (University of Utrecht)

**Speaker: **Hanyu Zhang (TikTok)

**Speaker: **Pilar Cossio (Flatiron Institute)

**Speaker: **Yusu Wang (University of California, San Diego (UCSD))

**Speaker: **Klara Bonneau (Freie University Berlin)

**Speaker: **Ming Zhong (Illinois Institute of Technology)

**Speaker: **Christophe Chipot (CNRS and Université de Nancy)

## Poster Session

Back to topThe posters that have been submitted in advance for the poster session are available on the poster session page.

## Videos

Learning the committor, free energy profile, and rates with ML-driven path sampling simulations

Roberto Covino

April 22, 2024

Towards quantitative prediction of kinetic rates and optimal reaction coordinates from the projected dynamics of transition paths

Karen Palacio-Rodriguez

April 22, 2024

A high-bias, low-variance review of data-driven collective variable discovery and enhanced sampling

Andrew Ferguson

April 22, 2024

Mathematical aspects of coarse graining: functional inequalities and quasi-stationary distributions

Tony Lelièvre

April 23, 2024

End-to-end differentiation for reversible coarse-graining and rare-event sampling

Rafael Gomez-Bombarelli

April 23, 2024

Quantum-Chemical Predictions from Coarse-Grained Molecular Representations

Nick Jackson

April 23, 2024

Autoencoders for dimensionality reduction in molecular dynamics: Collective variable dimension, biasing, and transition states

Paraskevi Gkeka

April 24, 2024

Learning reaction coordinates and finding optimal model parameters from sampled trajectory ensembles

Peter Bolhuis

April 24, 2024

Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy

Pilar Cossio

April 25, 2024

Graph learning models: theoretical understanding, limitations and enhancements

Yusu Wang

April 25, 2024