Unraveling the Photochemical Mechanism of Iron Tris-2,2'-Bipyridine via Wavepacket Dynamics
Explore photochemical mechanisms of iron complexes through wavepacket dynamics, uncovering insights into molecular behavior and reactions.
Differentiable Simulations for Enhanced Sampling of Rare Events
Explore differentiable simulations for enhanced sampling of rare events in chemical reactions, focusing on path-integral optimization and improvements to standard DiffSim techniques.
Systematic Analysis of Biomolecular Conformational Ensembles with PENSA
Explore biomolecular conformational ensembles using PENSA. Learn systematic analysis techniques, featurization methods, and state-specific information quantification for efficient molecular mechanism determination.
Molecular Modeling of Complex Biological Systems
Explore molecular modeling techniques for complex biological systems, focusing on chromatin dynamics, DNA methylation effects, and histone tail modeling. Learn to predict system responses to modifications and mutations.
Learning Quantum Mechanics Using Embedding, Symmetric Polynomials and Composition
Explore quantum mechanics through embedding, symmetric polynomials, and composition. Gain insights into molecular dynamics, atomic cluster expansion, and catalysis with practical demonstrations.
Computational Modeling of Active Systems - Class 4
Explore computational modeling techniques for active systems, focusing on advanced concepts and practical applications in the field of active matter.
Computational Modeling of Active Systems - Class 3
Explore computational modeling techniques for active systems, focusing on advanced concepts and practical applications in the field of active matter.
Spacetime Correlations of the Classical Toda Chain in Thermal Equilibrium
Explore spacetime correlations in the classical Toda chain, comparing molecular dynamics simulations with Landau-Lifshitz theory predictions based on generalized hydrodynamics.
Discovering Dynamic Laws from Observations: The Case of Self-Propelled Particles
Explore machine learning techniques for uncovering forces in active matter systems, from cellular to animal scales. Learn to extract local dynamics crucial for understanding collective phenomena.
Computational Modeling of Active Systems - Class 1
Explore computational modeling techniques for active systems, focusing on fundamental principles and practical applications in the field of active matter.
Timewarp: Transferable Acceleration of Molecular Dynamics by Time-Coarsened Dynamics
Explore Timewarp: a transferable method for accelerating molecular dynamics simulations using normalizing flows, enhancing sampling efficiency for equilibrium properties of molecular systems.
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Explore accelerated molecular dynamics simulation using Timewarp, a transferable AI method for efficient sampling of molecular systems and discovery of metastable states.
PepFlow: Direct Conformational Sampling From Peptide Energy Landscapes
Explore PepFlow, a deep learning approach for direct conformational sampling of peptides. Learn about its modular design, hypernetwork integration, and applications in drug discovery.
Accelerate Atomistic Simulations, Sampling, and Dynamics - Day 2
Explore advanced techniques for accelerating atomistic simulations, enhancing sampling methods, and optimizing dynamics in drug discovery research.
Coarse Grained Biological Systems - Day 2
Explore coarse-grained biological systems in drug discovery with Jacopo Venturin, delving into advanced machine learning applications for pharmaceutical research.
Training Neural Network Potentials: Bayesian and Simulation-based Approaches
Explore advanced techniques for training neural network potentials in molecular dynamics simulations, including data-efficient methods, experimental data integration, and uncertainty quantification for reliable results.
Accelerating Cryptic Pocket Discovery Using AlphaFold and Markov State Modeling
Explore cryptic pocket discovery using AlphaFold and Markov State Modelling. Learn about Plasmepsin II, transition states, and activation mechanisms in drug discovery.
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
Explore innovative machine learning techniques for generative backmapping of coarse-grained proteins, focusing on transferability, reliability, and efficiency in molecular simulations.
Path Integral Stochastic Optimal Control for Sampling Transition Paths in Molecular Dynamics
Explore stochastic optimal control for sampling molecular transition paths, considering full geometry without relying on collective variables. Applicable to larger proteins like Polyproline and Chignolin.
Protein Folding Algorithms - Lecture 9
Explore protein folding algorithms, their applications, and computational approaches in structural biology and drug discovery.
Differentiable Simulations for Enhanced Sampling of Rare Events
Explore deep learning applications in molecular simulations, including surrogate functions, sampling complex distributions, and time-propagation of differential equations. Learn about active learning, generative models, and reaction path finding.