Probabilistic Machine Learning in Bioinformatics
Explore probabilistic machine learning applications in bioinformatics, focusing on cutting-edge techniques and their impact on biological data analysis.
Computational Modeling of Active Systems - Class 2
Explore computational modeling techniques for active systems, focusing on advanced concepts and practical applications in the field of active matter.
EA-TDDFT for Calculating X-ray Absorption Spectra
Explore EA-TDDFT for accurate X-ray absorption spectra calculations. Learn its applications in liquid-phase systems, glycerol aerosols, and Fe(CO)5 photodissociation, revealing insights into electronic and nuclear structure interplay.
Crystallization Machine Learning - Theory and Simulation Techniques
Explore machine learning applications in crystallization, from molecular dynamics to enhanced sampling techniques for rare events and homogeneous nucleation.
CHGNet: A Pre-trained Universal Interatomic Potential for Electron-Coupled Ionic Systems
Explore CHGNet, a graph-neural-network-based machine-learning potential for modeling universal potential energy surfaces in large-scale simulations with complex electron interactions.
Diffusion Models
Explore advanced concepts in diffusion models, including theory, applications, and recent developments in probabilistic AI.
Learning Equivariant Non-Local Electron Density Functionals
Delve into advanced machine learning approaches for improving density functional theory through equivariant graph neural networks, focusing on non-local exchange-correlation functionals and molecular energy predictions.