Tutorials
Long-form explanations of scientific machine learning, from mathematical foundations to molecular and biological applications.
- Gas adsorption simulation: uptake, grand canonical Monte Carlo, classical density functional theory, and density-field learning.
- A practical bridge from molecular dynamics to enhanced sampling, metadynamics, collective variables, and recent ML approaches for rare molecular events.
- Statistical mechanics: from Newton's equations to ensembles, thermostats, barostats, Monte Carlo, and connections to generative modeling.
- GFlowNets from a probabilistic-ML perspective: reward-proportional sampling, training objectives, and connections to MaxEnt RL and variational inference.
- An introduction to protein structure, function, and computational design — from amino acids to the RFDiffusion/ProteinMPNN pipeline.
- Heterogeneous electrocatalysis: the energy storage problem, why oxides matter, the solid-liquid interface, and why real catalyst design is hard.
- Three routes to the Fokker-Planck equation—physical intuition, heuristic discretization, and a rigorous derivation with Itô calculus.
- Quantum chemistry and density functional theory: from the Schrödinger equation to Kohn-Sham DFT and modern deep learning approaches.
- Understanding the spherical equivariant layers that power modern molecular neural networks, from group theory foundations to Clebsch-Gordan tensor products.