SPML Lab Blog
Research updates, tutorials, and technical notes from SPML Lab for ML researchers entering scientific domains.
Post types Research 2 Tutorials 9 Technical notes 1
- MADField predicts the full 3D adsorbate density field in nanoporous materials, turning slow gas-adsorption simulation into a single forward pass.
- How HACO, a Human–AI Co-discovery system, produced MaskGXT, a competitive generative model for crystal structure prediction.
- 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.
- An introduction to GFlowNets from the perspective of probabilistic ML — sampling proportionally to rewards, training objectives, and connections to MaxEnt RL, variational inference, and diffusion models.
- From Jarzynski's equality to diffusion models — path measures unify free energy estimation, AIS, diffusion models, and GFlowNets as instances of the same mathematics.
- 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 — intuition, heuristic discretization, and rigorous Itô calculus — building from physical pictures to mathematical proof.
- 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.