Tutorial

Comprehensive user guide for the Embedded-Atom Method (EAM), covering theory, potential fitting, and applications to …

The key difference between multi-sample VAEs and IWAEs: how log-of-averages creates a tighter bound on log-likelihood.

A micro-review of Optical Chemical Structure Recognition (OCSR), covering rule-based systems to modern deep learning …

Visualize SELFIES molecular representations and test their 100% robustness through random sampling experiments.

Learn how to create 2D molecular images from SMILES strings using RDKit and PIL, with proper formatting and legends.

Compare inverse transform sampling and von Neumann's rejection method for exponential random numbers with Python …

Guide to implementing the Müller-Brown potential in PyTorch, comparing analytical vs automatic differentiation with …

How I used modern software engineering (caching, vectorization, and dependency locking) to reproduce a 60-year-old …

Learn to implement VAEs in PyTorch: ELBO objective, reparameterization trick, loss scaling, and MNIST experiments on …

Supervised learning reveals hidden eigenvalue patterns that clustering missed, testing k-NN and logistic regression on …

Learn how Coulomb matrices encode 3D molecular structure for machine learning from basic theory to Python implementation …

Learn about the Kabsch algorithm for optimal point alignment with implementations in NumPy, PyTorch, TensorFlow, and JAX …