A Python-Based Module for Exploring Machine Learning-Inspired Optimization Algorithms Using Quantum Chemistry

High Resolution Image Download MS PowerPoint Slide This Technology Report describes a Python-based teaching module for introducing function optimization in a chemically meaningful setting. The module combines the open-source PyNOF software with Jupyter notebooks so that students can implement, run, and compare classical and adaptive optimization algorithms while examining convergence behavior and the role of the learning rate. Hartree–Fock calculations provide an accessible entry point, Natural Orbital Functional theory extends the activities to a more demanding optimization landscape, and the dissociation of H 2 serves as an integrative example linking numerical choices to chemical interpretation. The report focuses on the technology, its intended classroom use, and its initial implementation with graduate students. All notebooks, source files, and supporting materials are openly available, making the module easy to adapt to a variety of computational and quantum chemistry courses.