🧭 Start Here

For those starting to learn machine learning and materials informatics systematically. Recommended order: big picture → mathematical foundations → first application.

1. Get the big picture of MLSupervised and unsupervised learning through evaluation — the core vocabulary and workflow at a glance.Introduction to Machine Learning →
2. Build the mathematical foundationCalculus, integration, and linear algebra — the prerequisites for every method, paired with Python.Calculus and Vector Analysis →
3. Step into applicationsThe mindset and workflow of data-driven materials development (materials informatics).Introduction to Materials Informatics → / MI Learning Roadmap →

From there, follow your interests into the Machine Learning Dojo or the Materials Science Fundamentals Dojo.

For those implementing data utilization in the process industry and on the factory floor. Anchored in runnable code, toward optimization and digital twins.

1. Foundations of process dataThe big picture of process informatics (PI) and how to work with real plant data.Introduction to Process Informatics →
2. Make optimization your toolFrom gradient descent to Bayesian optimization — practical condition search and hyperparameter tuning.Optimization for Machine Learning →
3. Move to the shop floorReproduce a real process with a digital twin and use it for monitoring, prediction, and optimization.Digital Twin →

More practice-oriented topics — quality, safety, scale-up — are gathered in the Process Informatics Dojo.

For those with a materials or chemistry background adopting the latest AI methods. Cover the basics quickly, then head straight to advanced topics.

1. Get the MI conventions downDescriptor design, model evaluation, and design of experiments — the essentials that connect directly to research.Introduction to Materials Informatics →
2. Dig into advanced methodsThe frontier of graph, sequence, and sequential optimization — start with whatever is closest to your goal.GNN Introduction / Transformers / Bayesian Optimization
3. Reinforce the fundamentalsCrystallography, thermodynamics, and physical properties — the materials-science basis needed for interpretation.Introduction to Materials Science →

If you already know the topic you need, full-text search is the fastest route.