AI and machine learning
How machine learning is used on materials data in practice: predicting properties from structure, graph neural networks, physics-informed models and AI agents, including where the models quietly break.
Related: Hands-on tutorials · Battery electrode agent
Claude Skills for materials science: what actually exists right now
What Claude Skills are, how they differ from MCP, and how to install three real materials science skills available today.
MCP and the Materials Project: a doorway for AI to materials data
What the Model Context Protocol is, why it matters for materials science, the Materials Project MCP servers that already exist, and how to build one yourself.
Materials informatics: a practical guide
What materials informatics is, how it turns data into material property predictions, the tools and databases behind it, and where it quietly breaks.
AI for materials: how it actually works
AI for materials discovery, explained: property prediction, generative design, and machine-learning potentials, plus the honest limits of each.
Graph neural networks for materials, explained
Composition-based models cannot tell polymorphs apart. A graph neural network that reads crystal structure finally can. How GNNs work for materials.
Physics-informed neural networks, explained
A network recovered a full cooling curve from six noisy points, refused to predict the impossible, and read a physical constant from the data. How PINNs work.
Six ways to put physics into machine learning
Ordinary machine learning learns only from data. Scientific ML also respects physics. Here are the six places to inject it, one use case each.
ML on the Materials Project, part 7: knowing what you don't know
A Gaussian Process predicts a value and an honest error bar. We audited whether the bars can be trusted, and found the model measured its own noise floor.
ML on the Materials Project, part 8: opening the box
The series finale: which features does the model actually use, and did it learn real chemistry? Yes, worn as a proxy nobody expected.
ML on the Materials Project, part 5: the map of chemical space
Unsupervised learning turns 26,566 materials into a 2D map with no labels, finds groups nobody defined, and shows why data's folds rarely match your question.
ML on the Materials Project, part 6: the statistical auditor
An Isolation Forest given zero rules flags the dataset's strangest entries on geometry alone, including a formation energy twenty times past any sane threshold.
ML on the Materials Project, part 3: is it even a metal?
Same features, a different question. Classification exposes the accuracy trap live, and asks why metallicity is hard to predict from composition.
ML on the Materials Project, part 4: were we lying to ourselves?
Grouped cross-validation measures how much a model's score was flattered by polymorph twins leaking between train and test. The answer: thirty percent.
ML on the Materials Project, part 2: trees, and overfitting caught
Same data, one change of model, and R2 jumps from 0.095 to 0.637. Plus the train-versus-test gap that shows memorization as a number.
ML on the Materials Project, part 1: the honest floor
Fetching 26,568 real oxides, turning formulas into numbers a model can learn from, and why a first score of R2 = 0.095 is a good start.
Why AI-predicted materials rarely get made
Models have predicted millions of new materials, yet only hundreds exist. Understanding the gap between a predicted structure and a real one.