AI for Materials Science
A public field guide for using AI in materials science while preserving verification, confidentiality, and scientific judgment.
AI Materials Publications
- How a Materials Scientist Can Collaborate With AI Without Outsourcing Judgment
A practical position paper on using AI as a research partner in materials science: strong for literature mapping, coding, data analysis, and explanation, but never a substitute for scientific judgment, verification, or ethics.
- The Materials Scientist's AI Lab Partner
A practical guide to using AI across the materials science workflow, from literature review and hypothesis generation to data cleanup, plotting, coding, and presentation prep.
- AI Will Not Replace Materials Scientists. It Will Change What the Best Ones Do.
The real shift is not replacement. It is a change in leverage: better search, better modeling, better experiment selection, and higher expectations for verification.
- Defects Are Features: AI, Neutron Scattering, and the Future of Materials Characterization
A student-friendly explanation of why defects matter, why characterization is hard, and how AI can help connect scattering signals to atomic-scale disorder.
- From Trial-and-Error to Active Learning: How AI Chooses the Next Experiment
A clear introduction to active learning, Bayesian optimization, and autonomous experimentation for materials research.
- A Beginner's Guide to the Materials Project for Materials Engineers
How to think about the Materials Project as a public computational resource for learning, screening, and building AI-ready materials intuition.
- AI in Nuclear Materials: What Can Be Public, What Must Stay Protected
A careful, public-facing guide to using AI around nuclear and national-lab-adjacent materials work without exposing proprietary, export-controlled, unpublished, or sensitive information.
- Polymer Crystallinity Meets Machine Learning
How AI can help organize the messy relationship among processing history, crystallinity, morphology, and mechanical behavior in semicrystalline polymers.
- Additive Manufacturing Lattices and Surrogate Models
A practical explanation of how surrogate models can accelerate design exploration for lattice structures, energy absorption, and manufacturing-aware optimization.
- Can AI Help Design Safer, Longer-Lived Materials?
A broad, serious look at AI's role in durability, failure prediction, qualification, uncertainty, and the engineering responsibility behind materials decisions.
- AI as Research Notebook, Tutor, Coding Partner, and Critic
A practical operating model for using AI day-to-day while studying and practicing materials science.
- GNoME, MatterGen, and the Reality Check Behind AI Materials Discovery
A grounded explanation of modern AI materials discovery: predicted candidates are exciting, but synthesis, processing, characterization, and validation still decide what becomes real.
AI Materials Modules
- AI Collaboration Playbook for Materials Scientists
A practical prompt library and safety workflow for using AI to study materials science, prepare for technical work, write code, and communicate research without exposing protected information.