Italyna

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.

Start here

Use AI as a thinking partner, not an authority. The goal is to become a stronger materials scientist: more prepared, more organized, more computationally fluent, and more careful about evidence.

Safety rules

Daily prompt patterns

Literature map:

Help me build a literature map for [public topic]. Organize it by mechanisms, materials systems, characterization methods, open questions, and search terms. Do not invent citations. Tell me what to verify.

Concept tutor:

Teach me [concept] at three levels: first-year engineering, materials science undergraduate, and graduate mechanism level. Then quiz me with five questions.

Experiment planner:

For a public materials question about [topic], propose a test matrix with variables, controls, measurements, failure modes, metadata fields, and likely sources of error.

Coding partner:

Write Python code to analyze a synthetic CSV with columns [columns]. Include a small fake dataset, clear units, and checks for missing values.

Hostile reviewer:

Review this public draft as a senior materials engineer. Find weak claims, missing controls, unclear assumptions, and places where I need better evidence.

A 30-day starter plan

Week 1: Build a public reading map on AI for materials science.

Week 2: Create one notebook using Materials Project or a synthetic dataset.

Week 3: Write one short article explaining a concept with a figure.

Week 4: Ask AI to critique the article and notebook, then revise both.

The professional sentence

I use AI to prepare, organize public information, write reproducible code, and pressure-test my understanding. I do not use it with protected data, and I verify claims against primary sources.