Italyna

Investigating layup strategies, tensile testing, and process variables toward circular high-strength components using recycled carbon fiber materials.

Design-print-test loop for energy absorption and weight reduction using architected lattices in additive manufacturing.

Turning electron microscopy imagery into stakeholder-ready visuals and quantitative fracture mechanics insights.

Polarized light microscopy techniques for characterizing semi-crystalline polymer morphology and processing effects.

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.

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.

The real shift is not replacement. It is a change in leverage: better search, better modeling, better experiment selection, and higher expectations for verification.

A student-friendly explanation of why defects matter, why characterization is hard, and how AI can help connect scattering signals to atomic-scale disorder.

A clear introduction to active learning, Bayesian optimization, and autonomous experimentation for materials research.

How to think about the Materials Project as a public computational resource for learning, screening, and building AI-ready materials intuition.

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to using AI around nuclear and national-lab-adjacent materials work without exposing proprietary, export-controlled, unpublished, or sensitive information.

How AI can help organize the messy relationship among processing history, crystallinity, morphology, and mechanical behavior in semicrystalline polymers.

A practical explanation of how surrogate models can accelerate design exploration for lattice structures, energy absorption, and manufacturing-aware optimization.

A broad, serious look at AI's role in durability, failure prediction, qualification, uncertainty, and the engineering responsibility behind materials decisions.

A practical operating model for using AI day-to-day while studying and practicing materials science.

A grounded explanation of modern AI materials discovery: predicted candidates are exciting, but synthesis, processing, characterization, and validation still decide what becomes real.

Understanding grain boundaries, their formation, and impact on mechanical properties. Includes SEM imagery and crystallographic concepts.

Interactive tutorial on reading and interpreting binary phase diagrams. Essential for materials selection and processing.

Clear explanation of these often-confused mechanical properties with real-world examples and visual comparisons.

Deep dive into fracture mechanics with interactive simulations, crack propagation models, and failure prediction tools.

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.

Hands-on tutorial for optimizing fiber orientation and stacking sequences in laminated composites.

Rapid scanning of scientific literature to identify state-of-the-art materials, manufacturing processes, and design opportunities. Deliverables include annotated bibliographies and feasibility assessments.

Data-driven materials selection using Ashby charts, performance indices, and multi-criteria decision matrices. Ideal for early-stage design and optimization.

Design for additive manufacturing including topology optimization, lattice structures, and process parameter selection. CAD deliverables ready for printing.

Structured test planning for mechanical characterization, durability validation, and prototype evaluation. Includes DOE strategies and statistical analysis plans.

Translating complex technical findings into stakeholder-ready decks, posters, and visual narratives. Publication-quality figures and infographics.

Public-source AI workflows for literature mapping, materials data notebooks, experiment planning, and technical communication. Designed for teams that need speed without losing scientific rigor.