Publications

Research papers, articles, and materials science explainers.

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to using AI around nuclear and national-lab-adjacent materials work without exposing proprietary, export-controlled, unpublished, or sensitive information.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

• Toughness Enhancement in Recycled Carbon Fiber Laminates

  This study investigates interlaminar fracture toughness of recycled carbon fiber composites through Mode I and Mode II testing. Surface treatments and sizing optimization increase GIC by 40% compared to untreated fibers.

• Energy Absorption in Gyroid Lattices: A Parametric Study

  Parametric investigation of gyroid lattice structures manufactured via selective laser melting. Cell size, wall thickness, and gradient strategies are systematically varied to optimize energy absorption under compression.

• Intro to SEM for Materials Characterization

  A practical guide to scanning electron microscopy for materials scientists and engineers. Covers sample preparation, imaging modes, and fracture surface interpretation.