Materials Lab explains computational and general materials science the way it actually works — the physics underneath, not the definitions on top. Every post is written for someone who wants to understand the mechanism, with extra depth tucked into “Going deeper” boxes if you want it and skippable if you don’t.
There is no single right order to read in. Pick the door that fits why you came.
If you are new to materials science
Start with structure. Almost everything else in the subject is downstream of how atoms are arranged, and these three posts build on each other in order.
- What Is a Unit Cell? — the smallest box that, repeated, gives you the whole crystal. Read this first; the rest assumes it.
- BCC vs FCC vs HCP — why the structure with the most slip systems is the one that shatters in the cold.
- Reading the Iron-Carbon Phase Diagram in 10 Minutes — the one diagram that explains most of what steel does.
If you want to start simulating
The usual failure mode is picking a method before understanding what question it answers. These go in order from “what is this” to “here is a simulation you can run tonight”.
- Density Functional Theory, Explained Without the Math
- DFT vs Molecular Dynamics: Which Should You Use? — a decision, not a ranking.
- Your First LAMMPS Simulation, Step by Step — with a real run, real numbers and a real energy drift.
If you are following machine learning for materials
Machine-learned potentials are the most consequential thing to happen to atomistic simulation in twenty years, and also the easiest to be quietly wrong with.
- What Is a Machine-Learned Interatomic Potential?
- How Accurate Are Machine Learning Potentials, Really? — benchmark numbers, and what they do not tell you.
If you are here for the engineering stories
Failures teach faster than successes, because in a failure the physics is no longer optional.
- The Liberty Ships That Broke in Half — how a welded hull, a cold sea and a body-centred cubic lattice sank ships without a torpedo.
If you want something practical today
- 8 Python Libraries Every Materials Researcher Should Know
- Annealing vs Normalizing vs Tempering — three heat treatments, three different jobs.
- Why Nanoparticles Break the Rules
How the site is organised
Four groups in the menu, each with its own archive:
- Materials — Fundamentals, Advanced Materials
- Computation — Simulation, Machine Learning, Toolkit & Careers
- Metals & Mining — Metallurgy, Mining & Extraction
- Series — Failures & History, Paper of the Week
What to expect
Two posts a week. Two rules I hold myself to, because they are what separates a useful science blog from a content farm:
- Every figure is either plotted from cited data or labelled as a schematic. There is no third category, and there are no decorative stock images of glowing atoms.
- No number without a source. If I cannot verify it, I say so rather than rounding it into a fact.
Paper of the Week is the standing series: one paper I have actually read, reviewed honestly — what it claims, what it shows, and whether those are the same thing.
Who writes this: see About. Corrections and collaboration enquiries are welcome — the Contact page has the details, and I would rather be corrected than be wrong in public.