DFT vs Molecular Dynamics: Which Should You Use?
One gives you electrons at zero kelvin, the other atoms in motion at temperature. A decision guide with the length scales, time scales and honest limits of each.
Category: Computation
How Accurate Are Machine Learning Potentials, Really?
Five top models differ by 11% on energy error and by 83% on thermal conductivity error. Which means the question has no single answer — here is what to ask…
8 Python Libraries Every Materials Researcher Should Know
The eight libraries that do the heavy lifting in computational materials science, what each is actually for, and the catch with every one of them.
Your First LAMMPS Simulation, Step by Step
Install LAMMPS, run a real melt simulation, and understand every line of the input script. A first molecular dynamics run you can finish in twenty minutes.
Density Functional Theory, Explained Without the Math
DFT predicts what materials do, from first principles, using electron density instead of wavefunctions. Here is how it works and what it gets wrong.
What Is a Machine-Learned Interatomic Potential?
An MLIP gives you near-DFT accuracy at close to classical-MD speed. Here is how these potentials work, where the speed comes from, and how they fail.