Ab Initio Versions [better] May 2026

We talk a lot about machine learning potentials, DFT surrogates, and foundation models for materials. But here’s a quiet truth: every new, truly predictive method still starts with an ab initio version.

The first implementation of a theory — no experimental fitting, no empirical parameters, just fundamental constants and equations. ab initio versions

So next time you run a fast, production-level calculation, thank the awkward, unoptimized, 1990s-era ab initio code that proved the physics first. We talk a lot about machine learning potentials,

And if you’re building something new — start with the ab initio version. Even if it only runs on 10 atoms. So next time you run a fast, production-level

Real insight emerges when you know exactly what you’re approximating. Would you like this adapted for LinkedIn, Twitter, or a blog format?

Here’s a draft for an interesting post about ab initio versions — tailored for a computational chemistry, materials science, or ML/physics audience. Why “Ab Initio” Versions Still Matter in an AI-Driven World