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Real insight emerges when you know exactly what you’re approximating. Would you like this adapted for LinkedIn, Twitter, or a blog format?
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.
So next time you run a fast, production-level calculation, thank the awkward, unoptimized, 1990s-era ab initio code that proved the physics first.
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Real insight emerges when you know exactly what you’re approximating. Would you like this adapted for LinkedIn, Twitter, or a blog format?
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.
So next time you run a fast, production-level calculation, thank the awkward, unoptimized, 1990s-era ab initio code that proved the physics first.
