Foundations
Machine Learning for Materials Discovery
How graph neural networks screen millions of candidate crystals for stability and how machine-learning interatomic potentials approximate DFT cheaply enough to simulate them, plus why an in-silico "stable" material is not yet a real one.
intermediate · 8 min read · Premium
The rate-limiting step in finding a new inorganic material has never been imagination; it has been arithmetic. To know whether a hypothetical crystal will actually hold together, you compute its energy with density functional theory (DFT), and a single DFT relaxation of a modestly sized unit cell costs hours of CPU time. Searching a chemical space of millions of candidate compositions and structures that way is a supercomputer-decade problem. In 2023, DeepMind's GNoME project reported 2.2 million candidate crystals screened and 381,000 predicted to be stable, an order-of-magnitude expansion of the known stable inorganic set, achieved by putting a cheap neural surrogate in front of the expensive physics. That is the whole game: learn to predict what DFT would say, then only run DFT where the prediction is promising.
Two learned surrogates for two different costs
Materials machine learning replaces two distinct expensive calculations, and it helps to keep them separate.
Keep reading with Pro.
You're reading the preview. Unlock the full concept plus the library, study plans, the AI mentor, and daily emails.