How Ångstrom (YC S24) used Claude Code to train a model that beat Meta's UMA-OMC
Ångstrom AI (YC S24), with the University of Cambridge (the Csanyi group) and AstraZeneca, released DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials. The paper presented CSP-MACE-Å, a machine learning model designed to replace DFT, the expensive quantum mechanical calculation at the heart of crystal structure prediction, with the same accuracy but a 10,000x speedup.
CSP-MACE-Å also significantly outperformed UMA-OMC on crystal-structure prediction benchmarks. UMA is Meta's general purpose model for atoms and molecules; UMA-OMC is the version adapted for organic molecular crystals.
Ångstrom built CSP-MACE-Å on anycloud, a CLI that runs GPU jobs across your own cloud accounts. Ångstrom pointed Claude Code at anycloud: the agent called the anycloud CLI to drive the experiment loop, roughly 100,000 GPU jobs, almost entirely on multi-cloud spot, on their own cloud accounts.