What Radical AI does
Radical AI is an AI-for-science company focused on accelerating materials discovery by tightly integrating (1) AI that proposes materials and experiments, (2) simulation and learning components, and (3) a self-driving/robotic laboratory that executes experiments and feeds measured data back into the system. The company positions its approach as “closed-loop” scientific intelligence: scientists provide performance targets, AI-generated experiments run in the lab, and results are iteratively refined until goals are met.
Technically, Radical AI has published and open-sourced parts of its underlying stack, including TorchSim, a PyTorch-native atomistic simulation engine intended to speed atomistic workflows and support real-time correlation with lab measurements. In addition, it has described ML-centric simulation capabilities such as EGIP (a direct-force interatomic potential) as well as research tooling for extracting experimental “process + measurement” detail from scientific literature (LitXBench).
Commercially, the company states that it will “sell materials” to industries including energy, aerospace, defense, semiconductors, and automotive, and it has pursued U.S. defense R&D funding via AFWERX (Direct-to-Phase II SBIR/STTR). It also announced and expanded a major footprint in New York tied to building autonomous materials science labs at the Brooklyn Navy Yard, with government-backed tax credits and an expectation of high throughput of AI-driven experiments per day.