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Using Self-Correcting Search to Accelerate Materials Discovery

goodfire.ai · 1,839 words · saved by 8 readers

The critical technologies of our time, from clean energy to advanced computing to space flight, all depend on the discovery of new materials. But progress in materials science has not kept pace with advances in fields like software or biology, and it takes decades to discover and manufacture new materials. The problem is that scientists can only do so many experiments and analyses, and have historically had to do them sequentially in slow feedback loops before even beginning to think about industry applications. Radical AI is building a fully autonomous and intelligent approach to materials design, where AI models generate hypotheses, self-driving robotic labs execute experiments, and the entire stack learns and improves in a continuous closed loop that compresses decades of materials development into months. A critical step in this process is finding candidate materials: given the universe of possible materials, which should we create and test? Goodfire partnered with Radical AI to im

Using Self-Correcting Search to Accelerate Materials Discovery Research Using Self-Correcting Search to Accelerate Materials Discovery Authors Dron Hazra †* Adeesh Kolluru § Mark Bissell † Delia McGrath ‡ Daniel Balsam † Jorge Colindres ‡ Published April 1, 2026 † Goodfire ‡ Radical AI § Work performed while at Radical AI * Core contributor Introduction The critical technologies of our time, from clean energy to advanced computing to space flight, all depend on the discovery of new materials. But progress in materials science has not kept pace with advances in fields like software or biology,

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