Cornell AI Screens Battery Electrolytes to Improve Storage
TL;DR. Cornell researchers developed IonNet, an AI framework predicting ion mobility to accelerate the discovery of better battery materials. - IonNet screens solid materials based on chemical composition, even without precise crystal structures. - The AI identified 87 fast-ion conductor candidates from 4,500 stable compounds, confirming 13 with simulations. - This method speeds up the search for safer, higher-performing energy storage solutions.
- Cornell University researchers used AI to accelerate battery material discovery.
- IonNet, an AI framework, predicts lithium ion movement through solid materials from chemical composition.
- The AI identified numerous fast-ion conductor candidates, reducing early-stage material evaluation time.
Sources
- AI and electrolyte engineering open new paths for better batteries — techxplore.com