In a groundbreaking achievement, researchers from Google DeepMind and Lawrence Berkeley National Laboratory have introduced GNoME, an advanced AI system that has uncovered over 2 million potential materials for applications such as batteries, solar panels, and computer chips.
This scientific breakthrough was detailed in two papers recently published in the renowned journal Nature. The first paper delves into the scaling up of deep learning techniques employed by DeepMind researchers to empower GNoME, enabling it to explore material structures with unparalleled efficiency.
Within an astonishing 17-day period, GNoME identified a staggering 2.2 million potentially stable new inorganic crystal structures, with over 700 already experimentally validated—a nearly 10x increase compared to previously known stable inorganic crystals. The second paper outlines how GNoME’s predictions underwent rigorous testing using autonomous robotic systems at Berkeley Lab, resulting in a remarkable 71% success rate in synthesizing 41 out of 58 predicted compounds over continuous automated experiments spanning 17 days.
Continue reading… “GNoME: Google DeepMind and Lawrence Berkeley National Lab Unveil Revolutionary AI-Driven Materials Discovery”