Quiver Quantitative - Google's (GOOGL) DeepMind, a subsidiary of Alphabet (NASDAQ:GOOGL) (GOOG), has made a groundbreaking discovery in material science using artificial intelligence (AI). The firm's AI has predicted the structure of over two million potential new materials, with nearly 400,000 of these designs being viable for production in laboratory conditions. This advancement, published in the journal Nature, holds immense potential for enhancing technologies in various sectors, including the development of more efficient batteries, solar panels, and computer chips.
The process of discovering and synthesizing new materials traditionally involves extensive research and significant investment, often spanning decades, as seen with the development of lithium-ion batteries. DeepMind's research scientist Ekin Dogus Cubuk expressed optimism that advancements in experimentation, autonomous synthesis, and AI models could drastically reduce this lengthy timeline. DeepMind's AI, trained on data from the Materials Project—an international collaboration founded at the Lawrence Berkeley National Laboratory—represents a leap forward in accelerating material discovery.
DeepMind plans to share its extensive data on these new materials with the broader research community, aiming to catalyze further advancements and practical applications. Kristin Persson, director of the Materials Project, highlighted the cautious approach of the industry towards new materials due to cost concerns. DeepMind's AI-driven discoveries could significantly shorten the time it takes for new materials to become cost-effective and commercially viable, marking a pivotal moment in material science.
Looking ahead, DeepMind's next objective is to use AI to predict the ease of synthesizing these materials in laboratory settings. This move could further streamline the development process, transforming the way new materials are discovered and brought to market. The implications of this research are vast, potentially leading to innovations that reshape industries and drive technological progress.
This article was originally published on Quiver Quantitative