Millions of new materials discovered with deep learning
Moderator: Redaktion
Millions of new materials discovered with deep learning
"AI tool GNoME finds 2.2 million new crystals, including 380,000 stable materials that could power future technologies
Modern technologies from computer chips and batteries to solar panels rely on inorganic crystals. To enable new technologies, crystals must be stable otherwise they can decompose, and behind each new, stable crystal can be months of painstaking experimentation.
Today, in a paper published in Nature, we share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
With GNoME, we’ve multiplied the number of technologically viable materials known to humanity. Of its 2.2 million predictions, 380,000 are the most stable, making them promising candidates for experimental synthesis. Among these candidates are materials that have the potential to develop future transformative technologies ranging from superconductors, powering supercomputers, and next-generation batteries to boost the efficiency of electric vehicles.
GNoME shows the potential of using AI to discover and develop new materials at scale. External researchers in labs around the world have independently created 736 of these new structures experimentally in concurrent work. In partnership with Google DeepMind, a team of researchers at the Lawrence Berkeley National Laboratory has also published a second paper in Nature that shows how our AI predictions can be leveraged for autonomous material synthesis.
We’ve made GNoME’s predictions available to the research community. We will be contributing 380,000 materials that we predict to be stable to the Materials Project, which is now processing the compounds and adding them into its online database. We hope these resources will drive forward research into inorganic crystals, and unlock the promise of machine learning tools as guides for experimentation"
https://deepmind.google/discover/blog/m ... -learning/