A new AI tool called GNoME from Google’s DeepMind artificial intelligence lab has reportedly discovered and contributed nearly 380,000 new compounds to the Materials Project, the open-access database founded at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab).
The Graph Networks for Materials Exploration (GNoME), is an AI-powered deep learning tool and a state-of-the-art graph neural network (GNN) model. Originally trained with data on crystal structures and their stability, it is particularly suited to discovering new crystalline materials.
Why Is Finding New Crystalline Materials So Important?
As Google’s DeepMind says: “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.”
380,000 New Stable Materials Discovered
DeepMind reports that using its GNoME AI model, not only has it discovered 2.2 million new crystals (the equivalent to nearly 800 years’ worth of knowledge) but has identified 380,000 of these as being the most stable, making them promising candidates for experimental synthesis.
Faster And Cheaper Than Past Methods
As DeepMind has highlighted, the traditional methods of scientists searching for novel crystal structures have been adjusting known crystals or experimenting with new combinations of elements. These methods have proven to be an expensive, trial-and-error processes that could take months to deliver limited results. Using the GNoME AI model, therefore, has dramatically speeded up and reduced the cost of this process.
Work Already Under Way On The New Materials
Google says that researchers in labs around the world have already independently created 736 of the newly discovered structures as part of experimental work. Also, in partnership with Google DeepMind, researchers at the Lawrence Berkeley National Laboratory have published a paper showing how the AI discoveries can be leveraged for autonomous material synthesis.
What Does This Mean For Your Organisation?
Many essential modern technologies rely on a supply of stable inorganic crystals, e.g. for computer chips, batteries, and solar panels. However, up until now, old methods of finding these crystals have involved time-consuming and expensive trial-and error process. Having an AI tool like GNoME has dramatically increased the speed and efficiency of discovery by predicting the stability of new materials. In doing so, it has demonstrated the potential of using AI to discover and develop new materials.
This could mean that AI models (such as GNoME) have the potential to develop a range of future transformative technologies which could include superconductors, powering supercomputers, and next-generation batteries to boost the efficiency of electric vehicles. Also, Google DeepMind releasing its database of newly discovered crystals to the research community could reduce development times for these new transformative technologies.
This could benefit society and businesses (new opportunities and new industries) as well as contributing to achieving environmental targets and improving sustainability by accelerating the development green technologies.