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Graph Networks for Materials Exploration (GNoME)
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Google DeepMind
Relevant to AI safety discussions around advanced AI capabilities and dual-use science acceleration; illustrates how frontier AI systems are rapidly expanding scientific discovery beyond human-pace research, raising questions about oversight of AI-driven scientific breakthroughs.
Metadata
Importance: 45/100blog postnews
Summary
DeepMind's Graph Networks for Materials Exploration (GNoME) used deep learning to discover 2.2 million new stable crystal structures, vastly expanding the known catalog of stable inorganic materials. The system demonstrates how AI can accelerate scientific discovery by predicting material stability and properties at scale. This work parallels AlphaFold's impact on biology but applied to materials science.
Key Points
- •GNoME discovered 2.2 million new stable crystal structures, including 380,000 materials deemed most promising for experimental synthesis.
- •The system uses graph neural networks to predict the stability of novel inorganic crystal structures at unprecedented scale.
- •Results represent a ~45x increase in AI-discovered stable materials compared to prior databases, dramatically expanding the materials design space.
- •Autonomous robotic lab experiments validated GNoME predictions, synthesizing 41 novel materials, demonstrating real-world applicability.
- •Potential applications include better batteries, solar cells, and superconductors, showing AI's transformative role in materials science.
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| Page | Type | Quality |
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| Scientific Research Capabilities | Capability | 68.0 |
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November 29, 2023
Science
# Millions of new materials discovered with deep learning
Amil Merchant and Ekin Dogus Cubuk
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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](https://www.nature.com/articles/s41586-023-06735-9), 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](https://www.nature.com/articles/s41586-023-06734-w) that shows how our AI predictions can be leveraged for autonomous material synthesis.
We’ve made [GNoME’s predictions available](https://www.nature.com/articles/s41586-023-06735-9) 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](https://next-gen.materialsproject.org/). We hope these resources will drive forward research into inorganic crystals, and unlock the promise of machine learning tools as guides for experimentation
## Accelerating materials discovery with AI
About 20,000 of the crystals experimentally identified in the ICSD database are computationally stable. Computational appr
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