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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

Graph Networks for Materials Exploration

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.

GNoME: Harnessing graph networks for materials exploration

GNoME uses two pipelines to discover low-energy (stable) materials. The structural pipeline creates candidates with structures similar to known crystals, while the compositional pipeline follows a more randomized approach based on chemical formulas. The outputs of both pipelines are evaluated using established Density Functional Theory calculations and those results are added to the GNoME database, informing the next round of active learning.

  • (GNN) model

    GNoME is a state-of-the-art graph neural network (GNN) model. The input data for GNNs take the form of a graph that can be likened to connections between atoms, which makes GNNs particularly suited to discovering new crystalline materials.

  • Active Learning!

    Used a training process called ‘active learning’ that dramatically boosted GNoME’s performance. GNoME would generate predictions for the structures of novel, stable crystals, which were then tested using DFT. The resulting high-quality training data was then fed back into the model training.

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Tokenomics

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  • Total Supply: 100,000,000 $GNOME

    Might change due to factors such as manual burning.

  • Tax: 0% buy & sell tax

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  • Liquidity: 100% Burnt

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Accelerating materials discovery with AI

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.

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