Graph Networks for Materials Exploration (GNoME)

GNoME, an AI tool, is accelerating the materials discovery using artificial intelligence (AI).

It is a state-of-the-art graph neural network (GNN) model that uses ‘active learning’ to enhance its performance, allowing it to predict the stability of new materials.

Launched by – Google DeepMind

Aim - To generate novel candidate crystals and to predict their stability.

Deep learning is a method of artificial intelligence (AI) where it is taught to process data in a way that is inspired by a human computer.

Working – GnoME can generate predictions for the structures of novel, stable crystals which were then tested, resulting in high-quality training data fed back into model training.

The final step in their approach exploits Density Functional Theory (DFT), a method to verify the stability of the new structures, which are then used as new training datasets for the tool.
The Materials Project is the original datasheet for GNoME.

The Materials Project is a multi-institution, multi-national endeavour to compute the properties of all inorganic materials and provide the data for every materials researcher free of charge.

Significance – It has boosted the precision rate for predicting materials stability from 50% to around 80%.
It increases the number of ‘stable materials’ by 10-fold including inorganic crystals that modern tech applications use.

It has published a list of 381,000 of the 2.2 million crystal structures that it predicts to be most stable.

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