[KongchangAI]
ModelGraph Networks for Materials Exploration

GNoME

A graph neural network-based AI materials discovery model developed by DeepMind, capable of predicting hundreds of thousands of potentially stable material structures.

Core Facts

Timeline (last 90 days)

Oct 12

GNoME is a project published by Google DeepMind in Nature in 2023 that uses graph neural networks to predict the stability of inorganic crystal structures, producing about 2.2 million new crystal structures at once, of which about 380,000 were assessed as stable candidates.

Unverified50%
Sep 16

GNoME (Graph Networks for Materials Exploration), released by DeepMind, predicts crystal structure stability through graph neural networks and screened out millions of potential new material candidates at once.

Unverified50%
Sep 15

Google DeepMind's GNoME model predicted 2.2 million new crystal structures in 2023.

Unverified50%
Sep 15

Of the 2.2 million crystal structures predicted by GNoME, approximately 380,000 are considered to have stability and may become candidate materials for next-generation batteries, semiconductors, or superconductors.

Unverified50%
Sep 8

The GNoME project discovered 2.2 million new stable crystal structures in the field of materials science.

Unverified50%
Aug 26

GNoME项目采用主动学习策略,其38万种预测稳定的新材料中已有数百种被独立实验室合成验证

Unverified50%
Aug 26

GNoME(Graph Networks for Materials Exploration)使用图神经网络(GNN)来预测晶体结构的稳定性,核心指标是材料在凸包上的分解能

Unverified50%

All Facts (9)

Verified

AlphaFold是DeepMind于2020年推出的蛋白质结构预测系统

90%
Verified

DeepMind's GNoME project predicted approximately 2.2 million potentially stable crystal structures, about an order of magnitude more than the stable materials previously known to humans.

65%
Unverified

GNoME is a project published by Google DeepMind in Nature in 2023 that uses graph neural networks to predict the stability of inorganic crystal structures, producing about 2.2 million new crystal structures at once, of which about 380,000 were assessed as stable candidates.

50%
Unverified

GNoME (Graph Networks for Materials Exploration), released by DeepMind, predicts crystal structure stability through graph neural networks and screened out millions of potential new material candidates at once.

50%
Unverified

Google DeepMind's GNoME model predicted 2.2 million new crystal structures in 2023.

50%
Unverified

Of the 2.2 million crystal structures predicted by GNoME, approximately 380,000 are considered to have stability and may become candidate materials for next-generation batteries, semiconductors, or superconductors.

50%
Unverified

The GNoME project discovered 2.2 million new stable crystal structures in the field of materials science.

50%
Unverified

GNoME项目采用主动学习策略,其38万种预测稳定的新材料中已有数百种被独立实验室合成验证

50%
Unverified

GNoME(Graph Networks for Materials Exploration)使用图神经网络(GNN)来预测晶体结构的稳定性,核心指标是材料在凸包上的分解能

50%

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