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Found 4 papers, 4 papers with code
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
The top 3 models are UIPs, the winning methodology for ML-guided materials discovery, achieving F1 scores of ~0. 6 for crystal stability classification and discovery acceleration factors (DAF) of up to 5x on the first 10k most stable predictions compared to dummy selection from our test set.
Materials Graph Transformer predicts the outcomes of inorganic reactions with reliable uncertainties
A common bottleneck for materials discovery is synthesis.
Computational Physics Materials Science
Coarse-graining and designing liquids by combining machine learning with liquid state theory
Pioneering work in liquid state theory derived analytical closures for the framework.
Soft Condensed Matter Computational Physics
Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost.
