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Found 4 papers, 3 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.
Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3
To that end, we present an approach using the powerful GPT-3 language model to extract structured multi-step seed-mediated growth procedures and outcomes for gold nanorods from unstructured scientific text.
BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules
Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge.
Pawpyseed: Perturbation-extrapolation band shifting corrections for point defect calculations
Significant progress has been made recently in the automation and standardization of ab initio point defect calculations.
Materials Science
