2 code implementations • 28 Aug 2023 • Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner, Yuan Chiang, Bowen Deng, Alpha A. Lee, Anubhav Jain, Kristin A. Persson
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.
no code implementations • 26 Apr 2023 • Nicholas Walker, John Dagdelen, Kevin Cruse, SangHoon Lee, Samuel Gleason, Alexander Dunn, Gerbrand Ceder, A. Paul Alivisatos, Kristin A. Persson, Anubhav Jain
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.
1 code implementation • 8 Dec 2020 • Mingjian Wen, Samuel M. Blau, Evan Walter Clark Spotte-Smith, Shyam Dwaraknath, Kristin A. Persson
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.
1 code implementation • 25 Apr 2019 • Kyle Bystrom, Danny Broberg, Shyam Dwaraknath, Kristin A. Persson, Mark Asta
Significant progress has been made recently in the automation and standardization of ab initio point defect calculations.
Materials Science