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Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner +7
The rapid adoption of machine learning (ML) in domain sciences necessitates best practices and standardized benchmarking for performance evaluation. We present Matbench Discovery,…
Fair GANs through model rebalancing for extremely imbalanced class distributions
Anubhav Jain, Nasir Memon, Julian Togelius
Deep generative models require large amounts of training data. This often poses a problem as the collection of datasets can be expensive and difficult, in particular datasets that…
Zero-shot racially balanced dataset generation using an existing biased StyleGAN2
Anubhav Jain, Nasir Memon, Julian Togelius
Facial recognition systems have made significant strides thanks to data-heavy deep learning models, but these models rely on large privacy-sensitive datasets. Further, many of thes…
Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3
Nicholas Walker, John Dagdelen, Kevin Cruse +7
Although gold nanorods have been the subject of much research, the pathways for controlling their shape and thereby their optical properties remain largely heuristically understood…
Crystal Toolkit: A Web App Framework to Improve Usability and Accessibility of Materials Science Research Algorithms
Matthew Horton, Jimmy-Xuan Shen, Jordan Burns +21
Crystal Toolkit is an open source tool for viewing, analyzing and transforming crystal structures, molecules and other common forms of materials science data in an interactive way.…