214 citations · 278 across the 5 of their papers we have counts for
5 papers
Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability
Matteo Cargnelutti, Catherine Brobston, John Hess +8
Large language models (LLMs) use data to learn about the world in order to produce meaningful correlations and predictions. As such, the nature, scale, quality, and diversity of th…
Machine Learning Materials Properties with Accurate Predictions, Uncertainty Estimates, Domain Guidance, and Persistent Online Accessibility
Ryan Jacobs, Lane E. Schultz, Aristana Scourtas +7
One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materia…
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali +50
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To…
Benchmark tests of atom segmentation deep learning models with a consistent dataset
Jingrui Wei, Ben Blaiszik, Aristana Scourtas +2
The information content of atomic resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column,…
FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
Nikil Ravi, Pranshu Chaturvedi, E. A. Huerta +7
A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and…