Taylor Sparks

Oct 31, 2024 | nanohub.org | Taylor Sparks

0 users 0 questions (Ask a question) 0 review(s) (Review this) Abstract In this seminar, Dr. Sparks presents an innovative online platform for teaching materials informatics, designed to seamlessly integrate advanced computational tools with an accessible user experience.

Sep 25, 2024 | nanohub.org | Taylor Sparks

Materials Informatics: Data, Algorithms, and Deployment from Property Prediction to Large Language Models Launch Tool Version 1.0 - published on 26 Sep 2024 doi:10.21981/CV4Y-SZ16 cite this Open source: license | download View All Supporting Documents 29 users, detailed usage 0 Citation(s) 0 questions (Ask a question) 0 review(s) (Review this) 0 wish(es) (New Wish) Published on 26 Sep 2024 Abstract This tool includes a series of notebooks that accompany the course "Materials Informatics:...

Aug 13, 2024 | nanohub.org | Taylor Sparks

Welcome back to our Materials Informatics series! In today's episode, we delve into Bayesian Optimization, a critical tool for incrementally improving processes and designs in materials research. Bayesian Optimization leverages Bayes' theorem to make informed decisions with minimal data, making it particularly valuable in material science, where property trade-offs are common.

Aug 13, 2024 | nanohub.org | Taylor Sparks

Welcome back to our Materials Informatics playlist! In today's episode, we explore the transformative impact of Large Language Models (LLMs) on materials science. David Sparks from the University of Utah's Materials Science and Engineering department takes us through the exciting possibilities that LLMs bring to the field.

Aug 13, 2024 | nanohub.org | Taylor Sparks

Welcome back to our Materials Informatics series! In this video, we continue exploring Bayesian and probabilistic machine learning by diving deeper into Naive Bayes classification. In this video, we cover:A brief recap of Naive Bayes, highlighting its simplicity and the assumption of feature independence. A practical demonstration of Naive Bayes using materials project data to classify whether a material is a metal or an insulator.