59 citations · 130 across the 7 of their papers we have counts for
8 papers
Retrofitting a commercial RF induction generator into a computer-controlled, vacuum and gas integrated annealing system for reactive-metal grain growth
Sterling G. Baird, Ryan Weber, Christopher Nyborg +3
High-temperature vacuum annealing near a metal's melting point drives controlled grain growth, but it normally requires an expensive turn-key vacuum induction furnace. We retrofit…
From Prompt to Protocol: Fast Charging Batteries with Large Language Models
Ge Lei, Ferran Brosa Planella, Sterling G. Baird +1
Efficiently optimizing battery charging protocols is challenging because each evaluation is slow, costly, and non-differentiable. Many existing approaches address this difficulty b…
Honegumi: An Interface for Accelerating the Adoption of Bayesian Optimization in the Experimental Sciences
Sterling G. Baird, Andrew R. Falkowski, Taylor D. Sparks
Bayesian optimization (BO) has emerged as a powerful tool for guiding experimental design and decision-making in various scientific fields, including materials science, chemistry,…
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
What is missing in autonomous discovery: Open challenges for the community
Phillip M. Maffettone, Pascal Friederich, Sterling G. Baird +16
Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has give…
High-dimensional Bayesian Optimization of Hyperparameters for an Attention-based Network to Predict Materials Property: a Case Study on CrabNet using Ax and SAASBO
Sterling G. Baird, Marianne Liu, Taylor D. Sparks
Expensive-to-train deep learning models can benefit from an optimization of the hyperparameters that determine the model architecture. We optimize 23 hyperparameters of a materials…
Data-Driven Materials Discovery and Synthesis using Machine Learning Methods
Sterling G. Baird, Marianne Liu, Hasan M. Sayeed +1
Experimentally [1-38] and computationally [39-50] validated machine learning (ML) articles are sorted based on the size of the training data: 1-100, 101-10000, and 10000+ in a comp…
Five Degree-of-Freedom Property Interpolation of Arbitrary Grain Boundaries via Voronoi Fundamental Zone Octonion Framework
Sterling G. Baird, Eric R. Homer, David T. Fullwood +1
We introduce the Voronoi fundamental zone octonion interpolation framework for grain boundary (GB) structure-property models and surrogates. The VFZO framework offers an advantage…