354 citations · 413 across the 16 of their papers we have counts for
16 papers
Effects of Yttrium Doping on Oxygen Conductivity in Ba(Fe, Co, Zr, Y)O_{3-δ} Cathode Materials for Proton Ceramic Fuel Cells
Chiyoung Kim, Ryan Jacobs, Jack H. Duffy +3
Proton ceramic fuel cells (PCFCs) achieve high efficiency at reduced operating temperatures, but their performance is often limited by slow oxygen reduction reaction (ORR) kinetics…
Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forests
Ni Li, Ryan Jacobs, Matthew Lynch +3
Quantifying prediction uncertainty when applying object detection models to new, unlabeled datasets is critical in applied machine learning. This study introduces an approach to es…
Regression with Large Language Models for Materials and Molecular Property Prediction
Ryan Jacobs, Maciej P. Polak, Lane E. Schultz +3
We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviation from the conventional LLM use case.…
Accelerating Domain-Aware Electron Microscopy Analysis Using Deep Learning Models with Synthetic Data and Image-Wide Confidence Scoring
Matthew J. Lynch, Ryan Jacobs, Gabriella Bruno +3
The integration of machine learning (ML) models enhances the efficiency, affordability, and reliability of feature detection in microscopy, yet their development and applicability…
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…
Ultra-fast Oxygen Conduction in Sillén Oxychlorides
Jun Meng, Md Sariful Sheikh, Lane E. Schultz +5
Oxygen ion conductors are crucial for enhancing the efficiency of various clean energy technologies, including fuel cells, batteries, electrolyzers, membranes, sensors, and more. I…
A General Approach for Determining Applicability Domain of Machine Learning Models
Lane E. Schultz, Yiqi Wang, Ryan Jacobs +1
Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions. In this work, we develop a new and general ap…
Accelerating Ensemble Error Bar Prediction with Single Models Fits
Vidit Agrawal, Shixin Zhang, Lane E. Schultz +1
Ensemble models can be used to estimate prediction uncertainties in machine learning models. However, an ensemble of N models is approximately N times more computationally demandin…
Direct evidence of low work function on SrVO cathode using thermionic electron emission microscopy and high-field ultraviolet photoemission spectroscopy
Md Sariful Sheikh, Lin Lin, Ryan Jacobs +5
Perovskite SrVO has recently been proposed as a novel electron emission cathode material. Density functional theory (DFT) calculations suggest multiple low work function surfac…
Machine Learning Design of Perovskite Catalytic Properties
Ryan Jacobs, Jian Liu, Harry Abernathy +1
Discovering new materials that efficiently catalyze the oxygen reduction and evolution reactions is critical for facilitating the widespread adoption of solid oxide fuel cell and e…
A Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties
Ryan Jacobs, Jian Liu, Harry Abernathy +1
The discovery and design of new materials which can efficiently catalyze the oxygen reduction and evolution reactions at reduced temperatures is important for facilitating the wide…
Time-dependence of SrVO thermionic electron emission properties
Md Sariful Sheikh, Ryan Jacobs, Dane Morgan +1
Thermionic electron emission cathodes are critical components of various high power and high frequency vacuum electronic devices, electron microscopes, e-beam lithographic devices,…
Role of Multifidelity Data in Sequential Active Learning Materials Discovery Campaigns: Case Study of Electronic Bandgap
Ryan Jacobs, Philip E. Goins, Dane Morgan
Materials discovery and design typically proceeds through iterative evaluation (both experimental and computational) to obtain data, generally targeting improvement of one or more…
Predictions and Uncertainty Estimates of Reactor Pressure Vessel Steel Embrittlement Using Machine Learning
Ryan Jacobs, Takuya Yamamoto, G. Robert Odette +1
An essential aspect of extending safe operation of the active nuclear reactors is understanding and predicting the embrittlement that occurs in the steels that make up the Reactor…
Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering
Maciej P. Polak, Dane Morgan
There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and…
Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models
Maciej P. Polak, Shrey Modi, Anna Latosinska +5
Accurate and comprehensive material databases extracted from research papers are crucial for materials science and engineering, but their development requires significant human eff…