8 citations · 8 across the 2 of their papers we have counts for
8 papers · 1 filter
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.…
Kolmogorov-Arnold Networks Applied to Materials Property Prediction
Ryan Jacobs, Lane E. Schultz, Dane Morgan
Kolmogorov-Arnold Networks (KANs) were proposed as an alternative to traditional neural network architectures based on multilayer perceptrons (MLP-NNs). The potential advantages of…
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…
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…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
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…