8 citations · 17 across the 7 of their papers we have counts for
7 papers
Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization
Lane E. Schultz, Benjamin Afflerbach, Paul M. Voyles +1
We have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties. We compare results for features derived from easy-to-c…
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…
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.…
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…