3 citations · 3 across the 1 of their papers we have counts for
4 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…
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…
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…