3 papers
cs.LG2025
XxaCT-NN: Structure Agnostic Multimodal Learning for Materials Science
Jithendaraa Subramanian, Linda Hung, Daniel Schweigert +2
Recent advances in materials discovery have been driven by structure-based models, particularly those using crystal graphs. While effective for computational datasets, these models…
cs.LG2024
UniMat: Unifying Materials Embeddings through Multi-modal Learning
Janghoon Ock, Joseph Montoya, Daniel Schweigert +3
Materials science datasets are inherently heterogeneous and are available in different modalities such as characterization spectra, atomic structures, microscopic images, and text-…
physics.chem-ph2023
The Role of Reference Points in Machine-Learned Atomistic Simulation Models
Xiangyun Lei, Weike Ye, Joseph Montoya +3
This paper introduces the Chemical Environment Modeling Theory (CEMT), a novel, generalized framework designed to overcome the limitations inherent in traditional atom-centered Mac…