2 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
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-…
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…
Bridging scales with Machine Learning: From first principles statistical mechanics to continuum phase field computations to study order disorder transitions in LixCoO2
G. H. Teichert, S. Das, M. Faghih Shojaei +5
LixTMO2 (TM=Ni, Co, Mn) forms an important family of cathode materials for Li-ion batteries, whose performance is strongly governed by Li composition-dependent crystal structure an…