6 papers
Spectra-Scope : A toolkit for automated and interpretable characterization of material properties from spectral data
Amalya C. Johnson, Chris Fajardo, Leena Sansguiri +2
Spectroscopy is a central pillar of materials characterization, providing useful information on properties like structure, composition, or excited state dynamics of a system. Howev…
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-…
De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models
Zhenze Yang, Weike Ye, Xiangyun Lei +3
Solid polymer electrolytes hold significant promise as materials for next-generation batteries due to their superior safety performance, enhanced specific energy, and extended life…
A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model
Arash Khajeh, Xiangyun Lei, Weike Ye +3
In this work, we introduce a polymer discovery platform to efficiently design polymers with tailored properties, exemplified by the discovery of high-performance polymer electrolyt…
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…