3 papers
physics.comp-ph2026
Machine Learning Hamiltonians are Accurate Energy-Force Predictors
Seongsu Kim, Chanhui Lee, Yoonho Kim +7
Recently, machine learning Hamiltonian (MLH) models have gained traction as fast approximations of electronic structures such as orbitals and electron densities, while also enablin…
cs.LG2025
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction
Jaewan Lee, Changyoung Park, Hongjun Yang +3
Recent advancements in graph neural networks (GNNs) have significantly enhanced the prediction of material properties by modeling crystal structures as graphs. However, GNNs often…
cond-mat.mtrl-sci2024
Lattice Lingo: Effect of Textual Detail on Multimodal Learning for Property Prediction of Crystals
Mrigi Munjal, Jaewan Lee, Changyoung Park +1
Most prediction models for crystal properties employ a unimodal perspective, with graph-based representations, overlooking important non-local information that affects crystal prop…