4 citations · 6 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Flexible and Scalable Deep Learning with MMLSpark
Mark Hamilton, Sudarshan Raghunathan, Akshaya Annavajhala +11
In this work we detail a novel open source library, called MMLSpark, that combines the flexible deep learning library Cognitive Toolkit, with the distributed computing framework Ap…