11 citations · 35 across the 15 of their papers we have counts for
5 papers · 1 filter
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-…
Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent
Janghoon Ock, Radheesh Sharma Meda, Tirtha Vinchurkar +2
Adsorption energy is a key reactivity descriptor in catalysis. Determining adsorption energy requires evaluating numerous adsorbate-catalyst configurations, making it computational…
Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous Catalysis
Tirtha Vinchurkar, Janghoon Ock, Amir Barati Farimani
The increasing popularity of machine learning (ML) in catalysis has spurred interest in leveraging these techniques to enhance catalyst design. Our study aims to bridge the gap bet…
GradNav: Accelerated Exploration of Potential Energy Surfaces with Gradient-Based Navigation
Janghoon Ock, Parisa Mollaei, Amir Barati Farimani
The exploration of molecular systems' potential energy surface is important for comprehending their complex behaviors, particularly through identifying various metastable states. H…
Multimodal Language and Graph Learning of Adsorption Configuration in Catalysis
Janghoon Ock, Srivathsan Badrinarayanan, Rishikesh Magar +2
Adsorption energy is a reactivity descriptor that must be accurately predicted for effective machine learning (ML) application in catalyst screening. This process involves determin…