8 citations · 18 across the 4 of their papers we have counts for
4 papers
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles
Tirtha Vinchurkar, Kareem Abdelmaqsoud, John R. Kitchin
Machine-learned potentials (MLPs) have revolutionized materials discovery by providing accurate and efficient predictions of molecular and material properties. Graph Neural Network…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…
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…