793 citations · 1.7k across the 34 of their papers we have counts for
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The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
Anuroop Sriram, Sihoon Choi, Xiaohan Yu +6
New methods for carbon dioxide removal are urgently needed to combat global climate change. Direct air capture (DAC) is an emerging technology to capture carbon dioxide directly fr…
Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials
Xiaoxiao Wang, Joseph Musielewicz, Richard Tran +6
Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput e…
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Nima Shoghi, Adeesh Kolluru, John R. Kitchin +3
Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has b…
Chemical Properties from Graph Neural Network-Predicted Electron Densities
Ethan M. Sunshine, Muhammed Shuaibi, Zachary W. Ulissi +1
According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this wo…
Beyond Independent Error Assumptions in Large GNN Atomistic Models
Janghoon Ock, Tian Tian, John Kitchin +1
The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and de…
WhereWulff: A semi-autonomous workflow for systematic catalyst surface reactivity under reaction conditions
Rohan Yuri Sanspeur, Javier Heras-Domingo, John R. Kitchin +1
This paper introduces WhereWulff, a semi-autonomous workflow for modeling the reactivity of catalyst surfaces. The workflow begins with a bulk optimization task that takes an initi…