51 citations · 56 across the 5 of their papers we have counts for
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Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
Jiayu Peng, James Damewood, Jessica Karaguesian +2
Graph convolutional neural networks (GCNNs) have become a machine learning workhorse for screening the chemical space of crystalline materials in fields such as catalysis and energ…
Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with machine learning
Jaclyn R. Lunger, Jessica Karaguesian, Hoje Chun +6
Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to prod…
Machine-learning-accelerated simulations to enable automatic surface reconstruction
Xiaochen Du, James K. Damewood, Jaclyn R. Lunger +4
Understanding material surfaces and interfaces is vital in applications like catalysis or electronics. By combining energies from electronic structure with statistical mechanics, a…
Representations of Materials for Machine Learning
James Damewood, Jessica Karaguesian, Jaclyn R. Lunger +4
High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composi…
Stern Layers on Surfaces of RuO2(100), RuO2(110), and Pt(111): Surface X-ray Scattering Studies
Tomoya Kawaguchi, Reshma R. Rao, Jaclyn R. Lunger +6
Surface X-ray scattering studies of electrochemical Stern layer are reported. The Stern layers formed at the interfaces of RuO2 (110) and (100) in 0.1 M CsF electrolyte are compare…