22 citations · 59 across the 16 of their papers we have counts for
9 papers · 1 filter
Fine-tuning universal machine learning potentials for transition state search in surface catalysis
Raffaele Cheula, Mie Andersen, John R. Kitchin
Determining transition states (TSs) of surface reactions is central to understanding and designing heterogeneous catalysts but remains computationally prohibitive with density func…
Computational Design of Ductile Additively Manufactured Tungsten-Based Refractory Alloys
Kareem Abdelmaqsoud, Daniel Sinclair, Venkata Satya Surya Amaranth Karra +4
Tungsten exhibits exceptional temperature and radiation resistance, making it well-suited for applications in extreme environments such as nuclear fusion reactors. Additive manufac…
Electronic structure and elasticity of the Ta-W solid solution
Kareem Abdelmaqsoud, John R. Kitchin, Michael Widom
The brittleness or ductility of metals has long been attributed to their elastic constants, with high Poisson ratio, or equivalently high Pugh ratio, favoring greater ductility. Gr…
The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
Anuroop Sriram, Logan M. Brabson, Xiaohan Yu +12
Identifying useful sorbent materials for direct air capture (DAC) from humid air remains a challenge. We present the Open DAC 2025 (ODAC25) dataset, a significant expansion and imp…
Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models
Jehad Abed, Jiheon Kim, Muhammed Shuaibi +17
The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Disco…
Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis
Brook Wander, Joseph Musielewicz, Raffaele Cheula +1
Access to the potential energy Hessian enables determination of the Gibbs free energy, and certain approaches to transition state search and optimization. Here, we demonstrate that…
CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Brook Wander, Muhammed Shuaibi, John R. Kitchin +2
Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural networ…
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…
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…