116 citations · 152 across the 7 of their papers we have counts for
7 papers · 1 filter
AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis
Omar Allam, Brook Wander, SungYeon Kim +16
Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some lim…
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…
Crystal Toolkit: A Web App Framework to Improve Usability and Accessibility of Materials Science Research Algorithms
Matthew Horton, Jimmy-Xuan Shen, Jordan Burns +21
Crystal Toolkit is an open source tool for viewing, analyzing and transforming crystal structures, molecules and other common forms of materials science data in an interactive way.…
AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
Janice Lan, Aini Palizhati, Muhammed Shuaibi +6
Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is…
Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion
Brook Wander, Kirby Broderick, Zachary W. Ulissi
Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces…