153 citations · 228 across the 5 of their papers we have counts for
5 papers
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…
Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery
Adeesh Kolluru, Muhammed Shuaibi, Aini Palizhati +6
The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in inter…
Machine-learning accelerated geometry optimization in molecular simulation
Yilin Yang, Omar A. Jimenez-Negron, John R. Kitchin
Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathway…
Alloy surface segregation in reactive environments: A first-principles atomistic thermodynamics study of Ag3Pd(111) in oxygen atmospheres
John Kitchin, Karsten Reuter, Matthias Scheffler
We present a first-principles atomistic thermodynamics framework to describe the structure, composition and segregation profile of an alloy surface in contact with a (reactive) env…