| Catalysis-Hub. org, an open electronic structure database for surface reactions KT Winther, MJ Hoffmann, JR Boes, O Mamun, M Bajdich, T Bligaard Scientific data 6 (1), 75, 2019 | 215 | 2019 |
| Low-scaling algorithm for nudged elastic band calculations using a surrogate machine learning model JAG Torres, PC Jennings, MH Hansen, JR Boes, T Bligaard Physical review letters 122 (15), 156001, 2019 | 160 | 2019 |
| Alkaline electrolyte and Fe impurity effects on the performance and active-phase structure of NiOOH thin films for OER catalysis applications JD Michael, EL Demeter, SM Illes, Q Fan, JR Boes, JR Kitchin The Journal of Physical Chemistry C 119 (21), 11475-11481, 2015 | 125 | 2015 |
| High-throughput calculations of catalytic properties of bimetallic alloy surfaces O Mamun, KT Winther, JR Boes, T Bligaard Scientific data 6 (1), 76, 2019 | 98 | 2019 |
| Neural network and ReaxFF comparison for Au properties JR Boes, MC Groenenboom, JA Keith, JR Kitchin International Journal of Quantum Chemistry 116 (13), 979-987, 2016 | 87 | 2016 |
| Graph theory approach to high-throughput surface adsorption structure generation JR Boes, O Mamun, K Winther, T Bligaard The Journal of Physical Chemistry A 123 (11), 2281-2285, 2019 | 82 | 2019 |
| Neural network predictions of oxygen interactions on a dynamic Pd surface JR Boes, JR Kitchin Molecular Simulation 43 (5-6), 346-354, 2017 | 78 | 2017 |
| Modeling segregation on AuPd (111) surfaces with density functional theory and Monte Carlo simulations JR Boes, JR Kitchin The Journal of Physical Chemistry C 121 (6), 3479-3487, 2017 | 59 | 2017 |
| A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts O Mamun, KT Winther, JR Boes, T Bligaard npj Computational Materials 6 (1), 177, 2020 | 51 | 2020 |
| Estimating Bulk-Composition-Dependent H2 Adsorption Energies on CuxPd1–x Alloy (111) Surfaces JR Boes, G Gumuslu, JB Miller, AJ Gellman, JR Kitchin ACS Catalysis 5 (2), 1020-1026, 2015 | 38 | 2015 |
| An atomistic machine learning package for surface science and catalysis MH Hansen, JAG Torres, PC Jennings, Z Wang, JR Boes, OG Mamun, ... arXiv preprint arXiv:1904.00904, 2019 | 29 | 2019 |
| A density functional theory parameterised neural network model of zirconia C Wang, A Tharval, JR Kitchin Molecular Simulation 44 (8), 623-630, 2018 | 28 | 2018 |
| Correlation of Electronic Structure with Catalytic Activity: H2–D2 Exchange across CuxPd1–x Composition Space G Gumuslu, P Kondratyuk, JR Boes, B Morreale, JB Miller, JR Kitchin, ... ACS Catalysis 5 (5), 3137-3147, 2015 | 26 | 2015 |
| First-principles study of the Cu-Pd phase diagram F Geng, JR Boes, JR Kitchin Calphad 56, 224-229, 2017 | 17 | 2017 |
| Core level shifts in Cu–Pd alloys as a function of bulk composition and structure JR Boes, P Kondratyuk, C Yin, JB Miller, AJ Gellman, JR Kitchin Surface Science 640, 127-132, 2015 | 17 | 2015 |
| CO2 electrocatalyst design using graph theory Z Wang, Y Li, J Boes, Y Wang, E Sargent | 8 | 2020 |
| An Atomistic Machine Learning Package for Surface Science and Catalysis M Hangaard Hansen, JA Garrido Torres, PC Jennings, Z Wang, JR Boes, ... arXiv e-prints, arXiv: 1904.00904, 2019 | | 2019 |
| Catalysis-hub. org: An open electronic structure database for surface reactions and catalytic materials K Winther, M Hoffmann, O Mamun, J Boes, M Bajdich, T Bligaard ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 257, 2019 | | 2019 |
| Catkit: Symmetry Methods for Automated Generation of Catalytic Structures JR Boes, T Bligaard 2018 AIChE Annual Meeting, 2018 | | 2018 |
| Catalysis Informatics: Accelerating Search and Discovery of New Catalysts JR Boes 2018 AIChE Annual Meeting, 2018 | | 2018 |
