at Google ScholarTitle data
Jung, Hyunwook ; Sauerland, Lena ; Stocker, Sina ; Reuter, Karsten ; Margraf, Johannes T.:
Machine-Learning Driven Global Optimization of Surface Adsorbate Geometries.
In: npj Computational Materials.
Vol. 9
(2023)
.
- 114.
ISSN 2057-3960
DOI: https://doi.org/10.1038/s41524-023-01065-w
Abstract in another language
The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in computational catalysis research. For the relatively large reaction intermediates frequently encountered, e.g., in syngas conversion, a multitude of possible binding motifs leads to complex potential energy surfaces (PES), however. This implies that finding the optimal structure is a difficult global optimization problem, which leads to significant uncertainty about the stability of many intermediates. To tackle this issue, we present a global optimization protocol for surface adsorbate geometries which trains a surrogate machine learning potential on-the-fly. The approach is applicable to arbitrary surface models and adsorbates and minimizes both human intervention and the number of required DFT calculations by iteratively updating the training set with configurations explored by the algorithm. We demonstrate the efficiency of this approach for a diverse set of adsorbates on the Rh(111) and (211) surfaces.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Institutions of the University: | Faculties Faculties > Faculty of Biology, Chemistry and Earth Sciences Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning > Chair Physical Chemistry V - Theory and Machine Learning - Univ.-Prof. Dr. Johannes Theo Margraf Research Institutions > Central research institutes > Research Center for AI in Science and Society |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 540 Chemistry |
| Date Deposited: | 13 Nov 2023 12:43 |
| Last Modified: | 04 Nov 2025 09:50 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/87662 |