2026-08-24T08:42:39Zhttps://www.radar-service.eu/oai/OAIHandler10.35097/njvtj906ggr9arf82026-06-25T08:36:35Zradar4kit
10.35097/njvtj906ggr9arf8
Sireci, Enrico
Enrico
Sireci
0000-0002-3253-6518
Institut für Katalyseforschung und -technologie (IKFT), Karlsruher Institut für Technologie (KIT)
Sharapa, Dmitry I.
Dmitry I.
Sharapa
0000-0001-9510-9081
Institut für Katalyseforschung und -technologie (IKFT), Karlsruher Institut für Technologie (KIT)
Studt, Felix
Felix
Studt
Institut für Katalyseforschung und -technologie (IKFT), Karlsruher Institut für Technologie (KIT)
Dataset for Publication "Elucidating Mn Promoter Structures and Stability on Co Nanoparticles through Machine Learning Potential-powered Genetic Algorithm"
Karlsruhe Institute of Technology
2026
2026
Engineering
DFT
Machine–Learning Potential
Genetic Algorithm
Fischer-Tropsch
Cobalt Nanoparticles
Mn Promotion
Open Access
Creative Commons Attribution 4.0 International
Sireci, Enrico
0000-0002-3253-6518
Sharapa, Dmitry I.
0000-0001-9510-9081
Studt, Felix
Dataset including genetic algorithm scripts, fine-tuned CHGNet in .tar format, global minima structures in .xyz format, plots (.png) showing algorithm progression (Energy vs. # of candidates), database files in .db format containing all relaxed candidates for all genetic algorithm runs, training dataset both in .db and .json format and structural files of the MnxOyHz motifs on different fcc-Co surface conformations extracted from global minima structures
The folders 6_nm_fcc, 8_nm_fcc, and 8_nm_hcp contain subdirectories corresponding to the investigated Mn phases. Each subdirectory includes plots illustrating the genetic algorithm convergence (e.g., energy vs. candidate) and the structural files (.xyz) of the identified global minima.
The "trajs" folder contains the databases of the trajectories of all candidates screened during the genetic algorithm searches for each run.
The "datasets" folder contains the training and test datasets used for the fine-tuning and benchmarking of the machine-learning potential on both fcc and hcp surfaces. The datasets are provided in both .db and .json formats. This folder also includes the fine-tuned machine-learning potential in .tar format.
The "ga_scripts" folder contains the Python scripts based on the ASE genetic algorithm (ase.ga) framework that were modified and extended specifically for the present study.
Finally, the "Mn_structural_motifs" folder contains the structural files and optimization trajectories of representative Mn structural motifs adsorbed on different Co surface sites, extracted from the identified global minimum structures.
https://publikationen.bibliothek.kit.edu/1000194586
15,7 GB
application/x-tar