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Name JARVIS_TinNet_N
Extended ID JARVIS_TinNet_N_WangPillaiWangAchenieXin__DS_ffsgl0wufoft_0
Description The JARVIS_TinNet dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the TinNet-N dataset: a collection assembled to train a machine learning model for the purposes of assisting catalyst design by predicting chemical reactivity of transition-metal surfaces. The adsorption systems contained in this dataset consist of {100}-terminated Pt-based bimetallic surfaces doped with a third element. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.
Authors Shih-Han Wang
Hemanth Somarajan Pillai
Siwen Wang
Luke E. K. Achenie
Hongliang Xin
DOI 10.60732/81faec41

Cite as: Wang, S., Pillai, H. S., Wang, S., Achenie, L. E. K., and Xin, H. "JARVIS TinNet N." ColabFit, 2023.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements H (5.26%)
N (5.26%)
Ni (2.86%)
O (5.26%)
Pt (45.32%)
Zn (2.56%)
Pd (2.66%)
Fe (2.58%)
Ru (3.18%)
Au (1.01%)
Os (2.24%)
Cu (3.15%)
Ir (3.26%)
Mo (1.2%)
Cr (1.63%)
Rh (3.84%)
Co (3.07%)
Tc (1.34%)
Re (0.7%)
Ag (0.35%)
Mn (1.02%)
W (0.5%)
V (1.15%)
Cd (0.13%)
Nb (0.26%)
Sc (0.06%)
Hf (0.13%)
Number of Data Objects 329
Number of Configurations 329
Number of Atoms 6,251
Configuration Sets by Name (None)
Configuration Sets by ID (None)
Data Objects
ColabFit ID DS_ffsgl0wufoft_0
Files colabfitspec.json

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