Dataset

mbGDML_maldonado_2023


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Name :
mbGDML_maldonado_2023
Extended ID :
mbGDML_maldonado_2023__Maldonado__DS_e94my2wrh074_0
ColabFit ID :
DS_e94my2wrh074_0
Files :
colabfitspec.json
Description :
Configurations of water, acetonitrile and methanol, simulated with ASE and modeled using a variety of software and methods: GAP, SchNet, GDML, ORCA and mbGDML. Forces and potential energy included; metadata includes kinetic energy and velocities.
Authors :
Alex M. Maldonado
DOI :
10.60732/717087e2 https://commons.datacite.org/doi.org/10.60732/717087e2 https://doi.datacite.org/dois/10.60732%2F717087e2 https://doi.org/10.60732/717087e2 Cite as: Maldonado, A. M. "mbGDML maldonado 2023." ColabFit, 2023. https://doi.org/10.60732/717087e2.
For other citation formats, see the DataCite Fabrica page for this dataset.
Num. Configurations :
24,509
Num. Atoms :
711,324
Downloads :
44
Calculated Property Types :
atomic_forces cauchy_stress energy
Elements :
C (18.57%) H (60.76%) N (5.91%) O (14.76%)
Methods :
IP-SchNet GFN2-xTB IP-mbGDML IP-GAP MP2
Software :
ORCA
Publication Link :
https://doi.org/10.26434/chemrxiv-2023-wdd1r
Data Source Link :
https://doi.org/10.5281/zenodo.7112197
Configuration Sets by Name :
Configuration Sets by ID :
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