Dataset
cG-SchNet
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| Name | cG-SchNet |
|---|---|
| Extended ID | cG-SchNet_GebauerGasteggerHessmannMullerSchutt__DS_bj8v98iazsm5_0 |
| Description | Configurations from a cG-SchNet trained on a subset of the QM9dataset. Model was trained with the intention of providing molecules withspecified functional groups or motifs, relying on sampling of molecularfingerprint data. Relaxation data for the generated molecules is computedusing ORCA software. Configuration sets include raw data fromcG-SchNet-generated configurations, with models trained on several differenttypes of target data and DFT relaxation data as a separate configurationset. Includes approximately 80,000 configurations. |
| Authors |
Niklas W.A. Gebauer Michael Gastegger Stefaan S.P. Hessmann Klaus-Robert Müller Kristof T. Schütt |
| Elements |
C (35.93%) H (51.0%) N (4.96%) O (7.96%) F (0.16%) |
| Number of Data Objects | 79,772 |
| Number of Configurations | 79,772 |
| Number of Atoms | 1,467,492 |
| Links |
https://doi.org/10.1038/s41467-022-28526-y https://github.com/atomistic-machine-learning/cG-SchNet/ |
| Configuration Sets by Name |
cG-SchNet-polarizability-predicted — Configurations from cG-SchNet dataset with properties predicted using cG-SchNet model trained on isotropic polarizability data cG-SchNet-polarizability-computed — Configurations from cG-SchNet dataset with relaxation properties computed using ORCA, based on cG-Schnet model trained on isotropic polarizability data cG-SchNet-fingerprint-predicted — Configurations from cG-SchNet dataset with properties predicted using cG-SchNet model trained on vector-valued molecular fingerprints cG-SchNet-fingerprint-computed — Configurations from cG-SchNet dataset with relaxation properties computed using ORCA, based on cG-Schnet model trained on vector-valued molecular fingerprints cG-SchNet-gap-predicted — Configurations from cG-SchNet dataset with properties predicted using cG-SchNet model trained on HOMO-LUMO gap data cG-SchNet-gap-computed — Configurations from cG-SchNet dataset with relaxation properties computed using ORCA, based on cG-Schnet model trained on HOMO-LUMO gap data cG-SchNet-composition-relative-energy-predicted — Configurations from cG-SchNet dataset with properties predicted using cG-SchNet model trained on atomic composition and relative atomic energy data cG-SchNet-composition-relative-energy-computed — Configurations from cG-SchNet dataset with relaxation properties computed using ORCA, based on cG-Schnet model trained on atomic composition and relative atomic energy data cG-SchNet-gap-relative-energy-predicted — Configurations from cG-SchNet dataset with properties predicted using cG-SchNet model trained on HOMO-LUMO gap and relative atomic energy data cG-SchNet-gap-computed — Configurations from cG-SchNet dataset with relaxation properties computed using ORCA, based on cG-Schnet model trained on HOMO-LUMO gap and relative atomic energy data |
| Configuration Sets by ID |
CS_5qjcm2akq55o_0 CS_g3uhanndnb1v_0 CS_j8la9x2up2tp_0 CS_lgqthj3rs88w_0 CS_8wc49zelt5l2_0 CS_xf0uo0yw47c4_0 CS_7o4yer09jmni_0 CS_ej0d4cvvfc72_0 CS_qc5t9oq22rhv_0 CS_w9bbmaf8dirp_0 |
| Data Objects | Too many to display |
| ColabFit ID | DS_bj8v98iazsm5_0 |
| Files | colabfitspec.json |
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