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Model for optb88vdw_total_energy

  • Description: This is a benchmark to evaluate how accurately an AI model can predict the total energy (at the OPTB88vdW functional level of theory) using the JARVIS-DFT (dft_3d) dataset. The dataset contains different types of chemical formula and atomic structures. Here we use mean absolute error (MAE) to compare models with respect to DFT (OPT) accuracy.


Reference(s): https://www.nature.com/articles/s41524-023-01012-9;https://hackingmaterials.lbl.gov/matminer, https://hackingmaterials.lbl.gov/matminer/, https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301, https://www.nature.com/articles/s41524-021-00650-1, https://doi.org/10.1103/PhysRevMaterials.2.083801, https://doi.org/10.48550/arXiv.2305.11842, https://www.nature.com/articles/s41524-020-00440-1, https://github.com/aimat-lab/gcnn_keras, https://github.com/divelab/AIRS/tree/main/OpenMat/PotNet

Model benchmarks

Model nameDataset MAE Team name Dataset size Date submitted Notes
kgcnn_schnetdft_3d0.0374kgcnn5571309-26-2023CSV, JSON, run.sh, Info
kgcnn_coNGNdft_3d0.0273kgcnn5571305-06-2023CSV, JSON, run.sh, Info
matminer_xgboostdft_3d0.0936UofT5571305-22-2023CSV, JSON, run.sh, Info
alignn_modeldft_3d0.0367ALIGNN5571301-14-2023CSV, JSON, run.sh, Info
potnetdft_3d0.0321DIVE@TAMU5571306-02-2023CSV, JSON, run.sh, Info
kgcnn_megnetdft_3d0.0393kgcnn5571305-06-2023CSV, JSON, run.sh, Info
cfid_chemdft_3d0.3526JARVIS5571301-14-2023CSV, JSON, run.sh, Info
matminer_lgbmdft_3d0.1509Matminer5571301-14-2023CSV, JSON, run.sh, Info
matminer_rfdft_3d0.1544UofT5571305-22-2023CSV, JSON, run.sh, Info
kgcnn_coGNdft_3d0.0262kgcnn5571305-06-2023CSV, JSON, run.sh, Info
cgcnn_modeldft_3d0.0815CGCNN5571301-14-2023CSV, JSON, run.sh, Info
cfiddft_3d0.2436JARVIS5571301-14-2023CSV, JSON, run.sh, Info
kgcnn_dimenetPPdft_3d0.051kgcnn5571305-06-2023CSV, JSON, run.sh, Info
kgcnn_cgcnndft_3d0.0584kgcnn5571309-26-2023CSV, JSON, run.sh, Info