model_name: MatRIS v0.5.0 MPtrj model_key: matris-v050-mptrj model_version: v0.5.0 date_added: '2025-03-13' date_published: '2025-03-13' authors: - name: Yuanchang Zhou affiliation: Institute of Computing Technology, Chinese Academy of Sciences email: zhouyuanchang23s@ict.ac.cn orcid: https://orcid.org/0009-0004-6801-7033 - name: Siyu Hu affiliation: Institute of Computing Technology, Chinese Academy of Sciences email: husiyu@ict.ac.cn orcid: https://orcid.org/0009-0008-6085-4645 - name: Chen Wang affiliation: Institute of Computing Technology, Chinese Academy of Sciences email: wangchen246@mails.ucas.ac.cn orcid: https://orcid.org/0009-0006-7542-8281 - name: Guangming Tan affiliation: Institute of Computing Technology, Chinese Academy of Sciences email: tgm@ict.ac.cn orcid: https://orcid.org/0000-0002-6361-5948 - name: Weile Jia affiliation: Institute of Computing Technology, Chinese Academy of Sciences email: jiaweile@ict.ac.cn orcid: https://orcid.org/0000-0001-8539-8326 corresponding: true trained_by: - name: Yuanchang Zhou affiliation: Institute of Computing Technology, Chinese Academy of Sciences email: zhouyuanchang23s@ict.ac.cn orcid: https://orcid.org/0009-0004-6801-7033 repo: https://github.com/HPC-AI-Team/MatRIS url: https://github.com/HPC-AI-Team/MatRIS doi: https://github.com/HPC-AI-Team/MatRIS # no paper yet, using repo URL paper: https://github.com/HPC-AI-Team/MatRIS # no paper yet, using repo URL pr_url: https://github.com/janosh/matbench-discovery/pull/293 checkpoint_url: https://figshare.com/files/59143058 license: code: BSD-3-Clause code_url: https://github.com/HPC-AI-Team/MatRIS/blob/main/LICENSE checkpoint: BSD-3-Clause checkpoint_url: https://github.com/HPC-AI-Team/MatRIS/blob/main/LICENSE openness: OSOD train_task: S2EFSM test_task: IS2RE-SR targets: EFS_GM model_type: UIP model_params: 5825620 trained_for_benchmark: true n_estimators: 1 status: superseded superseded_by: matris-10m-mp hyperparams: max_force: 0.05 max_steps: 500 ase_optimizer: FIRE cell_filter: FrechetCellFilter optimizer: Adam loss: MAE loss_weights: energy: 5.0 force: 5.0 stress: 0.1 magmom: 0.1 batch_size: 160 initial_learning_rate: 0.0003 learning_rate_schedule: CosineAnnealingLR(T_max=300) epochs: 30 n_layers: 6 n_features: 128 n_radial_bessel_basis: 7 graph_construction_radius: 6.0 # Å, from https://github.com/HPC-AI-Team/MatRIS/issues/1#issuecomment-2802181230 three_body_cutoff: 4.0 # Å max_neighbors: .inf # see sec. 3.2 in arXiv:2502.12147 requirements: torch: 2.4.1 ase: 3.23.0 pymatgen: 2024.11.23 numpy: 1.26.4 training_set: [MPtrj] training_cost: Nvidia A100 GPUs: { amount: 16, hours: 120 } notes: Description: | MatRIS is a foundation model for materials representation and interaction simulation. metrics: phonons: kappa_103: κ_SRME: 0.8651 pred_file: models/matris/matris-0.5.0-mptrj/2025-03-12-kappa-103-fire-dist=0.01-fmax1e-4-symprec1e-5.json.gz pred_file_url: https://figshare.com/files/53090600 κ_SRE: 0.7711 geo_opt: pred_file: models/matris/matris-0.5.0-mptrj/2025-03-12-wbm-geo-opt.jsonl.gz pred_file_url: https://figshare.com/files/57751045 struct_col: matris_structure symprec=1e-5: rmsd: 0.0773 # unitless n_sym_ops_mae: 5.7108 # unitless symmetry_decrease: 0.593 # fraction symmetry_match: 0.3362 # fraction symmetry_increase: 0.0619 # fraction n_structures: 256959 # count analysis_file: models/matris/matris-0.5.0-mptrj/2025-03-12-wbm-geo-opt-symprec=1e-5-moyo=0.4.2.csv.gz analysis_file_url: https://figshare.com/files/53504666 symprec=1e-2: rmsd: 0.0773 # unitless n_sym_ops_mae: 2.0591 # unitless symmetry_decrease: 0.0955 # fraction symmetry_match: 0.7874 # fraction symmetry_increase: 0.1091 # fraction n_structures: 256959 # count analysis_file: models/matris/matris-0.5.0-mptrj/2025-03-12-wbm-geo-opt-symprec=1e-2-moyo=0.4.2.csv.gz analysis_file_url: https://figshare.com/files/53504669 discovery: pred_file: models/matris/matris-0.5.0-mptrj/2025-03-12-wbm-IS2RE.csv.gz pred_file_url: https://figshare.com/files/53000174 pred_col: e_form_per_atom_matris_mp full_test_set: F1: 0.798 # fraction DAF: 4.456 # dimensionless Precision: 0.765 # fraction Recall: 0.834 # fraction Accuracy: 0.927 # fraction TPR: 0.834 # fraction FPR: 0.053 # fraction TNR: 0.947 # fraction FNR: 0.166 # fraction TP: 36755.0 # count FP: 11312.0 # count TN: 201559.0 # count FN: 7337.0 # count MAE: 0.035 # eV/atom RMSE: 0.08 # eV/atom R2: 0.8 # dimensionless missing_preds: 6 # count most_stable_10k: F1: 0.984 # fraction DAF: 6.341 # dimensionless Precision: 0.969 # fraction Recall: 1.0 # fraction Accuracy: 0.969 # fraction TPR: 1.0 # fraction FPR: 1.0 # fraction TNR: 0.0 # fraction FNR: 0.0 # fraction TP: 9693.0 # count FP: 307.0 # count TN: 0.0 # count FN: 0.0 # count MAE: 0.026 # eV/atom RMSE: 0.057 # eV/atom R2: 0.926 # dimensionless missing_preds: 0 # count unique_prototypes: F1: 0.809 # fraction DAF: 5.049 # dimensionless Precision: 0.772 # fraction Recall: 0.85 # fraction Accuracy: 0.938 # fraction TPR: 0.85 # fraction FPR: 0.046 # fraction TNR: 0.954 # fraction FNR: 0.15 # fraction TP: 28379.0 # count FP: 8391.0 # count TN: 173723.0 # count FN: 4995.0 # count MAE: 0.037 # eV/atom RMSE: 0.082 # eV/atom R2: 0.803 # dimensionless missing_preds: 4 # count