add benchmark result of e_form for iComFormer and eComFormer models

benchmarks/matbench_v0.1_eComFormer/config.py

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+"""Pydantic model for default configuration and validation."""
+"""Implementation based on the template of ALIGNN."""
+
+import subprocess
+from typing import Optional, Union
+import os
+from pydantic import root_validator
+
+# vfrom pydantic import Field, root_validator, validator
+from pydantic.typing import Literal
+from matformer.utils import BaseSettings
+from matformer.models.pyg_att import MatformerConfig
+
+# from typing import List
+
+try:
+    VERSION = (
+        subprocess.check_output(["git", "rev-parse", "HEAD"]).decode().strip()
+    )
+except Exception as exp:
+    VERSION = "NA"
+    pass
+
+
+FEATURESET_SIZE = {"basic": 11, "atomic_number": 1, "cfid": 438, "cgcnn": 92}
+
+
+TARGET_ENUM = Literal[
+    "formation_energy_peratom",
+    "optb88vdw_bandgap",
+    "bulk_modulus_kv",
+    "shear_modulus_gv",
+    "mbj_bandgap",
+    "slme",
+    "magmom_oszicar",
+    "spillage",
+    "kpoint_length_unit",
+    "encut",
+    "optb88vdw_total_energy",
+    "epsx",
+    "epsy",
+    "epsz",
+    "mepsx",
+    "mepsy",
+    "mepsz",
+    "max_ir_mode",
+    "min_ir_mode",
+    "n-Seebeck",
+    "p-Seebeck",
+    "n-powerfact",
+    "p-powerfact",
+    "ncond",
+    "pcond",
+    "nkappa",
+    "pkappa",
+    "ehull",
+    "exfoliation_energy",
+    "dfpt_piezo_max_dielectric",
+    "dfpt_piezo_max_eij",
+    "dfpt_piezo_max_dij",
+    "gap pbe",
+    "e_form",
+    "e_hull",
+    "energy_per_atom",
+    "formation_energy_per_atom",
+    "band_gap",
+    "e_above_hull",
+    "mu_b",
+    "bulk modulus",
+    "shear modulus",
+    "elastic anisotropy",
+    "U0",
+    "HOMO",
+    "LUMO",
+    "R2",
+    "ZPVE",
+    "omega1",
+    "mu",
+    "alpha",
+    "homo",
+    "lumo",
+    "gap",
+    "r2",
+    "zpve",
+    "U",
+    "H",
+    "G",
+    "Cv",
+    "A",
+    "B",
+    "C",
+    "all",
+    "target",
+    "max_efg",
+    "avg_elec_mass",
+    "avg_hole_mass",
+    "_oqmd_band_gap",
+    "_oqmd_delta_e",
+    "_oqmd_stability",
+    "edos_up",
+    "pdos_elast",
+    "bandgap",
+    "energy_total",
+    "net_magmom",
+    "b3lyp_homo",
+    "b3lyp_lumo",
+    "b3lyp_gap",
+    "b3lyp_scharber_pce",
+    "b3lyp_scharber_voc",
+    "b3lyp_scharber_jsc",
+    "log_kd_ki",
+    "max_co2_adsp",
+    "min_co2_adsp",
+    "lcd",
+    "pld",
+    "void_fraction",
+    "surface_area_m2g",
+    "surface_area_m2cm3",
+    "indir_gap",
+    "f_enp",
+    "final_energy",
+    "energy_per_atom",
+    "label",
+]
+
+
+class TrainingConfig(BaseSettings):
+    """Training config defaults and validation."""
+
+    version: str = VERSION
+
+    # dataset configuration
+    dataset: Literal[
+        "dft_3d",
+        "megnet",
+        "mpf",
+    ] = "dft_3d"
+    target: TARGET_ENUM = "formation_energy_peratom"
+    atom_features: Literal["basic", "atomic_number", "cfid", "cgcnn"] = "cgcnn"
+    neighbor_strategy: Literal["k-nearest", "voronoi", "pairwise-k-nearest"] = "k-nearest"
+    id_tag: Literal["jid", "id", "_oqmd_entry_id"] = "jid"
+
+    # logging configuration
+
+    # training configuration
+    random_seed: Optional[int] = 123
+    classification_threshold: Optional[float] = None
+    n_val: Optional[int] = None
+    n_test: Optional[int] = None
+    n_train: Optional[int] = None
+    train_ratio: Optional[float] = 0.8
+    val_ratio: Optional[float] = 0.1
+    test_ratio: Optional[float] = 0.1
+    target_multiplication_factor: Optional[float] = None
+    epochs: int = 300
+    batch_size: int = 64
+    weight_decay: float = 0
+    learning_rate: float = 1e-2
+    filename: str = "sample"
+    warmup_steps: int = 2000
+    criterion: Literal["mse", "l1", "poisson", "zig"] = "mse"
+    optimizer: Literal["adamw", "sgd"] = "adamw"
+    scheduler: Literal["onecycle", "none", "step", "polynomial"] = "onecycle"
+    pin_memory: bool = False
+    save_dataloader: bool = False
+    write_checkpoint: bool = True
+    write_predictions: bool = True
+    store_outputs: bool = True
+    progress: bool = True
+    log_tensorboard: bool = False
+    standard_scalar_and_pca: bool = False
+    use_canonize: bool = True
+    num_workers: int = 2
+    cutoff: float = 4.0
+    max_neighbors: int = 12
+    keep_data_order: bool = False
+    distributed: bool = False
+    n_early_stopping: Optional[int] = None  # typically 50
+    output_dir: str = os.path.abspath(".")  # typically 50
+    matrix_input: bool = False
+    pyg_input: bool = False
+    use_lattice: bool = False
+    use_angle: bool = False
+
+    # model configuration
+    model = MatformerConfig(name="matformer")
+    print(model)
+    @root_validator()
+    def set_input_size(cls, values):
+        """Automatically configure node feature dimensionality."""
+        values["model"].atom_input_features = FEATURESET_SIZE[
+            values["atom_features"]
+        ]
+
+        return values