metadata
dataset_info:
- config_name: Z_1-90
features:
- name: material_id
dtype: string
- name: formation_energy_per_atom
dtype: float64
- name: dft_band_gap
dtype: float64
- name: pretty_formula
dtype: string
- name: e_above_hull
dtype: float64
- name: elements
sequence: string
- name: spacegroup_number
dtype: float64
- name: azure_bulk_modulus
dtype: float64
- name: larsen_score_2d
dtype: float64
- name: Si_100_mismatch
dtype: float64
- name: azure_band_gap
dtype: float64
- name: dft_bulk_modulus
dtype: float64
- name: dft_poisson_ratio
dtype: float64
- name: dft_mag_density
dtype: float64
- name: structure
dtype: string
- name: unique_elements
sequence: string
- name: num_sites
dtype: int64
splits:
- name: train
num_bytes: 69299317
num_examples: 25923
- name: valid
num_bytes: 22967236
num_examples: 8650
- name: test
num_bytes: 22926999
num_examples: 8647
download_size: 42557908
dataset_size: 115193552
- config_name: default
features:
- name: material_id
dtype: string
- name: formation_energy_per_atom
dtype: float64
- name: dft_band_gap
dtype: float64
- name: pretty_formula
dtype: string
- name: e_above_hull
dtype: float64
- name: elements
sequence: string
- name: spacegroup_number
dtype: float64
- name: azure_bulk_modulus
dtype: float64
- name: larsen_score_2d
dtype: float64
- name: Si_100_mismatch
dtype: float64
- name: azure_band_gap
dtype: float64
- name: dft_bulk_modulus
dtype: float64
- name: dft_poisson_ratio
dtype: float64
- name: dft_mag_density
dtype: float64
- name: structure
dtype: string
- name: unique_elements
sequence: string
- name: num_sites
dtype: int64
splits:
- name: train
num_bytes: 72244062
num_examples: 27136
- name: valid
num_bytes: 23916305
num_examples: 9047
- name: test
num_bytes: 23864869
num_examples: 9046
download_size: 44322023
dataset_size: 120025236
configs:
- config_name: Z_1-90
data_files:
- split: train
path: Z_1-90/train-*
- split: valid
path: Z_1-90/valid-*
- split: test
path: Z_1-90/test-*
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
The dataset is created with this pipeline:
git clone https://github.com/microsoft/mattergen.git
cd mattergen
git lfs pull -I data-release/mp-20/ --exclude=""
unzip data-release/mp-20/mp_20.zip -d datasets
import pandas as pd
from tqdm.auto import tqdm
from pymatgen.io.cif import CifParser
from datasets import DatasetDict, Dataset
train_path = "./datasets/mp_20/train.csv"
valid_path = "./datasets/mp_20/val.csv"
test_path = "./datasets/mp_20/test.csv"
train_df = pd.read_csv(train_path)
train_structures = [
CifParser.from_str(s).parse_structures(primitive=True, on_error="ignore")[0]
for s in tqdm(train_df["cif"], desc="Parsing CIFs", miniters=20)
]
train_df = train_df.drop(columns=['Unnamed: 0', 'cif'])
train_df["structure"] = [s.to_json() for s in tqdm(train_structures, miniters=20)]
valid_df = pd.read_csv(valid_path)
valid_structures = [
CifParser.from_str(s).parse_structures(primitive=True, on_error="ignore")[0]
for s in tqdm(valid_df["cif"], desc="Parsing CIFs", miniters=20)
]
valid_df = valid_df.drop(columns=['Unnamed: 0', 'cif'])
valid_df["structure"] = [s.to_json() for s in tqdm(valid_structures, miniters=20)]
test_df = pd.read_csv(test_path)
test_structures = [
CifParser.from_str(s).parse_structures(primitive=True, on_error="ignore")[0]
for s in tqdm(test_df["cif"], desc="Parsing CIFs", miniters=20)
]
test_df = test_df.drop(columns=['Unnamed: 0', 'cif'])
test_df["structure"] = [s.to_json() for s in tqdm(test_structures, miniters=20)]
train_dataset = Dataset.from_pandas(train_df)
valid_dataset = Dataset.from_pandas(valid_df)
test_dataset = Dataset.from_pandas(test_df)
dataset_dict = DatasetDict({
'train': train_dataset,
'valid': valid_dataset,
'test': test_dataset
})
dataset_dict.push_to_hub('xpanceo-team/mp-20', private=True)