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meta_modulus.py

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+import datasets
+import torch
+from torch_geometric.data import Data
+
+
+_CITATION = """\
+@article{metamatbench,
+  title={MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery},
+  author={Chen, Jianpeng and Zhan, Wangzhi and Wang, Haohui and Jia, Zian and Gan, Jingru and Zhang, Junkai and Qi, Jingyuan and Chen, Tingwei and Huang, Lifu and Chen, Muhao and others},
+  journal={arXiv preprint arXiv:2505.20299},
+  year={2025}
+}
+"""
+
+_DESCRIPTION = """
+This dataset contains lattice structure data for predicting modulus properties, preprocessed into PyTorch Geometric (PyG) compatible format.
+"""
+
+
+class LatticeModulusDataset(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            features=datasets.Features({
+                'frac_coords': datasets.Array2D(shape=(None, 3), dtype='float32'),
+                'cart_coords': datasets.Array2D(shape=(None, 3), dtype='float32'),
+                'node_feat': datasets.Array2D(shape=(None, 4), dtype='float32'),
+                'node_type': datasets.Sequence(datasets.Value('int64')),
+                'edge_feat': datasets.Array2D(shape=(None, 1), dtype='float32'),
+                'edge_index': datasets.Array2D(shape=(2, None), dtype='int64'),
+                'lengths': datasets.Array2D(shape=(1, 3), dtype='float32'),
+                'num_nodes': datasets.Value('int64'),
+                'num_atoms': datasets.Value('int64'),
+                'angles': datasets.Array2D(shape=(1, 3), dtype='float32'),
+                'vector': datasets.Array2D(shape=(1, 9), dtype='float32'),
+                'y': datasets.Array2D(shape=(1, 12), dtype='float32'),
+                'young': datasets.Array2D(shape=(1, 3), dtype='float32'),
+                'shear': datasets.Array2D(shape=(1, 3), dtype='float32'),
+                'poisson': datasets.Array2D(shape=(1, 6), dtype='float32'),
+            }),
+        )
+
+    def _split_generators(self, dl_manager):
+        file_path = dl_manager.download_and_extract('data.pkl')
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepath': file_path})]
+
+    def _generate_examples(self, filepath):
+        dataset = torch.load(filepath)
+
+        for idx, data in enumerate(dataset):
+            yield idx, {
+                'frac_coords': data.frac_coords.numpy(),
+                'cart_coords': data.cart_coords.numpy(),
+                'node_feat': data.node_feat.numpy(),
+                'node_type': data.node_type.numpy().tolist(),
+                'edge_feat': data.edge_feat.numpy(),
+                'edge_index': data.edge_index.numpy(),
+                'lengths': data.lengths.numpy(),
+                'num_nodes': data.num_nodes,
+                'num_atoms': data.num_atoms,
+                'angles': data.angles.numpy(),
+                'vector': data.vector.numpy(),
+                'y': data.y.numpy(),
+                'young': data.young.numpy(),
+                'shear': data.shear.numpy(),
+                'poisson': data.poisson.numpy(),
+            }