---
language:
- en
license: mit
task_categories:
- time-series-forecasting
dataset_info:
  features:
  - name: t
    dtype:
      array2_d:
        shape:
        - 1001
        - 1
        dtype: float64
  - name: x
    dtype:
      array2_d:
        shape:
        - 1001
        - 900
        dtype: float64
  - name: args
    dtype:
      array2_d:
        shape:
        - 1001
        - 1
        dtype: float64
  splits:
  - name: train
    num_bytes: 4413496400
    num_examples: 610
  - name: valid
    num_bytes: 795876400
    num_examples: 110
  - name: test_fast
    num_bytes: 3617620000
    num_examples: 500
  - name: test_medium
    num_bytes: 3617620000
    num_examples: 500
  - name: test_slow
    num_bytes: 3617620000
    num_examples: 500
  download_size: 14279999184
  dataset_size: 16062232800
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: valid
    path: data/valid-*
  - split: test_fast
    path: data/test_fast-*
  - split: test_medium
    path: data/test_medium-*
  - split: test_slow
    path: data/test_slow-*
---

# Dataset for polymer dynamics


References
- Chen, X. et al. Constructing custom thermodynamics using deep learning. Nature Computational Science 4, 66–85 (2024).