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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'SEA'}) and 1 missing columns ({'CO'}).

This happened while the csv dataset builder was generating data using

hf://datasets/fishmoon1234/Polymer_Fire_Properties/10folds_data/SEA_exp_data_avg/data_0/train.csv (at revision 370ab56b4453859d072db7bd9aa1b65aad5bd07e), ['hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_0/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_1/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_2/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_3/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_4/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_5/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_6/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_7/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_8/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_9/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_0/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_1/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_2/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_3/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_4/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_5/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_6/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_7/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_8/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_9/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_0/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_1/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_2/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_3/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_4/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_5/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_6/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_7/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_8/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_9/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_0/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_1/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_2/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_3/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_4/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_5/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_6/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_7/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_8/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_9/train.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              smiles_canonicalized: string
              SEA: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 525
              to
              {'smiles_canonicalized': Value('string'), 'CO': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'SEA'}) and 1 missing columns ({'CO'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/fishmoon1234/Polymer_Fire_Properties/10folds_data/SEA_exp_data_avg/data_0/train.csv (at revision 370ab56b4453859d072db7bd9aa1b65aad5bd07e), ['hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_0/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_1/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_2/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_3/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_4/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_5/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_6/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_7/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_8/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/CO_exp_data_avg/data_9/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_0/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_1/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_2/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_3/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_4/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_5/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_6/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_7/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_8/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/SEA_exp_data_avg/data_9/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_0/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_1/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_2/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_3/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_4/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_5/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_6/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_7/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_8/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/phrr_exp_data/data_9/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_0/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_1/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_2/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_3/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_4/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_5/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_6/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_7/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_8/train.csv', 'hf://datasets/fishmoon1234/Polymer_Fire_Properties@370ab56b4453859d072db7bd9aa1b65aad5bd07e/10folds_data/tig_exp_data/data_9/train.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

smiles_canonicalized
string
CO
float64
*C(=O)c1cccc(C(=O)c2cccc(Oc3cccc(*)c3)c2)c1
0.0375
*C(=O)c1cccc(Oc2cccc(Oc3cccc(*)c3)c2)c1
0.136667
*CC(*)(C)C(=O)OC
0.008333
*CC(*)C
0.03
*CC(*)Cl
0.037667
*CC*
0.022333
*N=C(*)Oc1c(C)ccc(Cc2ccc(C)c(OC(*)=N*)c2C)c1C
0.03
*N=C(*)Oc1cccc(C(C)(C)c2cccc(C(C)(C)c3cccc(OC(*)=N*)c3)c2)c1
0.145
*N=C(*)Oc1cccc(C(C)(C)c2cccc(OC(*)=N*)c2)c1
0.0425
*N=C(*)Oc1cccc(C(C)c2cccc(OC(*)=N*)c2)c1
0.0725
*N=C(*)Oc1cccc(C(c2cccc(OC(*)=N*)c2)(C(F)(F)F)C(F)(F)F)c1
0.085
*N=C(*)Oc1ccccc1Cc1cccc(Cc2ccccc2OC(*)=N*)c1OC(*)=N*
0.02
*N=P(*)(Oc1ccccc1)Oc1ccc(OCC)cc1
0.14
*NC(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3cccc(*)c3)cc1)C2=O
0.143333
*NCCCCCCNC(=O)CCCCC(*)=O
0.009333
*OCCOC(=O)Nc1cccc(Cc2ccc(NC(*)=O)cc2)c1
0.03
*OCCOC(=O)c1ccc(C(*)=O)cc1
0.024667
*OCCOC(=O)c1ccc2cc(C(*)=O)ccc2c1
0.0625
*Oc1ccc(-c2cccc(Oc3cccc(S(=O)(=O)c4cccc(*)c4)c3)c2)cc1
0.073333
*Oc1ccc(C(=C(Cl)Cl)c2ccc(OC(*)=O)cc2)cc1
0.155
*Oc1ccc(C(c2ccc(OC(=O)c3ccc(OC(=O)c4ccc(C(=O)Oc5ccc(C(*)=O)cc5)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
0.2
*Oc1ccc(C(c2ccc(Oc3ccc(P(=O)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
0.45
*Oc1cccc(C(C)(C)c2cccc(OC(*)=O)c2)c1
0.070833
*Sc1ccc(*)cc1
0.026667
*c1ccc(-c2nc3ccc(-c4ccc5[nH]c(*)nc5c4)cc3[nH]2)cc1
0.01
*c1ccc(C2CC3CC2C2CC(*)CC32)cc1OC#N
0.03225
*c1ccc(N2C(=O)c3ccc(Oc4cccc(C(C)(C)c5cccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)c5)c4)cc3C2=O)cc1
0.0625
*c1cccc(Oc2ccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)cc2)c1
0.005
*C(=O)c1cccc(C(=O)c2cccc(Oc3cccc(*)c3)c2)c1
0.0375
*C(=O)c1cccc(Oc2cccc(Oc3cccc(*)c3)c2)c1
0.136667
*CC(*)(C)C(=O)OC
0.008333
*CC(*)C
0.03
*CC(*)Cl
0.037667
*CC*
0.022333
*N=C(*)Oc1c(C)ccc(Cc2ccc(C)c(OC(*)=N*)c2C)c1C
0.03
*N=C(*)Oc1ccc(C(=C(Cl)Cl)c2ccc(OC(*)=N*)cc2)cc1
0.034
*N=C(*)Oc1cccc(C(C)(C)c2cccc(C(C)(C)c3cccc(OC(*)=N*)c3)c2)c1
0.145
*N=C(*)Oc1cccc(C(C)(C)c2cccc(OC(*)=N*)c2)c1
0.0425
*N=C(*)Oc1cccc(C(C)c2cccc(OC(*)=N*)c2)c1
0.0725
*N=C(*)Oc1cccc(C(c2cccc(OC(*)=N*)c2)(C(F)(F)F)C(F)(F)F)c1
0.085
*N=C(*)Oc1cccc(Cc2ccc(Cc3ccccc3OC(*)=N*)cc2OC(*)=N*)c1
0.0125
*N=P(*)(Oc1ccccc1)Oc1ccc(OCC)cc1
0.14
*NC(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3cccc(*)c3)cc1)C2=O
0.143333
*OCCOC(=O)Nc1cccc(Cc2ccc(NC(*)=O)cc2)c1
0.03
*OCCOC(=O)c1ccc(C(*)=O)cc1
0.024667
*OCCOC(=O)c1ccc2cc(C(*)=O)ccc2c1
0.0625
*Oc1ccc(-c2cccc(Oc3cccc(S(=O)(=O)c4cccc(*)c4)c3)c2)cc1
0.073333
*Oc1ccc(C(=C(Cl)Cl)c2ccc(OC(*)=O)cc2)cc1
0.155
*Oc1ccc(C(c2ccc(OC(=O)c3ccc(OC(=O)c4ccc(C(=O)Oc5ccc(C(*)=O)cc5)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
0.2
*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4ccc(C(c5ccc(C(=O)c6ccc(*)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
0.14
*Oc1ccc(C(c2ccc(Oc3ccc(P(=O)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
0.45
*Oc1cccc(C(C)(C)c2cccc(OC(*)=O)c2)c1
0.070833
*Oc1cccc(C(C)(C)c2cccc(Oc3cccc(S(=O)(=O)c4cccc(*)c4)c3)c2)c1
0.072
*Sc1ccc(*)cc1
0.026667
*c1ccc(C2CC3CC2C2CC(*)CC32)cc1OC#N
0.03225
*c1cccc(Oc2ccc(-n3c(=O)c4cc5c(=O)n(*)c(=O)c5cc4c3=O)cc2)c1
0.005
*C(=O)c1cccc(C(=O)c2cccc(Oc3cccc(*)c3)c2)c1
0.0375
*C(=O)c1cccc(Oc2cccc(Oc3cccc(*)c3)c2)c1
0.136667
*CC(*)(C)C(=O)OC
0.008333
*CC(*)C
0.03
*CC(*)Cl
0.037667
*CC*
0.022333
*N=C(*)Oc1c(C)ccc(Cc2ccc(C)c(OC(*)=N*)c2C)c1C
0.03
*N=C(*)Oc1ccc(C(=C(Cl)Cl)c2ccc(OC(*)=N*)cc2)cc1
0.034
*N=C(*)Oc1cccc(C(C)(C)c2cccc(C(C)(C)c3cccc(OC(*)=N*)c3)c2)c1
0.145
*N=C(*)Oc1cccc(C(C)(C)c2cccc(OC(*)=N*)c2)c1
0.0425
*N=C(*)Oc1cccc(C(c2cccc(OC(*)=N*)c2)(C(F)(F)F)C(F)(F)F)c1
0.085
*N=C(*)Oc1cccc(Cc2ccc(Cc3ccccc3OC(*)=N*)cc2OC(*)=N*)c1
0.0125
*N=C(*)Oc1ccccc1Cc1cccc(Cc2ccccc2OC(*)=N*)c1OC(*)=N*
0.02
*N=P(*)(Oc1ccccc1)Oc1ccc(OCC)cc1
0.14
*NC(=O)c1ccc2c(c1)C(=O)N(c1ccc(Cc3cccc(*)c3)cc1)C2=O
0.143333
*NCCCCCCNC(=O)CCCCC(*)=O
0.009333
*OCCOC(=O)Nc1cccc(Cc2ccc(NC(*)=O)cc2)c1
0.03
*OCCOC(=O)c1ccc(C(*)=O)cc1
0.024667
*OCCOC(=O)c1ccc2cc(C(*)=O)ccc2c1
0.0625
*Oc1ccc(-c2cccc(Oc3cccc(S(=O)(=O)c4cccc(*)c4)c3)c2)cc1
0.073333
*Oc1ccc(C(c2ccc(OC(=O)c3ccc(OC(=O)c4ccc(C(=O)Oc5ccc(C(*)=O)cc5)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
0.2
*Oc1ccc(C(c2ccc(Oc3ccc(C(=O)c4ccc(C(c5ccc(C(=O)c6ccc(*)cc6)cc5)(C(F)(F)F)C(F)(F)F)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
0.14
*Oc1ccc(C(c2ccc(Oc3ccc(P(=O)(c4ccccc4)c4ccc(*)cc4)cc3)cc2)(C(F)(F)F)C(F)(F)F)cc1
0.45
*Oc1cccc(C(C)(C)c2cccc(OC(*)=O)c2)c1
0.070833
*Oc1cccc(C(C)(C)c2cccc(Oc3cccc(S(=O)(=O)c4cccc(*)c4)c3)c2)c1
0.072
*Sc1ccc(*)cc1
0.026667
*c1ccc(-c2nc3ccc(-c4ccc5[nH]c(*)nc5c4)cc3[nH]2)cc1
0.01
*c1ccc(C2CC3CC2C2CC(*)CC32)cc1OC#N
0.03225
*c1ccc(N2C(=O)c3ccc(Oc4cccc(C(C)(C)c5cccc(Oc6ccc7c(c6)C(=O)N(*)C7=O)c5)c4)cc3C2=O)cc1
0.0625
*C(=O)c1cccc(C(=O)c2cccc(Oc3cccc(*)c3)c2)c1
0.0375
*C(=O)c1cccc(Oc2cccc(Oc3cccc(*)c3)c2)c1
0.136667
*CC(*)C
0.03
*CC(*)Cl
0.037667
*CC*
0.022333
*N=C(*)Oc1c(C)ccc(Cc2ccc(C)c(OC(*)=N*)c2C)c1C
0.03
*N=C(*)Oc1ccc(C(=C(Cl)Cl)c2ccc(OC(*)=N*)cc2)cc1
0.034
*N=C(*)Oc1cccc(C(C)(C)c2cccc(C(C)(C)c3cccc(OC(*)=N*)c3)c2)c1
0.145
*N=C(*)Oc1cccc(C(C)(C)c2cccc(OC(*)=N*)c2)c1
0.0425
*N=C(*)Oc1cccc(C(C)c2cccc(OC(*)=N*)c2)c1
0.0725
*N=C(*)Oc1cccc(C(c2cccc(OC(*)=N*)c2)(C(F)(F)F)C(F)(F)F)c1
0.085
*N=C(*)Oc1cccc(Cc2ccc(Cc3ccccc3OC(*)=N*)cc2OC(*)=N*)c1
0.0125
*N=C(*)Oc1ccccc1Cc1cccc(Cc2ccccc2OC(*)=N*)c1OC(*)=N*
0.02
*NCCCCCCNC(=O)CCCCC(*)=O
0.009333
*OCCOC(=O)Nc1cccc(Cc2ccc(NC(*)=O)cc2)c1
0.03
End of preview.

Polymer Fire Properties

Repeat-unit polymer structures written as pSMILES, paired with four fire properties:

Symbol Property Unit
tig time to ignition s
phrr / pkhrr peak heat release rate kW m⁻²
SEA specific extinction area m² kg⁻¹
CO carbon monoxide yield kg kg⁻¹

Three datasets are provided: a large corpus of enumerated repeat-unit structures (v1), a simulated corpus labelled with the Fire Dynamics Simulator (v2), and a small set of bench-scale cone-calorimeter measurements collected from the literature, together with the ten cross-validation folds used to evaluate property prediction on it.

Structures are canonical SMILES with each open end of the repeat unit written as a bare asterisk, for example *CC(*)C for polypropylene.

Files

Path Rows Contents
v1_1.7M_synthetic_structure.smi 1,735,755 Enumerated repeat-unit structures, unlabelled
v2_110k_FDS.smi 109,993 Simulated cone-calorimeter cases
experimental_flux_thickness.smi 203 Measured cone-calorimeter data, 49 polymers
10folds_data/ — Ten-fold splits of the experimental data, per property

v1_1.7M_synthetic_structure.smi

One pSMILES string per line, no header and no other columns.

The corpus contains 1,732,431 distinct strings over 1,735,755 lines: 1,077 strings occur more than once, for 3,324 duplicate lines in total. Deduplicate if your use requires unique structures.

v2_110k_FDS.smi

Whitespace-separated, no header, nine columns:

# Column Unit Notes
1 index — row counter
2 name — POL_#####, one identifier per repeat unit
3 psmiles — canonical, bare *
4 thickness mm 1.0 – 25.0
5 flux kW m⁻² 20.0 – 100.0
6 tig s see the note on non-ignition below
7 pkhrr kW m⁻²
8 sea m² kg⁻¹
9 co kg kg⁻¹

55,000 distinct repeat units, each simulated at two exposures: one incident flux drawn from 20–50 kW m⁻² and one from 50–100 kW m⁻², each at a specimen thickness drawn from 1–25 mm. Simulations use the solid-phase-only pyrolysis model of the Fire Dynamics Simulator, version 6.10.1. Ignition is taken at a critical heat release rate per unit area of 5 kW m⁻². From each simulated curve, tig is the time at which the heat release rate per unit area first exceeds 5 kW m⁻², and pkhrr is the maximum of that curve.

Two things to handle before training on this file.

Non-ignition is censored, not measured. In 1,566 rows the specimen never ignited within the simulated horizon, and tig carries the horizon value of exactly 900.0 s rather than a time to ignition. These rows are identified by tig == 900.0: the longest ignition time among cases that did ignite is 899.0 s, so the test separates them exactly. Treat them as right-censored at 900 s, or drop them. Fitting a regressor to them as if 900 s were an observation will bias the long-ignition end.

SEA and CO do not vary with the exposure. They are not inputs to the pyrolysis model; they are assigned once per repeat unit from the structure–property relationships. Both rows of a given polymer therefore repeat the same sea and co, and a model that conditions them on thickness or flux is fitting noise.

experimental_flux_thickness.smi

Space-separated with a header row:

smiles_canonicalized thickness flux tig pkhrr sea co

203 measurements over 49 polymers, collected from the literature, at specimen thicknesses of 1.6–25.0 mm and incident fluxes of 20–100 kW m⁻².

Coverage is uneven, and empty cells read as NaN:

Property Rows measured Polymers measured
tig 203 49
pkhrr 203 49
sea 157 44
co 96 32

This file holds one row per (polymer, thickness, flux). Where a source reports replicate measurements at the same condition they are collapsed here; the fold files below keep the replicates, which is why they hold more rows.

10folds_data/

Ten-fold splits of the experimental data, one folder per property, each with data_0 … data_9 holding train.csv and test.csv.

Folder Columns Rows per fold (train / test) Distinct polymers
tig_exp_data/ smiles_names, smiles_canonicalized, thickness, flux, tig 206 / 22 49
phrr_exp_data/ smiles_names, smiles_canonicalized, thickness, flux, pkhrr 206 / 22 49
SEA_exp_data_avg/ smiles_canonicalized, SEA 40 / 4 44
CO_exp_data_avg/ smiles_canonicalized, CO 29 / 3 32

Note that the target column inside phrr_exp_data/ is named pkhrr, not phrr.

tig and pkhrr are modelled per measurement, so their folds carry the exposure conditions and retain the replicate measurements: 228 rows in all, against the 203 rows of experimental_flux_thickness.smi. SEA and CO are modelled per polymer, so their folds carry one row per polymer with the measurements averaged, and no exposure columns.

Split granularity. The SEA and CO folds hold one row per polymer, so no test polymer appears in the training split of its fold. The tig and pkhrr folds are split by measurement row rather than by polymer: in each fold, 15 to 21 of the test polymers also appear in that fold's training split, at a different thickness or flux, or as a replicate of the same condition. A model evaluated on these folds is therefore being asked to interpolate across exposures of polymers it has seen, not to generalize to unseen polymers. Group the rows by smiles_canonicalized and re-split if you need the stricter protocol.


Provenance

The v1 structures are enumerated combinatorially from a library of backbone templates and pendant groups. Every candidate is validated, written with each open end as a bare asterisk, and stored as a canonical SMILES string.

The v2 labels are simulated, not measured. The pyrolysis model takes ten polymer properties — density, thermal conductivity, specific heat capacity and surface emissivity; onset of decomposition, temperature at peak mass loss rate and heat of pyrolysis; and total heat of combustion, combustion efficiency and char. Surface emissivity is held fixed at 0.88 and the remaining nine are estimated from the repeat-unit SMILES by quantitative structure–property relationships.

The experimental measurements are drawn from the published cone-calorimeter literature. The per-polymer source for each of the 49 materials is listed in Table S1 of the supplementary information accompanying the paper.

Citation

Physics-guided chemical language modelling for polymer flammability prediction and chemically meaningful inverse design.

Code: https://github.com/fishmoon1234/poly4mer · Model weights: https://huggingface.co/fishmoon1234/poly4mer

License

MIT.

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