polyOne Data Set The data set contains 100 million hypothetical polymers each with 29 predicted properties using machine learning models. We use PSMILES strings to represent polymer structures, see here and here. The polymers are generated by decomposing previously synthesized polymers into unique chemical fragments. Random and enumerative compositions of these fragments yield 100 million hypothetical PSMILES strings. All PSMILES strings are chemically valid polymers but, mostly, have never been synthesized before. More information can be found in the paper. Please note the license agreement in the LICENSE file. Full data set including the properties The data files are in Apache Parquet format. The files start with `polyOne_*.parquet`. I recommend using dask (`pip install dask`) to load and process the data set. Pandas also works but is slower. Load sharded data set with dask ```python import dask.dataframe as dd ddf = dd.read_parquet("*.parquet", engine="pyarrow") ``` For example, compute the description of data set ```python df_describe = ddf.describe().compute() df_describe ``` PSMILES strings only generated_polymer_smiles_train.txt - 80 million PSMILES strings for training polyBERT. One string per line. generated_polymer_smiles_dev.txt - 20 million PSMILES strings for testing polyBERT. One string per line.