Open source data in large scale are the cornerstones for data-driven research, but they are not readily available for polymers. In this work, we build a benchmark database, called PI1M (referring to ~1 million polymers for polymer informatics), to provide data resources that can be used for machine learning research in polymer informatics. A generative model is trained on ~12,000 polymers manually collected from the largest existing polymer database PolyInfo, and then the model is used to generate ~1 million polymers. A new representation for polymers, polymer embedding (PE), is introduced, which is then used to perform several polymer informatics regression tasks for density, glass transition temperature, melting temperature and dielectric constants. By comparing the PE trained by the PolyInfo data and that by the PI1M data, we conclude that the PI1M database covers similar chemical space as PolyInfo, but significantly populate regions where PolyInfo data are sparse. We believe PI1M will serve as a good benchmark database for future research in polymer informatics.

PoLyInfo is a polymer database of the National Institute for Materials Science (NIMS) of Japan. In our previous work, to make the PoLyInfo data machine-readable and further machine-understandable, we built PoLyInfoRDF to store these data in the standard Resource Description Framework (RDF) format and then defined its schema in the Shape Expressions (ShEx) language. When designing the schema, it is important to modularize the schema such that the common components are reusable. This is the objective of this study and is essential for efficiently defining schemas of the descriptors and properties, which constitute the core of PoLyInfo, a large collection of experimentally measured polymer characteristics. As an example of modularization, descriptors of the source-based name and molecular formula both include a string value, hence their schemas may well share (‘inherit’) the schema for string values, which would be defined once and subsequently reused throughout the entire set of schemas. Actually we noticed a considerable amount of common portions among schemas of descriptors and properties, and clarified a ‘schema hierarchy’ to reflect the above ‘inheritance’ relationships, separately from the ontological ‘concept hierarchy’. We then investigated the extent to which the adapted strategy was able to successfully define the PoLyInfoRDF schema. Under this schema hierarchy, inheritance mechanisms in ShEx played a significant role in sharing common portions effectively in a well-organized manner. We expect future developments based on our approach to contribute to the standardization of scientific data representation in RDF by providing a library of reusable schemas. We have developed a new method for modularizing scientific data schemas and　managing them hierarchically, and demonstrated it in PoLyInfo. This paves the way for data fusion in materials chemistry.

Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal–organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure–property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane–methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage.

This code and example files were used to run the self-consistent field theory parameterization in the publication "Predicting the toughness of compatibilized polymer blends" by Robert J. S. Ivancic (OrcID: https://orcid.org/0000-0001-9969-2534, National Institute of Standards and Technology, Material Measurement Laboratory, Division 642, Group 1) and Debra J. Audus (OrcID: https://orcid.org/0000-0002-5937-7721, National Institute of Standards and Technology, Material Measurement Laboratory, Division 642, Group 1). The data/README.md and code/README.md files describe the code and dataset.Abstract from the publication : Polymer blends can yield superior materials by merging the unique properties of their components. However, these mixtures often phase separate, leading to brittleness. While compatibilizers can toughen these blends, their vast design space makes optimization difficult. Here, we design a model to predict the toughness of compatibilized glassy polymer mixtures. This theory reveals thatcompatibilizers increase blend toughness by creating molecular bridges that stitch the interface together. We validate this theory by directly comparing its predictions to extensive molecular dynamics simulations in which we vary polymer incompatibility, chain stiffness, compatibilizer areal density, and blockiness of copolymer compatibilizers. We then parameterize the model using self-consistent field theory and confirm its ability to make predictions for practical applications through comparison with simulations and experiments. These results suggest the theory can optimize compatibilizer design for industrial glassy polymer blends extit{in silico} while providing microscopic insight, allowing for the development of next-generation mixtures.

Description of each file: - External Data 1. Tc_SMILES.csv: Same as dataset1.csv given in the latest by NeurIPS Challenge. 2. density_data.xlsx: https://github.com/Duke-MatSci/ChemProps 3. tg_data_1.xlsx: https://www.sciencedirect.com/science/article/pii/S2590159123000377#ec0005 3. tg_data_2.xlsx: https://springernature.figshare.com/articles/dataset/dataset_with_glass_transition_temperature/24219958?file=42507037 4. PolyOne Dataset: https://zenodo.org/records/7766806, Only the first 35000 data rows were taken from the file dataframe aa and ab. Look into this notebook (Version 15) for reference. Also, look into this notebook (Version 6) for the dataset, i.e., collecting Tg and Density from aa and ab, and using it in the preprocessing notebook. Kaggle Dataset Link: https://www.kaggle.com/datasets/fridaycode10/polyone-tg-and-density-dataset-of-2-parquet-files - Augmented Data 1. augmented_data_v1_10.csv: Data augmentation for only train.csv (no external data and no NeurIPS datasets 1, 2, 3, 4), with 10 oligomers per monomer, without Descriptors. Added during version 1 of this Kaggle dataset. 2. augmented_data_with_descriptors_v3_5.csv: Data augmentation for train.csv, all external data mentioned above (except polyone), and NeurIPS datasets (1, 2, 3, 4), with 5 oligomers per monomer, with Descriptors. Added during version 3 of this Kaggle dataset. 3. augmented_data_with_descriptors_nd_chembert_embeddings_v4_5.csv: Data augmentation for train.csv, all external data mentioned above (except polyone), and NeurIPS datasets (1, 2, 3, 4), with 5 oligomers per monomer, with Descriptors and ChemBERT Embeddings. Added during version 4 of this Kaggle dataset. 4. augmented_data_with_descriptors_v8_5.csv: Data augmentation for train.csv, all external data mentioned above (with polyone), and NeurIPS datasets (1, 2, 3, 4), with 5 oligomers per monomer, with Descriptors. Added during version 8 of this Kaggle dataset. And, oligomer creation isn't done with the polyone dataset. 5. augmented_data_with_descriptors_nd_polybert_embeddings_v6_5.csv: Data augmentation for train.csv, all external data mentioned above (except polyone), and NeurIPS datasets (1, 2, 3, 4), with 5 oligomers per monomer, with Descriptors and PolyBERT Embeddings. Added during version 6 of this Kaggle dataset.

# https://www.kaggle.com/datasets/minatoyukinaxlisa/tc-smiles

data_tc = pd.read_csv('/kaggle/input/tc-smiles/Tc_SMILES.csv')

data_tc = data_tc.rename(columns={'TC_mean': 'Tc'})

# https://springernature.figshare.com/articles/dataset/dataset_with_glass_transition_temperature/24219958?file=42507037

data_tg2 = pd.read_csv('/kaggle/input/smiles-extra-data/JCIM_sup_bigsmiles.csv', usecols=['SMILES', 'Tg (C)'])

data_tg2 = data_tg2.rename(columns={'Tg (C)': 'Tg'})

# https://www.sciencedirect.com/science/article/pii/S2590159123000377#ec0005

data_tg3 = pd.read_excel('/kaggle/input/smiles-extra-data/data_tg3.xlsx')

data_tg3 = data_tg3.rename(columns={'Tg [K]': 'Tg'})

data_tg3['Tg'] = data_tg3['Tg'] - 273.15

# https://github.com/Duke-MatSci/ChemProps

data_dnst = pd.read_excel('/kaggle/input/smiles-extra-data/data_dnst1.xlsx')

data_dnst = data_dnst.rename(columns={'density(g/cm3)': 'Density'})[['SMILES', 'Density']]

data_dnst['SMILES'] = data_dnst['SMILES'].apply(lambda s: make_smile_canonical(s))

data_dnst = data_dnst[(data_dnst['SMILES'].notnull())&(data_dnst['Density'].notnull())&(data_dnst['Density'] != 'nylon')]

data_dnst['Density'] = data_dnst['Density'].astype('float64')

data_dnst['Density'] -= 0.118

data_pm1_tg_tc = pd.read_csv('/kaggle/input/extra-data-11k-10k-features/train_pm1_tg_tc_new_11k.csv')

def add_extra_data(df_train, df_extra, target):

n_samples_before = len(df_train[df_train[target].notnull()])

df_extra['SMILES'] = df_extra['SMILES'].apply(lambda s: make_smile_canonical(s))

df_extra = df_extra.groupby('SMILES', as_index=False)[target].mean()

cross_smiles = set(df_extra['SMILES']) & set(df_train['SMILES'])

unique_smiles_extra = set(df_extra['SMILES']) - set(df_train['SMILES'])

# Make priority target value from competition's df

for smile in df_train[df_train[target].notnull()]['SMILES'].tolist():

if smile in cross_smiles:

cross_smiles.remove(smile)

# Imput missing values for competition's SMILES

for smile in cross_smiles:

df_train.loc[df_train['SMILES']==smile, target] = df_extra[df_extra['SMILES']==smile][target].values[0]

df_train = pd.concat([df_train, df_extra[df_extra['SMILES'].isin(unique_smiles_extra)]], axis=0).reset_index(drop=True)

n_samples_after = len(df_train[df_train[target].notnull()])

print(f'

For target "{target}" added {n_samples_after-n_samples_before} new samples!')

print(f'New unique SMILES: {len(unique_smiles_extra)}')

return df_train

train = add_extra_data(train, data_tc, 'Tc')

train = add_extra_data(train, data_tg2, 'Tg')

train = add_extra_data(train, data_tg3, 'Tg')

train = add_extra_data(train, data_dnst, 'Density')

train = add_extra_data(train, data_pm1_tg_tc, 'Tg')

train = add_extra_data(train, data_pm1_tg_tc, 'Tc')

train = add_extra_data(train, data_pm1_tg_tc, 'Density')

train = add_extra_data(train, data_pm1_tg_tc, 'Rg')

print('

'*3, '--- SMILES for training ---', )

for t in CFG.TARGETS:

print(f'"{t}": {len(train[train[t].notnull()])}')

LitChemPlast database

The objective of this database was to map chemo-analytical measurements of chemicals in plastics and to compile their data in a comprehensive manner. Studies reporting the measured chemical composition of plastic raw materials (e.g., granulate, nurdles), products (e.g., agricultural, building and construction - B&C, packaging plastics), and waste were considered relevant.

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.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This resource consists of methods for matrix-assisted laser desorption ionization (MALDI) mass spectrometry on a wide variety of synthetic polymers. The methods are taken from the peer-review scientific literature. The database covers the period from 1988, the year of Tanaka's first paper on the subject, through 2011. Each recipe comes with a literature citation and associated digital object identifier (DOI) when available. The database currently contains entries for over 1250 polymer/matrix combinations. These methods have not necessarily been tested by the National Institute of Standards and Technology (NIST), nor does NIST claim the database to be a comprehensive list of all methods to be found in the open literature. This database is provided solely as a resource for the mass spectroscopy community. All polymers are sorted into five main groups: A - polymers containing carbon and hydrogen only B - polymers containing carbon, hydrogen and oxygen (but not nitrogen) C - polymers containing carbon, hydrogen, and nitrogen (oxygen may also be present) D - polymers containing sulfur, phosphorous, or halides E - polymers containing one or more of: silicon, germanium, tin, or metals Within these groups, the polymers are further sorted into classes according to 'Glossary of Class Names of Polymers Based on Chemical Structure and Molecular Architecture (IUPAC Recommendations 2009)' Pure Appl. Chem., Vol. 81, No. 6, pp. 1131-1186, 2009. doi: http://dx.doi.org/10.1351/PAC-REC-08-01-30. Not every IUPAC class contains a MALDI recipe. This occurs for a number of reasons, but primarily because there are no published methods for many classes. The database is organized by main chain chemistry. Copolymers can be found under all classes represented by repeat units in their main chain. End group and chair architecture information is available but not as a sortable field. There are entries for dendrimers, nanoparticles, polysaccharides, and homopolymer polypeptides, but the database has significantly less coverage in these areas. The database could be extended into these areas if there is significant community interest. Chemical Abstracts Service (CAS) number are provided for all matrices having them. Links to the NIST Chemistry WebBook are available for all matrices having a WebBook entry. The WebBook provides thermophysical, structural, and spectroscopic information a wide variety of compounds.

The data in the field of phase equilibrium of polymer solution plays an important role for describing the phase behavior of polymer-containing systems. The Polymer Solution Vapor-Liquid Equilibria Database collected the vapor-liquid equilibria and solvent infinite dilution experimental data from 22 periodicals, covering the polymer solution systems composed of 1500 polymers and 240 solvents. Standardization and pragmaticalness for the vapor-liquid equilibria data and solvent infinite dilution were carried out, in order to improve the applicability of data. The solvent infinite dilution and vapor-liquid equilibrium data were processed as solvent weight fraction activity coefficients at infinite dilution and solvent activities with vapor phase nonideality corrected. Furthermore, the UNIQUAC activity coefficient model was extended to correlate the polymer solution vapor-liquid equilibrium data. The Polymer Solution Vapor-Liquid Equilibrium Database can provide the originally measured data, standardized values and correlation parameters of UNIQUAC model.

Descriptions

  Converting script

import pickle from pathlib import Path

import numpy as np from datasets import Dataset

DATA_DIR = Path("/path/to/cached/hugging_face/datasets/for/MLDS-NUS/Experimental_Images")

should end with something like "snapshots/fd299418e9435f8fd98956a3f0a7344d208cc142"

def calc_left_right(data: np.ndarray): left_rights = [] for im in data: nonzero_columns = (im != 0).any(axis=-2) left = nonzero_columns.argmax() if… See the full description on the dataset page: https://huggingface.co/datasets/MLDS-NUS/polymer-dynamics_experimental-data.

These posters describe a use of information extraction and crowdsourcing to populate a database of polymer properties.

We have developed a polymer descriptor data set from the existing data. The data set has 188 descriptors which describe polymer atomic and molecular behavior. The descriptors are mapped to the specific heat of polymers using supervised and unsupervised machine learning approaches. The mapping helps predict the specific heat of polymers at room temperature. The descriptor data set is useful in synthesizing novel polymers with desired heat capacities.

Bahrain Imports from France of Ion-exchangers Based on Polymers of Ethylene or Natural Polymer was US$139.05 Thousand during 2023, according to the United Nations COMTRADE database on international trade.

Investigating State of the Art Hyperspectral Imaging Classification Models for Plastic Types Identification

Polymers Dataset

Description:

The polymers dataset is a multiscene Hyperspectral benchmark dataset comprising of reference polymer samples and shredded polymer samples in the visible to short-wave infrared, capturing 450 bands within the [400–2500] nm range using an AisaFENIX (Spectral Imaging Ltd, Oulu, Finland) spectrometer.

Two sample batches were investigated:

• reference polymers of known composition and dimensions (15 X 10) cm, commonly found in e-waste.
• shredded pieces of polymers with sizes ranging from (0.3–4) cm.

Data Format

• HSI data: each hyperspectral data cube is accompanied by a data file and a .hdr file.
• Ground truth mask: .png file (only for multi samples scenes)

Folder Organization

• Polymers
  • Test
    • HSI: .dat & .hdr
    • Ground truth mask: test.png
    • False colour representation of the scene: .png
  • Train
    • HSI_ : 3 different scans of reference samples scanned
    • PC, PE, PET, PP: Hyperspectral cubes (11x11x450) .hdr & .dat

Data Classes in Masks

• Masks contain 1 to 6 segmentation classes:
  • 1: "PP"
  • 2: "Black Plastic"
  • 3: "PVC"
  • 4: "PET"
  • 5: "ABS"
  • 6: "PE"

Code Repository

To facilitate reading and working with the data, Python codes are available on the GitHub repository:

https://github.com/hifexplo

https://github.com/Elias-Arbash

Citation

If you use this dataset, please cite the following article: (To be filled once published)

Contact

For further information or inquiries, please visit our website:

https://www.iexplo.space/

Contact Email: e.arbash@hzdr.de

Slovakia Imports from Germany of Ion-exchangers Based on Polymers of Ethylene or Natural Polymer was US$994.52 Thousand during 2024, according to the United Nations COMTRADE database on international trade.

Polymer engineering and science Acceptance Rate - ResearchHelpDesk - Polymer engineering and science - Every day, the Society of Plastics Engineers (SPE) takes action to help companies in the plastics industry succeed. How? By spreading knowledge, strengthening skills and promoting plastics. Employing these vital strategies, Polymer engineering and science - SPE has helped the plastics industry thrive for over 60 years. In the process, we've developed a 25,000-member network of leading engineers and other plastics professionals, including technicians, salespeople, marketers, retailers, and representatives from tertiary industries. For more than 30 years, Polymer Engineering & Science has been one of the most highly regarded journals in the field, serving as a forum for authors of treatises on the cutting edge of polymer science and technology. The importance of PE&S is underscored by the frequent rate at which its articles are cited, especially by other publications - literally thousands of times a year. Engineers, researchers, technicians, and academicians worldwide are looking to PE&S for the valuable information they need. There are special issues compiled by distinguished guest editors. These contain proceedings of symposia on such diverse topics as polyblends, mechanics of plastics and polymer welding. Abstracting and Indexing Information Academic ASAP (GALE Cengage) Advanced Technologies & Aerospace Database (ProQuest) Applied Science & Technology Index/Abstracts (EBSCO Publishing) CAS: Chemical Abstracts Service (ACS) CCR Database (Clarivate Analytics) Chemical Abstracts Service/SciFinder (ACS) Chemistry Server Reaction Center (Clarivate Analytics) ChemWeb (ChemIndustry.com) Chimica Database (Elsevier) COMPENDEX (Elsevier) Current Contents: Engineering, Computing & Technology (Clarivate Analytics) Current Contents: Physical, Chemical & Earth Sciences (Clarivate Analytics) Expanded Academic ASAP (GALE Cengage) InfoTrac (GALE Cengage) Journal Citation Reports/Science Edition (Clarivate Analytics) Materials Science & Engineering Database (ProQuest) PASCAL Database (INIST/CNRS) Polymer Library (iSmithers RAPRA) ProQuest Central (ProQuest) ProQuest Central K-462 Reaction Citation Index (Clarivate Analytics) Research Library (ProQuest) Research Library Prep (ProQuest) Science Citation Index (Clarivate Analytics) Science Citation Index Expanded (Clarivate Analytics) SciTech Premium Collection (ProQuest) SCOPUS (Elsevier) STEM Database (ProQuest) Technology Collection (ProQuest) Web of Science (Clarivate Analytics)

Polymer engineering and science - ResearchHelpDesk - Polymer engineering and science - Every day, the Society of Plastics Engineers (SPE) takes action to help companies in the plastics industry succeed. How? By spreading knowledge, strengthening skills and promoting plastics. Employing these vital strategies, Polymer engineering and science - SPE has helped the plastics industry thrive for over 60 years. In the process, we've developed a 25,000-member network of leading engineers and other plastics professionals, including technicians, salespeople, marketers, retailers, and representatives from tertiary industries. For more than 30 years, Polymer Engineering & Science has been one of the most highly regarded journals in the field, serving as a forum for authors of treatises on the cutting edge of polymer science and technology. The importance of PE&S is underscored by the frequent rate at which its articles are cited, especially by other publications - literally thousands of times a year. Engineers, researchers, technicians, and academicians worldwide are looking to PE&S for the valuable information they need. There are special issues compiled by distinguished guest editors. These contain proceedings of symposia on such diverse topics as polyblends, mechanics of plastics and polymer welding. Abstracting and Indexing Information Academic ASAP (GALE Cengage) Advanced Technologies & Aerospace Database (ProQuest) Applied Science & Technology Index/Abstracts (EBSCO Publishing) CAS: Chemical Abstracts Service (ACS) CCR Database (Clarivate Analytics) Chemical Abstracts Service/SciFinder (ACS) Chemistry Server Reaction Center (Clarivate Analytics) ChemWeb (ChemIndustry.com) Chimica Database (Elsevier) COMPENDEX (Elsevier) Current Contents: Engineering, Computing & Technology (Clarivate Analytics) Current Contents: Physical, Chemical & Earth Sciences (Clarivate Analytics) Expanded Academic ASAP (GALE Cengage) InfoTrac (GALE Cengage) Journal Citation Reports/Science Edition (Clarivate Analytics) Materials Science & Engineering Database (ProQuest) PASCAL Database (INIST/CNRS) Polymer Library (iSmithers RAPRA) ProQuest Central (ProQuest) ProQuest Central K-462 Reaction Citation Index (Clarivate Analytics) Research Library (ProQuest) Research Library Prep (ProQuest) Science Citation Index (Clarivate Analytics) Science Citation Index Expanded (Clarivate Analytics) SciTech Premium Collection (ProQuest) SCOPUS (Elsevier) STEM Database (ProQuest) Technology Collection (ProQuest) Web of Science (Clarivate Analytics)

Slovakia Imports from Czech Republic of Ion-exchangers Based on Polymers of Ethylene or Natural Polymer was US$49.52 Thousand during 2024, according to the United Nations COMTRADE database on international trade.