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PI1M: A Benchmark Database for Polymer Informatics.

Ruimin Ma1, Tengfei Luo1,2

  • 1Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States.

Journal of Chemical Information and Modeling
|September 28, 2020
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Summary
This summary is machine-generated.

A new benchmark database, PI1M (∼1 million polymers for polymer informatics), was created using a generative model. This database enhances machine learning research in polymer informatics by expanding data availability.

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Area of Science:

  • Polymer Science
  • Materials Informatics
  • Computational Chemistry

Background:

  • Large-scale open-source data are crucial for data-driven research but are scarce for polymers.
  • Existing polymer databases lack comprehensive coverage for machine learning applications.

Purpose of the Study:

  • To develop a benchmark database, PI1M (∼1 million polymers for polymer informatics), for machine learning in polymer informatics.
  • To introduce a novel polymer representation, polymer embedding (PE), for improved data analysis.
  • To assess the utility of PI1M in populating sparse regions of chemical space.

Main Methods:

  • A generative model was trained on ∼12,000 polymers from the PolyInfo database.
  • The generative model was employed to create a dataset of ∼1 million polymers (PI1M).
  • A new polymer representation, polymer embedding (PE), was developed and applied to regression tasks (density, glass transition temperature, melting temperature, dielectric constants).

Main Results:

  • The PI1M database was successfully generated, containing approximately one million polymers.
  • The polymer embedding (PE) representation was effective for various polymer informatics regression tasks.
  • Comparison of PE models trained on PolyInfo and PI1M data indicated that PI1M covers a similar chemical space but significantly populates sparse regions.

Conclusions:

  • The PI1M database provides valuable data resources for machine learning in polymer informatics.
  • The PI1M database complements existing resources by expanding data coverage in underrepresented chemical spaces.
  • PI1M is expected to serve as a crucial benchmark for advancing polymer informatics research.