Modular Software for Generating and Modeling Diverse Polymer Databases.

ABSTRACT: Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale; however, building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modeling workflows are therefore becoming essential tools in this data-driven hunt for new materials with novel properties, since they offer a means by which to create and curate molecular databases without requiring significant levels of user input. This ensures that well-founded concerns regarding data provenance, reproducibility, and replicability are mitigated. We have developed a versatile and flexible software package, PySoftK (Python Soft Matter at King's College London) that provides flexible, automated computational workflows

SUBMITTER: Santana-Bonilla A 

PROVIDER: S-EPMC10302471 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature