MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations

Molecular modeling of synthetic polymers is today increasingly dependent on automation, high-throughput exploration, and the use of a diverse set of simulation tools. However, despite notable progress in molecular simulation methods, the practical infrastructure for constructing, modifying, and parametrizing polymer systems remains fragmented. Here, we launch a toolkit, MolPy, which aims to bridge this gap by providing an extensible and chemically intuitive platform for building polymer systems and orchestrating simulation workflows. The design philosophy emphasizes a clear separation of responsibilities, a stable and inspectable execution model, and a modular architecture that enables flexible extensions. In practice, the framework supports the introduction of new chemical building blocks and bonding schemes that automatically participate in construction, topology generation, and force-field assignment without requiring modification of existing modules. Interoperability with established simulation tools positions MolPy to support streamlined polymer modeling workflows that remain compatible with emerging large language model (LLM)-driven orchestration and other forms of automated pipeline construction.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-06-30
DOI
https://doi.org/10.1021/acs.jcim.6c01137
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations

Jichen Li, Wentao Guo, Daniel Brandell, Fabian Schwarz et al.
Journal of Chemical Information and Modeling
Machine Learning in Materials Science
article

MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations

Jichen Li, Wentao Guo, Daniel Brandell, Fabian Schwarz, Ge Sun
article en

Abstract

Molecular modeling of synthetic polymers is today increasingly dependent on automation, high-throughput exploration, and the use of a diverse set of simulation tools. However, despite notable progress in molecular simulation methods, the practical infrastructure for constructing, modifying, and parametrizing polymer systems remains fragmented. Here, we launch a toolkit, MolPy, which aims to bridge this gap by providing an extensible and chemically intuitive platform for building polymer systems and orchestrating simulation workflows. The design philosophy emphasizes a clear separation of responsibilities, a stable and inspectable execution model, and a modular architecture that enables flexible extensions. In practice, the framework supports the introduction of new chemical building blocks and bonding schemes that automatically participate in construction, topology generation, and force-field assignment without requiring modification of existing modules. Interoperability with established simulation tools positions MolPy to support streamlined polymer modeling workflows that remain compatible with emerging large language model (LLM)-driven orchestration and other forms of automated pipeline construction.

Journal of Chemical Information and Modeling
California Institute of Technology (US), Uppsala University (SE), Courant Institute of Mathematical Sciences (US), Division of Chemistry (US), Tân Tạo University (VN), New York University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 16%
Machine Learning in Materials Science
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