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<p>Chemical&#xa0;reactivity within DFT (CRDFT), also known as conceptual DFT (CDFT), provides a theoretical framework for understanding chemical reactivity through a set of descriptors derived from the electronic structure theory of matter. In this study we present PyCRDFT, a Python package developed to compute CDFT-based reactivity descriptors. The package extends the atomic simulation environment (ASE) atoms object to incorporate electronic properties necessary for descriptor evaluation and supports multiple models, including the Parr–Pearson model, the Two-Parabolas model, and several other formulations. PyCRDFT facilitates the automated calculation of chemical potential, hardness, softness, electrophilicity, nucleophilicity, Fukui functions, and other global and local reactivity indexes. It also enables interfacing with electronic structure codes for property calculations, drawing inspiration from ASE and the developing Atomic Simulation Recipes framework. To validate its implementation, we tested PyCRDFT on benchmark charge-transfer reactions, reproducing values reported in the literature. In addition, PyCRDFT offers automation tools for large-scale calculations, including scheduling, batch processing, and systematic reactivity analysis, making it a versatile tool for computational chemists. The code is openly available on GitLab (<a href="https://gitlab.com/izxle/pycrdft"><Emphasis FontCategory="NonProportional">https://gitlab.com/izxle/pycrdft</Emphasis></a>) and is open to collaboration.</p>

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PyCRDFT: A Python package to evaluate chemical reactivity descriptors within conceptual DFT

  • 期刊论文

  • 发表日期：

    2026年2月27日

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  • Oscar Xavier Guerrero-Gutiérrez,
  • Ashley Acosta-García,
  • Alberto Vela

摘要

Chemical reactivity within DFT (CRDFT), also known as conceptual DFT (CDFT), provides a theoretical framework for understanding chemical reactivity through a set of descriptors derived from the electronic structure theory of matter. In this study we present PyCRDFT, a Python package developed to compute CDFT-based reactivity descriptors. The package extends the atomic simulation environment (ASE) atoms object to incorporate electronic properties necessary for descriptor evaluation and supports multiple models, including the Parr–Pearson model, the Two-Parabolas model, and several other formulations. PyCRDFT facilitates the automated calculation of chemical potential, hardness, softness, electrophilicity, nucleophilicity, Fukui functions, and other global and local reactivity indexes. It also enables interfacing with electronic structure codes for property calculations, drawing inspiration from ASE and the developing Atomic Simulation Recipes framework. To validate its implementation, we tested PyCRDFT on benchmark charge-transfer reactions, reproducing values reported in the literature. In addition, PyCRDFT offers automation tools for large-scale calculations, including scheduling, batch processing, and systematic reactivity analysis, making it a versatile tool for computational chemists. The code is openly available on GitLab (https://gitlab.com/izxle/pycrdft) and is open to collaboration.