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CAT: A Compound Attachment Tool for the Construction of Composite Chemical Compounds

  • Bas van Beek
  • , Juliette Zito
  • , Lucas Visscher*
  • , Ivan Infante
  • *Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

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Abstract

The continuous improvement of computer architectures allows for the simulation of molecular systems of growing sizes. However, such calculations still require the input of initial structures, which are also becoming increasingly complex. In this work, we present CAT, a Compound Attachment Tool (source code available at https://github.com/nlesc-nano/CAT) and Python package for the automatic construction of composite chemical compounds, which supports the functionalization of organic, inorganic, and hybrid organic-inorganic materials. The CAT workflow consists in defining the anchoring sites on the reference material, usually a large molecular system denoted as a scaffold, and on the molecular species that are attached to it, i.e., the ligands. Usually, ligands are pre-optimized in a conformation biased toward more linear structures to minimize interligand(s) steric interactions, a bias that is important when multiple ligands are attached onto the scaffold. The resulting superstructure(s) are then stored in various formats that can be used afterward in quantum chemical calculations or classical force field-based simulations.

Original languageEnglish
Pages (from-to)5525-5535
Number of pages11
JournalJournal of chemical information and modeling
Volume62
Issue number22
Early online date31 Oct 2022
DOIs
Publication statusPublished - 28 Nov 2022

Bibliographical note

Funding Information:
The authors acknowledge The Netherlands Organization of Scientific Research (NWO) for financial support through the Computational Sciences for Energy Research (CSER) Joint CSER & eScience Research Programme 2017 grant with number 680-91-086. The computational work was carried out on the Dutch national e-infrastructure with the support of the SURFCooperative.

Publisher Copyright:
© 2022 American Chemical Society. All rights reserved.

Funding

The authors acknowledge The Netherlands Organization of Scientific Research (NWO) for financial support through the Computational Sciences for Energy Research (CSER) Joint CSER & eScience Research Programme 2017 grant with number 680-91-086. The computational work was carried out on the Dutch national e-infrastructure with the support of the SURFCooperative.

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