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 language | English |
|---|---|
| Pages (from-to) | 5525-5535 |
| Number of pages | 11 |
| Journal | Journal of chemical information and modeling |
| Volume | 62 |
| Issue number | 22 |
| Early online date | 31 Oct 2022 |
| DOIs | |
| Publication status | Published - 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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