Journal article
Modelling ligand exchange in metal complexes with machine learning potentials
- Abstract:
- Metal ions are irreplaceable in many areas of chemistry, including (bio)catalysis, self-assembly and charge transfer processes. Yet, modelling their structural and dynamic properties in diverse chemical environments remains challenging for both force fields and ab initio methods. Here, we introduce a strategy to train machine learning potentials (MLPs) using MACE, an equivariant message-passing neural network, for metal–ligand complexes in explicit solvents. We explore the structure and ligand exchange dynamics of Mg2+ in water and Pd2+ in acetonitrile as two illustrative model systems. The trained potentials accurately reproduce equilibrium structures of the complexes in solution, including different coordination numbers and geometries. Furthermore, the MLPs can model structural changes between metal ions and ligands in the first coordination shell, and reproduce the free energy barriers for the corresponding ligand exchange. The strategy presented here provides a computationally efficient approach to model metal ions in solution, paving the way for modelling larger and more diverse metal complexes relevant to biomolecules and supramolecular assemblies.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.2MB, Terms of use)
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- Publisher copy:
- 10.1039/d4fd00140k
Authors
+ Engineering and Physical Sciences Research Council
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- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/P020267/1
- Publisher:
- Royal Society of Chemistry
- Journal:
- Faraday Discussions More from this journal
- Volume:
- 256
- Pages:
- 156-176
- Place of publication:
- England
- Publication date:
- 2024-08-03
- Acceptance date:
- 2024-07-31
- DOI:
- EISSN:
-
1364-5498
- ISSN:
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1359-6640
- Pmid:
-
39308396
- Language:
-
English
- Pubs id:
-
2032264
- Local pid:
-
pubs:2032264
- Deposit date:
-
2025-02-28
Terms of use
- Copyright holder:
- Juraskova et al
- Copyright date:
- 2024
- Rights statement:
- © 2024 The Authors. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
- Licence:
- CC Attribution (CC BY) 3.0
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