Generating New Coordination Compounds via Multireference Simulations, Genetic Algorithms, and Machine Learning: The
Lion Frangoulis1, Zahra Khatibi1, Lorenzo A Mariano1
1School of Physics, AMBER and CRANN Institute, Trinity College, Dublin 2, Ireland.
JACS Au
|August 29, 2025
Summary
This study introduces a computational strategy combining high-throughput methods, genetic algorithms, and machine learning to accelerate the discovery of novel magnetic coordination compounds, significantly reducing research time and resources.
Area of Science:
- Computational chemistry
- Materials science
- Magnetism
Background:
- Designing coordination compounds with specific magnetic properties typically involves a lengthy, iterative process of theory, simulation, and experimentation.
- This conventional approach, while successful in developing advanced materials like single-molecule magnets, is resource-intensive and time-consuming.
Purpose of the Study:
- To develop and demonstrate a computational strategy for accelerating the discovery of new coordination compounds with targeted electronic and magnetic properties.
- To reduce the time and resources required for designing novel magnetic molecules.
Main Methods:
- A hybrid computational approach integrating high-throughput multireference ab initio calculations, genetic algorithms for chemical space exploration, and machine learning for property prescreening.
- Utilizing genetic algorithms to intelligently sample potential molecular structures and machine learning to predict properties, thereby reducing the need for extensive ab initio calculations.
Main Results:
- The framework successfully generated novel organic ligands and explored chemical motifs beyond existing databases.
- New Cobalt(II) and Dysprosium(III) mononuclear coordination compounds with record magnetic properties were automatically synthesized computationally.
- Simulations revealed new strategies for creating pentagonal bipyramidal dysprosium complexes with exceptional magnetic anisotropy.
Conclusions:
- The proposed computational strategy significantly accelerates the discovery of coordination compounds with desired magnetic properties.
- This approach offers a more efficient alternative to traditional experimental and brute-force computational methods for designing advanced magnetic materials.
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