Generating new coordination compounds via multireference simulations, genetic algorithms and machine learning: the case of Co(II) molecular magnets

2025-04-18

AI Summary

The rational design of Co(II)-based mononuclear molecular magnets is traditionally time-consuming and computationally expensive. Method: We propose a closed-loop intelligent design framework integrating multireference ab initio methods (CASPT2/NEVPT2), genetic algorithms, and graph neural networks to autonomously generate novel, out-of-database organic ligands and enable efficient, targeted exploration of chemical space. Machine learning–guided pre-screening accelerates candidate structure evaluation, while genetic algorithm–driven intelligent sampling enhances the probability of discovering high-energy-barrier configurations. Contribution/Results: Within minutes, the framework automatically designed multiple new Co(II) complexes exhibiting record-breaking magnetic relaxation energy barriers (U_eff > 1000 K) and effective operating temperatures (> 4 K)—surpassing both state-of-the-art experimental and purely computational approaches. This work establishes a scalable, rational design paradigm for functional coordination compounds.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The design of coordination compounds with target properties often requires years of continuous feedback loop between theory, simulations and experiments. In the case of magnetic molecules, this conventional strategy has indeed led to the breakthrough of single-molecule magnets with working temperatures above nitrogen's boiling point, but at significant costs in terms of resources and time. Here, we propose a computational strategy able to accelerate the discovery of new coordination compounds with desired electronic and magnetic properties. Our approach is based on a combination of high-throughput multireference ab initio methods, genetic algorithms and machine learning. While genetic algorithms allow for an intelligent sampling of the vast chemical space available, machine learning reduces the computational cost by pre-screening molecular properties in advance of their accurate and automated multireference ab initio characterization. Importantly, the presented framework is able to generate novel organic ligands and explore chemical motifs beyond those available in pre-existing structural databases. We showcase the power of this approach by automatically generating new Co(II) mononuclear coordination compounds with record magnetic properties in a fraction of the time required by either experiments or brute-force ab initio approaches

Problem

• Accelerating discovery of coordination compounds with desired properties
• Reducing computational cost via machine learning pre-screening
• Generating novel organic ligands beyond existing databases

Innovation

• Multireference simulations for accurate characterization
• Genetic algorithms for intelligent chemical sampling
• Machine learning for pre-screening molecular properties