An evolutionary algorithm for de novo optimization of functional transition metal compounds.

Area of Science:

• Inorganic Chemistry
• Computational Chemistry
• Catalysis

Background:

• Developing functional inorganic and transition metal compounds often relies on intuition rather than computational methods.
• Existing methods for catalyst discovery are often ad hoc, limiting systematic exploration.

Purpose of the Study:

• To develop a de novo evolutionary algorithm (EA) for automated generation of transition metal complexes.
• To explore the potential of EA in designing novel catalysts with improved activity.

Main Methods:

• An evolutionary algorithm (EA) was designed to generate transition metal complexes from predefined fragments.
• A cost-efficient fitness function based on a quantitative structure-activity relationship (QSAR) model for catalytic activity was employed.
• Density Functional Theory (DFT) calculations were used to evaluate the catalytic activity of evolved structures.

Main Results:

• The EA successfully retraced the development from first-generation phosphine-based Grubbs catalysts to second-generation N-heterocyclic carbene (NHC) based catalysts.
• Evolved structures showed catalytic activity comparable to existing highly active catalysts.
• The generated structures were complex, suggesting potential synthesis challenges but offering leads for new catalyst designs.

Conclusions:

• The presented EA provides a powerful tool for the automated de novo design of transition metal catalysts.
• Virtual evolution can generate catalytically relevant structures, complementing traditional design approaches.
• Simplified variations of evolved structures may lead to a new generation of highly active Grubbs catalysts.