Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Area of Science:

• Focuses on inorganic chemistry and materials science, specifically transition-metal complexes.
• Explores the intersection of computational chemistry, computer science, and experimental data.

Background:

• Transition-metal complexes are vital for catalysts and functional materials.
• Predicting metal-organic bond behavior is complex, requiring extensive searching.
• Discovering desirable complexes necessitates efficient exploration of chemical space.

Purpose of the Study:

• To review techniques enabling high-throughput searching of transition-metal chemical space.
• To highlight how computational chemistry and computer science advances accelerate discovery.
• To address unique challenges in transition-metal complex discovery compared to organic molecules.

Main Methods:

• Discusses traditional computational chemistry methods (force field, semiempirical, DFT) for data generation.
• Examines leveraging experimental data sources.
• Focuses on advances in statistical modeling, AI, multiobjective optimization, and automation.

Main Results:

• Demonstrates how integrating diverse computational approaches accelerates uncovering structure-property relationships.
• Highlights unique aspects of metal-organic bonding (spin, oxidation state, bond strength) influencing discovery.
• Identifies how data scarcity and uncertainty drive specific machine learning developments.

Conclusions:

• Advances in computational chemistry and computer science enable rapid discovery of transition-metal complexes.
• Addressing unique bonding characteristics and data challenges is key for future progress.
• Outlook provided on opportunities for accelerated discovery in this field.