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• Strandgaard, Magnus; Linjordet, Trond; Kneiding, Hannes; Burnage, Arron Luc; Nova, Ainara & Jensen, Jan Halborg [Show all 7 contributors for this article] (2025). A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes. JACS Au. 5(5), p. 2294–2308. doi: 10.1021/jacsau.5c00242. Full text in Research Archive Show summary

  Deep generative models yielding transition metal complexes (TMCs) remain scarce despite the key role of these compounds in industrial catalytic processes, anticancer therapies, and the energy transition. Compared to drug discovery within the chemical space of organic molecules, TMCs pose further challenges, including the encoding of chemical bonds of higher complexity and the need to optimize multiple properties. In this work, we developed a generative model for the inverse design of transition metal ligands and complexes, based on the junction tree variational autoencoder (JT-VAE). After implementing a SMILES-based encoding of the metal–ligand bonds, the model was trained with the tmQMg-L ligand library, allowing for the generation of thousands of novel, highly diverse monodentate (κ1) and bidentate (κ2) ligands, including imines, phosphines, and carbenes. Further, the generated ligands were labeled with two target properties reflecting the stability and electron density of the associated homoleptic iridium TMCs: the HOMO–LUMO gap (ϵ) and the charge of the metal center (qIr). This data was used to implement a conditional model that generated ligands from a prompt, with the single- or dual-objective of optimizing either or both the ϵ and qIr properties and allowing for chemical interpretation based on the optimization trajectories. The optimizations also had an impact on other chemical properties, including ligand dissociation energies and oxidative addition barriers. A similar model was implemented to condition ligand generation by solubility and steric bulk.

• Burnage, Arron Luc; Kneiding, Hannes; Nova, Ainara & Badia, David Balcells (2025). Cross-coupling catalysts for inactive substrates from a three-dimensional virtual screening. Journal of Catalysis. ISSN 0021-9517. 452. doi: 10.1016/j.jcat.2025.116446. Full text in Research Archive Show summary

  We report a three-dimensional virtual screening method to search a chemical space formed of ligands from the tmQMg-L library for Pd Suzuki–Miyaura cross-coupling catalysts that has future potential to be applied to a broader range of catalytic reactions. It utilises three criteria related to important steps in the catalytic cycle of the reaction to distinguish between candidate Pd(0) species, namely the oxidative addition and reductive elimination barriers and the binding energy of acetonitrile. This method ultimately leads to identifying a region of chemical space that provides a good compromise between the three search criteria, highlighting a smaller set of ligands that merits further exploration. After the screening, several candidates are confirmed as catalysts based on literature searches. We also found Pd(0) complexes with high potential in catalytic cross-couplings involving less reactive substrates that require the oxidative addition of strong C–O bonds.

• Gonzalez, Lucia Moran; Burnage, Arron Luc; Flores, Ainara Nova & Badia, David Balcells (2025). AI Approaches to Homogeneous Catalysis with Transition Metal Complexes. ACS Catalysis. 15, p. 9089–9105. doi: 10.1021/acscatal.5c01202. Full text in Research Archive Show summary

  Artificial intelligence (AI) is transforming research in chemistry, including homogeneous catalysis with transition metals. Over the past 15 years, the number of publications combining AI with catalysis has increased exponentially, reflecting the interest and strength of this strategy in the field. Since this is a broad emerging discipline, it is essential to establish guidelines that clarify the diverse approaches already available. The complexity of the tasks that can be carried out with AI tools is directly linked to the nature of their components, including datasets, representations, algorithms, and high-throughput experimental and computational facilities. In parallel to the evolution of these tools, applications to catalysis have also advanced. Initially, models were developed to predict key aspects of the reaction mechanism, aiming at screening catalyst candidates. Subsequent studies have incorporated experimental data to optimize reaction conditions and yields. More recently, generative AI based on deep learning methods has enabled the inverse design of novel catalysts with predefined target properties. While most studies rely on computational data, recent advancements have improved the acquisition of experimental data, enabling AI-driven automated workflows. This Perspective gives a critical overview on selected studies that reflect the state of the art in the application of AI to homogeneous metal-catalyzed reactions, also highlighting future opportunities and challenges.

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• Burnage, Arron Luc; Kneiding, Hannes; Nova, Ainara & Balcells, David (2024). Cross-Coupling Catalysts for Inactive Substrates from a Three-Dimensional Virtual Screening. Full text in Research Archive
• Burnage, Arron Luc; Kneiding, Hannes; Nova, Ainara & Balcells, David (2023). A 3-Dimentional Search for a Pd Cross-Coupling Catalyst for the Suzuki-Miyaura Reaction. Full text in Research Archive

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