Lucía Morán González

• Solé-Daura, Albert; González, Lucía Morán; Apaloo-Messan, Edmond & Maseras, Feliu (2026). EASIQ-Marcus: An Automated Workflow for Applying Marcus Theory to Computational Chemistry Output Files. Journal of Computational Chemistry. ISSN 0192-8651. 47(27). doi: 10.1002/jcc.70513.
• 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, s. 9089–9105. doi: 10.1021/acscatal.5c01202. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Gonzalez, Lucia Moran; Betten, Jørn Eirik; Kneiding, Hannes & Badia, David Balcells (2024). AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning. Journal of Chemical Information and Modeling. ISSN 1549-9596. 64(23), s. 8756–8769. doi: 10.1021/acs.jcim.4c01583. Fulltekst i vitenarkiv Vis sammendrag

  Graphs are one of the most natural and powerful representations available for molecules; natural because they have an intuitive correspondence to skeletal formulas, the language used by chemists worldwide, and powerful, because they are highly expressive both globally (molecular topology) and locally (atom and bond properties). Graph kernels are used to transform molecular graphs into fixed-length vectors, which, based on their capacity of measuring similarity, can be used as fingerprints for machine learning (ML). To date, graph kernels have mostly focused on the atomic nodes of the graph. In this work, we developed a graph kernel based on atom-atom, bond-bond, and bond-atom (AABBA) autocorrelations. The resulting vector representations were tested on regression ML tasks on a data set of transition metal complexes; a benchmark motivated by the higher complexity of these compounds relative to organic molecules. In particular, we tested different flavors of the AABBA kernel in the prediction of the energy barriers and bond distances of the Vaska’s complex data set (Friederich et al., Chem. Sci., 2020, 11, 4584). For a variety of ML models, including neural networks, gradient boosting machines, and Gaussian processes, we showed that AABBA outperforms the baseline including only atom-atom autocorrelations. Dimensionality reduction studies also showed that the bond-bond and bond-atom autocorrelations yield many of the most relevant features. We believe that the AABBA graph kernel can accelerate the exploration of large chemical spaces and inspire novel molecular representations in which both atomic and bond properties play an important role.

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• González, Lucía Morán (2026). Mini-Symposium for Machine Learning in Chemistry. doi: https:/memento.epfl.ch/event/mini-symposium-for-machine-learning-in-chemistry/. Fulltekst i vitenarkiv
• González, Lucía Morán; Kneiding, Hannes; Badia, David Balcells & Flores, Ainara Nova (2026). High-Throughput Computational Screening of Bifunctional Catalysts for CO₂ Hydrogenation. doi: https:/ccsc2026.github.io/. Fulltekst i vitenarkiv
• Gonzalez, Lucia Moran; Kneiding, Hannes; Balcells, David & Nova, Ainara (2024). Seeking promising bifunctional catalysts for CO2 hydrogenation using active learning. Fulltekst i vitenarkiv
• Gonzalez, Lucia Moran; Kneiding, Hannes; Balcells, David & Nova, Ainara (2024). Integrating in silico design and data-driven strategies for advanced homogeneous bifunctional catalysts in the CO2 to methanol reaction. Fulltekst i vitenarkiv

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