At CIC energiGUNE, the Atomistic Modeling for Catalysis (AMC) group advances the discovery of efficient, affordable, and sustainable catalytic materials to support Europe’s clean energy transition. By combining state-of-the-art computational chemistry with data-driven machine-learning approaches, the group delivers atomistic insights that connect fundamental understanding with real-world energy technologies.

Within the PeCATHS project, CIC energiGUNE co-leads the computational efforts of Work Package 3, focusing on the modeling of (photo)electrocatalytic materials under experimentally relevant operating conditions. This includes explicitly accounting for applied potential, pH, and temperature to identify realistic catalyst surface states and to predict their stability and reactivity. Establishing these resting states is a critical first step toward reliable mechanistic understanding and rational materials design.

Building on this foundation, the AMC group investigates reaction mechanisms relevant to reversible hydrogen storage and sustainable chemical production, including the (de)hydrogenation of liquid organic hydrogen carriers (LOHCs) and the electrochemical oxidation of biomass-derived molecules. Through these studies, the group identifies reaction pathways that lower the energy input relative to conventional water electrolysis, while pinpointing the rate-determining steps and reaction descriptors that control catalytic activity and selectivity.

To efficiently explore the vast chemical space of LOHCs and catalytic materials, CIC energiGUNE develops machine-learning-accelerated screening workflows. These data-driven tools enable the identification of promising candidates prior to experimental validation, significantly reducing development time and cost. The computational studies carried out within PeCATHS are supported by generous access to high-performance computing resources at the Barcelona Supercomputing Center (BSC) through the Spanish Network of Supercomputing (RES), enabling large-scale simulations and data generation. Close integration with experimental partners is also central to this approach: computational predictions are continuously validated and refined through a strong feedback loop with work performed by UJI, UZH, TCD, and ICN2-CERCA.

CIC energiGUNE works particularly closely with Trinity College Dublin (TCD), providing advanced modeling methodologies and machine-learning frameworks for the high-throughput screening of (photo)electrocatalytic materials and substrates. The atomistic insights obtained not only rationalize experimental observations across the consortium but also guide the proposal of new catalyst compositions and design principles. This tight coupling between theory, synthesis, and characterization fosters synergies across all stages of material development.

Beyond PeCATHS, the AMC group has strong expertise in the modeling of a wide range of materials as cost-effective thermal and electrochemical catalysts for industrially relevant reactions. Its combined experience in modeling both heterogeneous and molecular catalysts provides a unique versatility, enabling the group to address the diverse systems explored within the PeCATHS consortium.

Through its leadership in advanced simulation techniques, high-performance computing, and data science, the AMC group strengthens the scientific foundations of PeCATHS and contributes to the development of efficient, sustainable technologies in support of a carbon-neutral Europe.