This project aligns closely with the research objectives of the newly established Stenlid Lab at Chalmers University of Technology and pursues three interconnected goals: (1) to develop and validate quantum chemical methods for understanding charge-transfer reactions at materials and water interfaces; (2) to explore surface reactions relevant to application in electrochemical conversion of lignin derivatives, nanostructured electrode design for fast-charging Li-ion batteries, and greenhouse gas capture using reactive water nanodroplets; and (3) to train physics-based machine-learning (ML) models for rapid prediction of surface properties that govern site-specific chemical reactivity. The computational framework developed will leverage first-principles modeling of electrochemical reactions, explicitly accounting for applied potentials via grand canonical density functional theory (DFT), solvent dynamics and character through implicit and explicit solvation models, and the impact of surface nanostructure. Additionally, efficient modeling approaches for radical chemistry occurring at water-vacuum interfaces will be established and validated using molecular dynamics simulations combined with embedded quantum mechanics/molecular mechanics (QM/MM) cluster methods, employing DFT or advanced multireference quantum chemistry approaches for the quantum mechanical region. These customized methods will integrate state-of-the-art computational strategies to study practical applications in electrocatalysis, battery technology, and greenhouse gas capture. Within electrocatalysis, we will initially focus on reductive (and later oxidative) processes to achieve depolymerization and valorization of lignin. We plan detailed investigations into dimer and monomer model systems, analyzing how local surface morphology and transition-metal alloy composition influence reaction kinetics and selectivity toward high-value aromatic products. In the context of batteries, the rate of charge transfer at electrode-electrolyte interfaces significantly affects battery performance, including fast charging capabilities, power density, and long-term cyclability. We aim to establish nanoscale design principles to engineer innovative nanostructured electrode materials, specifically anode materials based on lithium and lithium alloys and cathodes composed of CFx, optimized for fast charging, high-power, and high cyclability. Additionally, we will exploit the increased chemical reactivity of water nanodroplets (relative bulk water) in the context of greenhouse gas capture and conversion. This includes evaluating solvent engineering strategies and exploring potential synergies between the intrinsic reactivity of nanodroplets and heterogeneous catalytic surfaces. Supporting these studies, we will develop physics-based ML models capable of rapidly predicting local reactivity characteristics from readily accessible structural and compositional surface information. These ML-derived insights will feed directly into the development of design guidelines established within our core research areas. Our primary ML approaches will include graph neural networks (GNNs) and Gaussian process regression (GPR). The necessary training and validation data will be sourced from existing public databases or generated internally through dedicated DFT simulations. Through these interconnected activities, the project will establish versatile computational tools, deliver foundational insights into crucial surface reactions, and provide predictive capabilities critical for advancing materials science research at Chalmers and beyond.