PhD student _MSCA-DN e-ChemIn (DC5): In-silico reconstruction of solid-liquid and solid-solid electrochemical interfaces at the nanoscale

The aim of this project is developing inverse modelling and optimization techniques that leverage experimental data to construct detailed 3D representations of complex interfaces in energy storage and conversion devices at the atomistic level. These include solid-liquid (SL) interfaces (e.g., between carbonaceous anodes and ionic liquid electrolytes in next-generation Na-ion batteries), and the solid-solid (SS) interfaces (e.g., between metal oxide catalysts and carbon support materials to improve catalytic performance in water oxidation reactions). To achieve this goal, we will leverage BCAM’s enhanced Bayesian sampling techniques, generalized hybrid Monte Carlo (MC) schemes, and adaptive integration methods designed to accelerate atomistic simulations. The approach to be developed will initially integrate spectroscopy data (XPS, SAX, SXRD) to generate candidate structures for S-S and S-L interfaces, which are subsequently refined using high-resolution microscopy (EELS, TEM) and NMR measurements. We will then deploy these refined, high-fidelity models to analyse the structural evolution of materials during electrochemical processes and to characterize interfacial ionic transport. Expected results: (1) A robust methodology for the in-silico reconstruction of complex materials implemented in an open-source code with a user-friendly interface. (2) Atomistic models free from standard idealization (e.g. defect-free, planar surfaces), with unprecedented detail of the interface chemistry, morphology. (3) A fundamental description of how typically overlook features, e.g. substrate heterogeneity and surface corrugation, shape interface performance in storage and conversion devices.