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Explore advanced multiscale modeling techniques for battery systems using symbolic-numeric computing to bridge scales from atomistic to continuum levels in electrochemical applications.
Explore microstructural descriptors and correlation functions to characterize disordered materials, predict transport properties, and enable inverse design for electrochemical systems.
Discover how structure influences catalytic function in nanoparticles through advanced computational methods and theoretical frameworks for electrochemical systems.
Discover how to learn interaction laws in particle-based systems through advanced mathematical techniques for bridging atomistic to continuum scales in electrochemical applications.
Explore advanced computational methods for electrocatalytic modeling, from limiting-potential frameworks to machine learning approaches for predictive electrochemical simulations.
Discover how active learning techniques improve VASPsol computational accuracy for modeling non-aqueous solvents in electrochemical systems and materials science applications.
Explore advanced first-principles modeling techniques for electrocatalysis beyond potential of zero charge, covering implicit solvation, grand canonical calculations, and CV predictions.
Discover how to extract physics-based models from electrochemical data using PDE-constrained optimization and Bayesian inference for lithium-ion battery materials and biological systems.
Explore multiscale modeling techniques for electrochemical systems including fuel cells, batteries, and dialysis systems with empirical membrane and electrode behavior fitting.
Explore phase-field modeling techniques for predicting microstructural changes in corroding materials like magnesium alloys and nickel-chromium in various environments.
Discover how radiation-induced defects in actinide oxides affect surface chemistry and nuclear fuel corrosion through first-principles simulations and electron localization analysis.
Explore ab-initio simulations of electrified interfaces, covering electrode potential embedding, double-layer physics, and machine-learning potentials for electrochemical modeling.
Explore electrochemical interface fluctuations and their reaction impacts through ab initio molecular dynamics, featuring Pt and Mg surfaces with potential control methods.
Explore how electric double layers in complex electrolytes affect electrochemical reactions using molecular dynamics and quantum calculations for battery and synthesis applications.
Explore how electric fields influence viscous fingering patterns in Hele-Shaw cells through mathematical modeling and simulations of electro-osmotic flows.
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