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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.
Explore MISPR, an open-source computational framework integrating DFT, molecular dynamics, and machine learning for high-throughput electrolyte and electrode-electrolyte interface modeling.
Explore stochastic modeling and Monte Carlo simulation of electron transfer processes at electrode/electrolyte interfaces in electrochemical systems.
Explore atomic-scale electrochemical double layer properties through advanced modeling approaches including DFT, molecular dynamics, and machine learning techniques.
Explore stochastic interpolants for materials discovery using the Open Materials Generation framework to predict crystal structures and generate novel stable materials.
Explore quantum metric space structure on quantum compact groups, focusing on the Hamming metric analog on quantum permutation group S+n and the associated quantum 1-Wasserstein distance on tracial state space.
Delve into barrier relaxations of classical and quantum optimal transport problems, exploring entropic relaxations, Sinkhorn algorithms, and interior point methods for multi-partite transport challenges.
Delve into the convergence behavior of entropically regularized optimal transport, exploring geometric principles, selection mechanisms, and quantification methods for understanding its vanishing regularization limits.
Explore novel kernel-based divergences between probability distributions, focusing on the Kullback-Leibler divergence with kernel covariance operators (KKL) and its regularized variant for distributions with disjoint supports.
Explore stability bounds for optimal transport maps between distributions, their statistical implications, and applications in estimating OT maps from random samples. Based on joint research with Manole, Niles-Weed, and Wasserman.
Dive into the theoretical advancements of Osborne's matrix balancing algorithm, exploring its near-linear runtime guarantees and connections to optimal transport problems in modern computational applications.
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