most citedArtificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie

28 citations

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5 papers

cs.LG2026

Virtual Temperature Sensors in Power Transformers Using Neural Ordinary Differential Equations

Berk Hadzhamolla, Alexander Johannes Stasik, Signe Riemer-Sørensen

Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation. Numerical approaches suc…

cs.NE2026

Jump-diffusion models of parametric volume-price distributions

Anup Budhathoki, Leonardo Rydin Gorjão, Pedro G. Lind +1

We present a data-driven framework to model the stochastic evolution of volume-price distribution from the New York Stock Exchange (NYSE) equities. The empirical distributions are…

cond-mat.mtrl-sci2026

Exceptional thermoelectric properties in NaTlSb enabled by quasi-1D band structure

Øven A. Grimenes, Ole M. Løvvik, Kristian Berland

Materials with reduced dimensionality offer beneficial density-of-states (DOS) profiles for thermoelectric energy conversion, but can be impractical in realistic devices. Encouragi…

cond-mat.mtrl-sci2026★ 28 cited

Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie

Iman Peivaste, Salim Belouettar, Francesco Mercuri +15

Artificial Intelligence is rapidly transforming materials science and engineering, offering powerful tools to navigate complexity, accelerate discovery, and optimize material desig…

cond-mat.soft2025★ 5 cited

Convection can enhance the capacitive charging of porous electrodes

Aaron D. Ratschow, Alexander J. Wagner, Mathijs Janssen +1

Charge transport in porous electrodes is foundational for modern energy storage technologies like supercapacitors, fuel cells, and batteries. Supercapacitors in particular rely sol…