Accurate prediction of macroscopic transport from microscopic imaging via critical fractals at the Mott transition
P.-Y. Chen, A. R. Rajapurohita, M. Alzate Banguero, S. Basak, F. Simmons, P. Salev, L. Aigouy, Ivan. K. Schuller, A. Zimmers, and E. W. Carlson

TL;DR
This paper presents a multiscale resistor network model that accurately predicts VO$_2$'s macroscopic resistance from microscopic imaging by incorporating fractal domain structures modeled via the random field Ising model, bridging microscopic and macroscopic transport.
Contribution
The study introduces a novel multiscale resistor network model that uses fractal domain assumptions and the random field Ising model to predict VO$_2$ resistance from imaging data.
Findings
Model accurately predicts resistance across temperature range.
Fractal domain structures extend to sub-pixel scales.
Random field Ising model effectively describes domain patterns.
Abstract
Vanadium dioxide (VO) exhibits hysteresis in resistance while undergoing a thermally driven insulator-metal transition (IMT). Understanding the nonequilibrium effects in resistance is of great interest, as VO is a strong candidate for brain-inspired computing, which is more energy efficient for AI tasks compared to traditional computing. Accurate models of the connection between microscopic and macroscopic transport properties and microscopic imaging of VO will allow us to better utilize VO in future applications. However, predictions of macroscopic resistance of VO that quantitatively match observations using spatially resolved data have not yet been achieved. Here, we demonstrate an accurate prediction of the macroscopic resistance of VO throughout the entire temperature range of interest, by developing a multiscale resistor network model incorporating the…
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Taxonomy
TopicsTransition Metal Oxide Nanomaterials · Chemical and Physical Properties of Materials · Advanced Memory and Neural Computing
