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From the 4 of 205 papers with an AI index.

most citedTrainability barriers and opportunities in quantum generative modeling

41 citations

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cond-mat.mtrl-sci2026

Thermal transport in crystals: from the quantum Dyson equation to mesoscopic phonon hydrodynamics

Enrico Di Lucente, Michele Simoncelli, Nicola Marzari

Thermal transport in dielectric, non-magnetic crystals is mediated by quantized lattice vibrations, which drift and interact when driven out of equilibrium by a temperature gradien…

cond-mat.mtrl-sci2026

A Geometric Pathway for Tuning Ferroelectric Properties via Polar State Reconfiguration

Hao-Cheng Thong, Bo Wu, Fan Hu +7

We report the discovery of a geometric pathway for tuning ferroelectric properties through thermally driven reconfiguration between coexisting polar states in Li-substituted NaNbO3…

cond-mat.mtrl-sci20261 cited

The impact of interfacial chemistry on the band offset of GaAs/GaO heterostructures

Sofia Apergi, Alfredo Pasquarello, Charles Cornet +1

GaO/GaAs heterojunctions are emerging as promising candidates for next-generation power electronics, photonics, and energy devices, leveraging the high breakdown voltage an…

cond-mat.mtrl-sci2026

Photovoltaic creation of charged domain walls in barium titanate

P. S. Bednyakov, P. V. Yudin, A. K. Tagantsev +1

The optical control of domain structures in ferroelectrics is of great interest. In the present work, we demonstrate the reliable creation of charged domain walls - conductive chan…

cond-mat.mtrl-sci2026

Extraction of the self energy and Eliashberg function from angle resolved photoemission spectroscopy using the xARPES code

Thomas P. van Waas, Christophe Berthod, Jan Berges +3

Angle-resolved photoemission spectroscopy is a powerful experimental technique for studying anisotropic many-body interactions through the electron spectral function. Existing atte…

cond-mat.mtrl-sci2026

Load-dependent Hardness Prediction for Materials using Machine Learning

Madhubanti Mukherjee, Rampi Ramprasad, Harikrishna Sahu

Superhard materials are critical for wear-resistant and high-stress applications. Conventional approaches correlating hardness with elastic moduli derived from DFT calculations ena…