Výsledky vyhledávání - "Pimachev, Artem"

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FluxGAN: A Physics-Aware Generative Adversarial Network Model for Generating Microstructures That Maintain Target Heat Flux

Autor: Pimachev, Artem K., Settipalli, Manoj, Neogi, Sanghamitra

We propose a physics-aware generative adversarial network model, FluxGAN, capable of simultaneously generating high-quality images of large microstructures and description of their thermal properties. During the training phase, the model learns about

Externí odkaz: http://arxiv.org/abs/2310.04622

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Image to Properties: Extracting Atomic Structure Information from Band Dispersion Images of Semiconductor Heterostructures Using Machine Learning

Autor: Pimachev, Artem K, Neogi, Sanghamitra

The atomic environments of semiconductor heterostructures can be highly varied as various structural imperfections, lattice mismatch and non-uniform strain environments are generally present. The computational costs of first-principles modeling techn

Externí odkaz: http://arxiv.org/abs/2302.00261

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First-Principles Prediction of Electronic Transport in Experimental Semiconductor Heterostructures via Physics-Based Machine Learning

Autor: Pimachev, Artem K., Neogi, Sanghamitra

Publikováno v: npj Computational Materials 7, 93 (2021)

First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to model heterostructures incorporating variability due to fabrication processes. Machine-learning (ML)

Externí odkaz: http://arxiv.org/abs/2011.08426

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Modulation of Semiconductor Superlattice Thermopower Through Symmetry and Strain

Autor: Proshchenko, Vitaly. S., Settipalli, Manoj, Pimachev, Artem K., Neogi, Sanghamitra

Publikováno v: Journal of Applied Physics 129, 025301 (2021)

In doped semiconductors and metals, the thermopower decreases with increasing carrier concentration, in agreement with the Pisarenko relation. Here, we demonstrate a new strain engineering approach to increase the thermopower of [001] Si/Ge superlatt

Externí odkaz: http://arxiv.org/abs/1907.03461

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Deep Learning Model for Inverse Design of Semiconductor Heterostructures with Desired Electronic Band Structures

Autor: Pimachev, Artem K, Neogi, Sanghamitra

First-principles modeling techniques have shown remarkable success in predicting electronic band structures of materials. However, the computational costs make it challenging to use them for predicting band structures of semiconductor heterostructure

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5bda394738940ebdb2fc537c1b255707

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Akademický článek

Role of substrate strain to tune energy bands–Seebeck relationship in semiconductor heterostructures.

Autor: Proshchenko, Vitaly S., Settipalli, Manoj, Pimachev, Artem K., Neogi, Sanghamitra

Publikováno v: Journal of Applied Physics; 1/14/2021, Vol. 129 Issue 2, p1-13, 13p

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Molecular tunneling in large tubes of 3D nitrogenated micropore materials.

Autor: Pimachev, Artem, Dahnovsky, Yuri

Publikováno v: Journal of Applied Physics; 2018, Vol. 124 Issue 19, pN.PAG-N.PAG, 6p, 3 Color Photographs, 7 Diagrams, 1 Chart, 1 Graph

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Tunable bandgap in halogen doped 2D nitrogenated microporous materials.

Autor: Pimachev, Artem, Proshchenko, Vitaly, Dahnovsky, Yuri

Publikováno v: Journal of Applied Physics; 2017, Vol. 122 Issue 11, p115305-1-115305-6, 6p, 2 Color Photographs, 1 Chart, 6 Graphs

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