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Article overview
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Deep Learning Model for Inverse Design of Semiconductor Heterostructures with Desired Electronic Band Structures | | Artem K Pimachev
; Sanghamitra Neogi
; | | Date: |
1 Feb 2023 | | Abstract: | 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 heterostructures, that show high variability of atomic
structures. We propose a machine learning-assisted first-principles framework
that bypasses expensive computations and predicts band structures from the
knowledge of atomic structural features. Additionally, the framework directly
connects modeling results and experimental data. For example, it accepts images
obtained with angle-resolved photoemission spectroscopy as input and predicts
the corresponding atomic structures. The framework leverages the physical
relationship between atomic environments and bands. We demonstrate the
framework using silicon/germanium based superlattices and heterostructures.
Once trained on silicon-based systems, the framework can even predict band
structures of gallium arsenide thin films. The physics-informed framework
establishes an approach to expedite the design and discovery of complex
materials with desired band structures, going beyond combinatorial approaches. | | Source: | arXiv, 2302.00261 | | Services: | Forum | Review | PDF | Favorites | |
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