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For photovoltaic materials, properties such as band gap E g are critical indicators of the material's suitability to perform a desired function. Calculating E g is often performed using Density Function Theory ( DFT ) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP , CRYSTAL, CASTEP or Quantum Expresso . Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as E g of a wide range of materials.
Keywords
machine learning, deep learning, materials prediction, band gap
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CURRENT STATUS: Under Review
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Posted 04 Dec, 2020
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Editorial decision: Revise before peer review
On 08 Dec, 2020
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On 01 Dec, 2020
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On 01 Dec, 2020
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This preprint is under consideration at Journal of Cheminformatics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 04 Dec, 2020
No community comments so far
Editorial decision: Revise before peer review
On 08 Dec, 2020
Editor assigned
On 01 Dec, 2020
Submission checks complete
On 01 Dec, 2020
Editor invited
On 01 Dec, 2020
First submitted
On 30 Nov, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
For photovoltaic materials, properties such as band gap E g are critical indicators of the material's suitability to perform a desired function. Calculating E g is often performed using Density Function Theory ( DFT ) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP , CRYSTAL, CASTEP or Quantum Expresso . Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as E g of a wide range of materials.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
The full text of this article is available to read as a PDF.
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