A Machine Learning Platform for the Discovery of Materials
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
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Posted 05 Feb, 2021
On 05 Feb, 2021
Received 24 Jan, 2021
Received 24 Jan, 2021
Received 14 Jan, 2021
On 01 Jan, 2021
On 26 Dec, 2020
Invitations sent on 20 Dec, 2020
On 20 Dec, 2020
On 17 Dec, 2020
On 17 Dec, 2020
On 17 Dec, 2020
Posted 04 Dec, 2020
On 08 Dec, 2020
On 01 Dec, 2020
On 01 Dec, 2020
On 01 Dec, 2020
On 30 Nov, 2020
A Machine Learning Platform for the Discovery of Materials
Posted 05 Feb, 2021
On 05 Feb, 2021
Received 24 Jan, 2021
Received 24 Jan, 2021
Received 14 Jan, 2021
On 01 Jan, 2021
On 26 Dec, 2020
Invitations sent on 20 Dec, 2020
On 20 Dec, 2020
On 17 Dec, 2020
On 17 Dec, 2020
On 17 Dec, 2020
Posted 04 Dec, 2020
On 08 Dec, 2020
On 01 Dec, 2020
On 01 Dec, 2020
On 01 Dec, 2020
On 30 Nov, 2020
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
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.