Data‐Driven Investigation of the Synthesizability and Bandgap of Double Perovskite Halides. Issue 8 (11th May 2022)
- Record Type:
- Journal Article
- Title:
- Data‐Driven Investigation of the Synthesizability and Bandgap of Double Perovskite Halides. Issue 8 (11th May 2022)
- Main Title:
- Data‐Driven Investigation of the Synthesizability and Bandgap of Double Perovskite Halides
- Authors:
- Kim, Joonchul
Min, Kyoungmin - Abstract:
- Abstract: Double perovskite halide materials have been widely used in batteries, light‐emitting diodes, and solar cells. Thus, investigations of the fundamental properties of the double perovskite halide to search for an ideal structure are crucial. In this study, a surrogate model is developed to predict the formation energy, convex hull energy, and bandgap of A2 BB′X6 type double perovskite halide structures. The material properties of 13 542 candidate structures are predicted and validated through first‐principles calculations. Without double perovskite halide information during training, the prediction accuracy for the formation energy is obtained as an R‐squared value of 0.770 and Root Mean Square Error (RMSE) of 0.404 eV atom −1 . For the convex hull energy, an accuracy of 0.642 is obtained. For the bandgap, R‐squared score of 0.427 and an RMSE of 1.235 eV are achieved. Furthermore, the optimization process confirms that adding only 850 (6%) double perovskite halide structures to the training set increases the R‐squared value to 0.90 for the formation energy. In the bandgap, more data are needed; 3550 data (68.2%) are added to achieve an R‐squared score of 0.9. The current study successfully predicts the fundamental properties of double perovskite halides for the accelerated discovery of ideal structures. Abstract : A surrogate model is developed to predict the formation energy, convex hull energy, and bandgap of A2 BB′X6 type double perovskite halide structures. TheAbstract: Double perovskite halide materials have been widely used in batteries, light‐emitting diodes, and solar cells. Thus, investigations of the fundamental properties of the double perovskite halide to search for an ideal structure are crucial. In this study, a surrogate model is developed to predict the formation energy, convex hull energy, and bandgap of A2 BB′X6 type double perovskite halide structures. The material properties of 13 542 candidate structures are predicted and validated through first‐principles calculations. Without double perovskite halide information during training, the prediction accuracy for the formation energy is obtained as an R‐squared value of 0.770 and Root Mean Square Error (RMSE) of 0.404 eV atom −1 . For the convex hull energy, an accuracy of 0.642 is obtained. For the bandgap, R‐squared score of 0.427 and an RMSE of 1.235 eV are achieved. Furthermore, the optimization process confirms that adding only 850 (6%) double perovskite halide structures to the training set increases the R‐squared value to 0.90 for the formation energy. In the bandgap, more data are needed; 3550 data (68.2%) are added to achieve an R‐squared score of 0.9. The current study successfully predicts the fundamental properties of double perovskite halides for the accelerated discovery of ideal structures. Abstract : A surrogate model is developed to predict the formation energy, convex hull energy, and bandgap of A2 BB′X6 type double perovskite halide structures. The material properties of 13 542 candidate structures are predicted and validated through first‐principles calculations. The current study successfully predicts the fundamental properties of double perovskite halides for the accelerated discovery of ideal structures. … (more)
- Is Part Of:
- Advanced theory and simulations. Volume 5:Issue 8(2022)
- Journal:
- Advanced theory and simulations
- Issue:
- Volume 5:Issue 8(2022)
- Issue Display:
- Volume 5, Issue 8 (2022)
- Year:
- 2022
- Volume:
- 5
- Issue:
- 8
- Issue Sort Value:
- 2022-0005-0008-0000
- Page Start:
- n/a
- Page End:
- n/a
- Publication Date:
- 2022-05-11
- Subjects:
- bandgaps -- double perovskite halide -- first‐principles calculations -- machine learning -- thermodynamical properties
Science -- Simulation methods -- Periodicals
Science -- Methodology -- Periodicals
Engineering -- Simulation methods -- Periodicals
Engineering -- Methodology -- Periodicals
507.21 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/adts.202200068 ↗
- Languages:
- English
- ISSNs:
- 2513-0390
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0696.935575
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23708.xml