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KFM: Fachverband Kristalline Festkörper und deren Mikrostruktur
KFM 31: Perovskite and Photovoltaics III (joint session HL/KFM)
KFM 31.6: Talk
Friday, March 22, 2024, 10:45–11:00, EW 203
Analyzing defect thermodynamics of (Ag,Cu)GaSe2 solar cell absorbers using a machine-learning interatomic potential — •Vasilios Karanikolas, Delwin Perera, and Karsten Albe — Institut für Materialwissenschaft, Technische Universität Darmstadt, Germany
One of the most widely used absorber materials for thin-film solar cells is Cu(In,Ga)Se2 (CIGS). Currently, CIGS yields the highest efficiencies within this technology and the addition of Ag has been found to further improve the efficiency. The performance of the CIGS absorber, however, is also governed by defects, especially by the type and density of grain boundaries (GBs) [1].
In this work, we investigate the thermodynamic properties of GBs for (Ag1−xCux)GaSe2 structures based on a machine learning interatomic potential (MLIP)[2]. The training dataset for the regression machine learning model is created by density functional theory (DFT) calculations.
The MLIP allows us to perform molecular dynamics simulations for structurally complex GBs that are inaccessible by conventional electronic structure methods. In particular, we investigate the thermodynamic properties of symmetric GBs beyond Σ 3 and include an analysis of silver segregation at the interfaces.
[1] D. Abou-Ras et al., Acta Materialia 118, 244-252 (2016).
[2] Y. Lysogorskiy et al., npj Computational Materials 7, 1 (2021).
Keywords: thin-film solar cells; CIGS; Machine learning; Interatomic potential; Photovoltaics