Berlin 2024 – wissenschaftliches Programm

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HL: Fachverband Halbleiterphysik

HL 55: Perovskite and Photovoltaics III (joint session HL/KFM)

HL 55.6: Vortrag

Freitag, 22. März 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 (Ag_1−xCu_x)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