Chemo-mechanical coupling stabilizes mixed $\mathrm{Ag}_{x}\mathrm{Cu}_{1-x}\mathrm{GaSe}_{2}$ solar-cell absorbers: Insights from Monte-Carlo simulations assisted by ab initio informed machine-learning potentials

TL;DR

This study uses Monte-Carlo simulations with machine learning potentials to show that chemo-mechanical effects stabilize mixed Ag-Cu-GaSe2 solar-cell absorbers, explaining experimental stability despite predicted phase separation.

Contribution

The paper introduces a novel ML-MC framework that incorporates elastic energy to resolve thermodynamic stability discrepancies in alloy systems.

Findings

Coherency strain resolves the Ag-Cu miscibility gap.

Elastic energy contributions lead to complete Ag-Cu miscibility.

Incoherent interfaces favor phase separation without strain.

Abstract

Alloying Ag into Cu(In,Ga)Se$_2$ has enabled record solar-cell efficiencies ($\sim23.6\%$), yet their long-term stability remains in question because initio calculations predict a Ag-Cu miscibility gap near ambient temperature. By off-lattice Monte-Carlo simulations using a newly developed machine learning (ML) interatomic potential we show that the presence of coherency strain is resolving the controversy between experimental observations and the predicted phase stability. Incorporating elastic energy contributions present in a coherent setup results in complete Ag-Cu miscibility, whereas the expected phase separation occurs in the absence of coherency strains with respect to the end boundary phases, which are mimicked by an incoherent interface with misfit dislocations. The developed ML-MC framework provides a novel approach for resolving discrepancies in thermodynamic stability for systems where mechanical and chemical effects compete.