| Summary: | The feasibility of mixed-cation mixed-halogen perovskites of formula A<inline-formula><math display="inline"><semantics><msub><mrow></mrow><mi>x</mi></msub></semantics></math></inline-formula>A’<inline-formula><math display="inline"><semantics><msub><mrow></mrow><mrow><mn>1</mn><mo>−</mo><mi>x</mi></mrow></msub></semantics></math></inline-formula>PbX<inline-formula><math display="inline"><semantics><msub><mrow></mrow><mi>y</mi></msub></semantics></math></inline-formula>X’<inline-formula><math display="inline"><semantics><msub><mrow></mrow><mi>z</mi></msub></semantics></math></inline-formula>X”<inline-formula><math display="inline"><semantics><msub><mrow></mrow><mrow><mn>3</mn><mo>−</mo><mi>y</mi><mo>−</mo><mi>z</mi></mrow></msub></semantics></math></inline-formula> is analyzed from the perspective of structural stability, opto-electronic properties and possible degradation mechanisms. Using density functional theory (DFT) calculations aided by machine-learning (ML) methods, the structurally stable compositions are further evaluated for the highest absorption and optimal stability. Here, the role of the halogen mixtures is demonstrated in tuning the contrasting trends of optical absorption and stability. Similarly, binary organic cation mixtures are found to significantly influence the degradation, while they have a lesser, but still visible effect on the opto-electronic properties. The combined framework of high-throughput calculations and ML techniques such as the linear regression methods, random forests and artificial neural networks offers the necessary grounds for an efficient exploration of multi-dimensional compositional spaces.
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