Improving the heat-moisture-light stability of organic-inorganic perovskites, a widely studied semiconductor material class, is a critical challenge. Compositional search within multinary perovskites employing brute force synthesis followed by environmental tests are prohibitively expensive in large chemical spaces. To identify the most stable multi-cation lead iodide perovskites containing Cs, formamidinium (FA) and methylammonium (MA), we fuse results from density functional theory (DFT) calculations and in situ thin-film degradation test within an end-toend machine learning (ML) algorithm to inform the compositional optimization of CsxMAyFA1-x-yPbI3. We integrate phase thermodynamics modelling as a probabilistic constraint in a Bayesian optimization (BO) loop, which effectively guides the experimental search while considering both structural and environmental stability. After three optimization rounds and only sampling 1.8% of the compositional space, we identify thin-film compositions centred at Cs0.17MA0.03FA0.80PbI3 that achieve a 3x delay in macroscopic degradation onset under elevated temperature, humidity, and light compared with the more complex state-of-the-art Cs0.05(MA0.17FA0.83)0.95Pb(I0.83Br0.17)3. We find up to 8% of MA can be incorporated into the perovskite structure before stability is significantly compromised. Cs is beneficial at low concentrations, however, beyond 17% is found to contribute to reduced stability. Synchrotron-based grazing-incidence wide-angle X-ray scattering (GIWAXS) further validates that the interplay of chemical decomposition and phase separation governs the non-linear instability landscape of this compositional space. We reveal the detrimental role of the ẟ-CsPbI3 minority phase in accelerating degradation and it can be kinetically suppressed by cooptimising Cs and MA content, providing insights into simplifying perovskite compositions for further environmental stability enhancement. Our approach realizes the effectiveness of MLenabled data fusion in achieving a holistic, efficient, and physics-informed experimentation for multinary systems, potentially generalisable to materials search in the vast structural and alloyed spaces beyond halide perovskites.

Carbon based perovskite solar cells are fabricated for the first time in a room temperature environment by employing inkjet infiltration of perovskite precursor ink. The fabricated perovskite solar cells exhibit impressive performance reproducibility with this automated method and exhibit high stability when exposed to 35 °C for a period of 1046 hours.

Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite absorber, called the capping layer. In this study, a machine-learning framework is presented to optimize this layer. We featurize 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI3) films, age them under accelerated conditions, and determine features governing stability using supervised machine learning and Shapley values. We find that organic molecules’ low number of hydrogen-bonding donors and small topological polar surface area correlate with increased MAPbI3 film stability. The top performing organic halide, phenyltriethylammonium iodide (PTEAI), successfully extends the MAPbI3 stability lifetime by 4 ± 2 times over bare MAPbI3 and 1.3 ± 0.3 times over state-of-the-art octylammonium bromide (OABr). Through characterization, we find that this capping layer stabilizes the photoactive layer by changing the surface chemistry and suppressing methylammonium loss.

The long term stability of air processed inkjet infiltrated carbon based perovskite solar cells (CPSCs) is investigated under intense ultra-violet light soaking equivalent to 1.5 Sun UV light illumination.

Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains. In this work, we quantify the performance of BO with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems. By defining acceleration and enhancement metrics for materials optimization objectives, we find that surrogate models such as Gaussian Process (GP) with anisotropic kernels and Random Forest (RF) have comparable performance in BO, and both outperform the commonly used GP with isotropic kernels. GP with anisotropic kernels has demonstrated the most robustness, yet RF is a close alternative and warrants more consideration because it is free from distribution assumptions, has smaller time complexity, and requires less effort in initial hyperparameter selection. We also raise awareness about the benefits of using GP with anisotropic kernels in future materials optimization campaigns.

Yes. Testing 7 different metals as a substrate for a counter electrode in dye sensitized solar cells (DSSC) showed that some metals can be a good option for use with cobalt electrolyte. It was found that Stainless steels 304 and 321 as well as Ni and Ti suit well to the counter electrodes in DSSCs with cobalt electrolyte. In these 4 cases both the efficiency and the lifetime were similar to the reference cells on conducting glass substrates. In contrast, the cells with Al, Cu and Zn substrates suffered from both a low efficiency and a poor stability. These three metals had clear marks of corrosion such as apparent corrosion products in the aged cells. Additionally, we also investigated how the different types of catalyst materials perform in the case of a metal counter electrode (stainless steel 304) with cobalt electrolyte in comparison to reference glass cells. Among the 5 different catalyst layers the best results for stainless steel electrode were achieved with low temperature platinization whereas polymer catalysts poly(3,4-ethylenedioxythiophene)-p-toluenesulfone and poly(3,4-ethylenedioxythiophene)-polystyrenesulfone that worked well on the glass worked very poorly on the metal.

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Urgent deficiencies in the aging tests of perovskite and dye solar cells are identified and improved methods are proposed in this work.