Hybrid perovskites have emerged as one of the leading materials in photovoltaics due to remarkable solar-to-electric power conversion efficiencies.[1-2] However, their limited stability under device operation conditions remain challenging.[1-2] In contrast, layered two-dimensional analogs have shown promising stabilities.[3-7] We demonstrate a strategy to provide stabilization without compromising the performance of perovskite solar cells through molecular modulation based on fine-tuning various noncovalent interactions (i.e. supramolecular engineering),[7-10] such as metal coordination,[10] hydrogen[6,9-10] or halogen bonding,[8] and π-interactions,[7] in a manner uniquely assessed by solid-state NMR spectroscopy.[5-6,8-10] As a result, we obtain perovskite solar cells featuring superior performances, accompanied by enhanced operational stabilities.[6-10] Moreover, extending the design into layered perovskite architectures enables further stability enhancements.[3-7] This has been investigated using a combination of techniques complemented by solid-state NMR to unravel the design principles and exemplify the supramolecular approach in advancing hybrid perovskite photovoltaics.

Reference:

[1] J. V. Milić et al. Chimia 2019, 73, 317; [2] M. Graetzel et al. Substantia 2019, 3, 27; [3] Y. Li  et  al.  Nano Lett. 2019, 19, 150; [4] J. V. Milić et al. Adv. Energy Mater. 2019, 1900284; [5] L. Hong, et al. Angew. Chem. Int. Ed. 2020, doi:10.1002/anie201912051; [6] A. Q. Alanazi, et al. J. Am. Chem. Soc. 2019, 141, 17659; [7] Y. Liu et al. Sci. Adv. 2019, 5, eaaw2543; [8] M. A. Ruiz-Preciado et al. J. Am. Chem. Soc. 2020, doi:10.1021/jacs.9b13701;

[9] M. M. Tavakoli et al. Energy Environ. Sci. 2018, 11, 3310; [10] D. Bi et al. Nat. Commun. 2018, 9, 4482.

The thermal stability and identification of decomposition products of formamidinium organic based precursors and hybrid perovskite materials have been investigated under inert atmosphere at constant heating rate and pulsed heating steps under illumination/dark conditions.[1] As in the previous case studied for methylammonium based precursors and perovskites, the thermogravimetry technique coupled to quadrupole mass spectrometry allows to draw interesting insights about the thermal stability of these hybrid perovskite materials allowing forecasts towards their operational stability working as light harvester material in photovoltaic devices.
In this talk, we compare and review the different results obtained in four recent articles in the topic  of decomposition products determination for formamidinium based perovskite paying special attention to the HCN release. We will discuss general tenets and beliefs misleading on the topic of stability of perovskite materials and finally, we will propose an operational stability test for perovskite solar cells without consensus.

Lead halide perovskites are mixed electronic and ionic semiconductors and whilst the electronic properties have been characterized early on in this emerging photovoltaic field the actual role of migrating ions is still remains under debate.^{1, 2} Chemical and physical interactions at external interfaces of perovskite with the contacts is believed to be one of the main limiting factor affecting solar cell performance and stability. The precise nature of these interactions have remained very elusive due to the difficulties to analyze buried interfaces. For this reason, the connection between iodine migration towards the external contacts, charge extraction and reactivity of ions with the contact materials remains an obscure subject. In this presentation, it is described how migrating ions can totally modify the charge carrier injection/extraction properties of the external contacts by using J-V measurements and Impedance Spectroscopy.^3-6 The reversibility of this chemical reaction will depend on the material actually used as contact and may be used to prepare resistive memory devices.⁷

Perovskite photovoltaics have received significant attention in recent years owing mostly to rapid improvements of their power conversion efficiency. However, the stability of perovskites is still facing challenges related to multiple irreversible and reversible degradation mechanisms. It results in various types of outdoor day/night performance variations¹. This has also recently led to a new ISOS consensus protocol for assessing the stability of perovskite photovoltaics². In this talk, recent work on bias-dependent dynamics of degradation will be discussed, detailing further the subtle interplay between the reversible and irreversible degradation mechanisms at specific degradation conditions for perovskite photovoltaics.

1. M. Khenkin et. al. Energy Environ. Sci., 11, 739 (2018)
2. M. Khenkin et. al. Nat. Energy, 5, 35 (2020)

Hybrid metal halide perovskites are known to decompose in the presence of heat and moisture. They are also mechanically fragile because of their weak chemical bonds and mismatches in the thermal expansion coefficients between layers. I will present ways to intrinsically improve stability of perovskite solar cells (PSCs). However, intrinsic modification is not sufficient. Therefore, I will then provide my insights on a holistic design of glass-glass encapsulation utilizing commercially available materials to further enhance stability of PSCs. This package design enabled PSCs to pass the IEC standard- damp heat, temperature cycling, and UV-exposure- required prior to real implementation.

Compositional engineering of perovskites enables the precise control of key material properties such as the bandgap [1]. This possibility makes perovskites a promising material for multijunc- tion, “tandem” solar cells, where the combination of two different bandgaps allows to easily break the Shockley-Queisser limit and thus improve efficiency [2]. The remaining challenge is to find structures with the target bandgap which are both stable in the environment as well as non-toxic (i.e. lead-free).

To this end, computer simulations allow rapid screening of a large array of compositions for a given structure and subsequent data modeling. However typical high-troughput calculation fall short in capturing the effect of different geometries in modeling, thus severely constraining the applicability of the result in predicting “new” structures. This becomes especially prob- lematic for large-cell systems, such as for example mixed, lead-free double perovskites, where different compositions might have varying relaxed geometries and sampling the whole feature space becomes infeasible.

Today researcher try to work around this problem by building surrogate models. In our work, we follow the approach outlined initially in the exploration of molecular datasets [3] and subsequently used for inorganic materials research [4]: first we employ a fingerprinting function to create a regular feature vector representation for all structure samples of a training databse and then feed it to a machine learning algorithm together with a target property from ab- initio-calculations for the original structure. Ideally, the resulting model can then use material fingerprints (which could also originate from experimental results) as a proxy for fast property prediction of new materials, sampling vast regions of the whole feature space and, ideally, opening up a way to extract “regions of interest” in feature space, informing high-level theoretical and practical work.

Building upon our previous work of introducing a new, general Radial-distribution-function (RDF)-based fingerprint (while still employing the typical Kernel-Ridge-Regression (KRR) ap- proach on machine learning (ML)) [5], we are now exploring the generalizability of this ap- proach on existing perovskite and novel, inhouse-developed, lead-free, mixed-inorganic per- ovskite databases. To this end we are replacing the KRR with neural networks and also com- paring to various other fingerprints (sine matrix, SOAP and more from the dscribe library [6]) as well as simple “structure-informed”, non-general property features (e.g. for an A₂B-structure the average of a property on the A-site-atoms).

To tackle the problem that the fingerprint size increases with database complexity (structure size and elemental variation) to the order of N ² (where N is the number of atoms in the structure) and thus mandates a larger DFT-based training data set, which in turn requires O(N ³)-scaling DFT-calculations, we employ an autoencoder framework, where a specially designed neural network is used to shrink the structure fingerprint into a meaningful intermediate representation. Ideally this representation should include all the information which is (redundantly) stored in the fingerprint and allow to build a good, non-overfitting model with much less data.

Preliminary studies on the hybrid-perovskite dataset published in [7] show the promise of the latter approach. Although the structure of the dataset (molecular center-ions, which are adding a lot of noise and for which properties are not as readily available as for atoms) prevents efficient usage of the PDDF, we see how adding a structural fingerprint improves on the model compared to plainly using basic features of the course-grained hybrid perovskites structure. Further, we can effectively reduce overfitting of the PDDF-model by encoding the fingerprint with an autoencoder.