Hybrid (organic–inorganic) multication lead halide perovskites hold promise for a new generation of easily processable solar cells. Best performing compositions to date are multiple-cation solid alloys of formamidinium (FA), methylammonium (MA), cesium, and rubidium lead halides which provide power conversion efficiencies up to around 22%. Here, we elucidate the atomic-level nature of Cs and Rb incorporation into the perovskite lattice of FA-based materials. We use 133Cs, 87Rb, 39K, 13C, and 14N solid-state MAS NMR to probe microscopic composition of Cs-, Rb-, K-, MA-, and FA-containing phases in double-, triple-, and quadruple-cation lead halides in bulk and in a thin film. Contrary to previous reports, we have found no proof of Rb or K incorporation into the 3D perovskite lattice in these systems. We also show that the structure of bulk mechanochemical perovskites bears close resemblance to that of thin films, making them a good benchmark for structural studies. These findings provide fundamental understanding of previously reported excellent photovoltaic parameters in these systems and their superior stability.

Mixed-cation organic lead halide perovskites attract unfaltering attention owing to their excellent photovoltaic properties. Currently, the best performing perovskite materials contain multiple cations and provide power conversion efficiencies up to around 22%. Here, we report the first quantitative, cation-specific data on cation reorientation dynamics in hybrid mixed-cation formamidinium (FA)/methylammonium (MA) lead halide perovskites. We use N,H, C, andH solid-state MAS NMR to elucidate cation reorientation dynamics, microscopic phase composition, and the MA/FA ratio, in (MA)(FA)PbI between 100 and 330 K. The reorientation rates correlate in a striking manner with the carrier lifetimes previously reported for these materials and provide evidence of the polaronic nature of charge carriers in PV perovskites.

Methylammonium (MA)- and formamidinium (FA)-based organic-inorganic lead halide perovskites provide outstanding performance as photovoltaic materials, due to their versatility of fabrication and their power conversion efficiencies reaching over 22%. The proposition of guanidinium (GUA)-doped perovskite materials generated considerable interest due to their potential to increase carrier lifetimes and open-circuit voltages as compared to pure MAPbI. However, simple size considerations based on the Goldschmidt tolerance factor suggest that guanidinium is too big to completely replace methylammonium as an A cation in the APbI perovskite lattice, and its effect was thus ascribed to passivation of surface trap states at grain boundaries. As guanidinium was not thought to incorporate into the MAPbI lattice, interest waned since it appeared unlikely that it could be used to modify the intrinsic perovskite properties. Here, using solid-state NMR, we provide for the first time atomic-level evidence that GUA is directly incorporated into the MAPbI and FAPbI lattices, forming pure GUA MAPbI or GUA FAPbI phases, and that it reorients on the picosecond time scale within the perovskite lattice, which explains its superior charge carrier stabilization capacity. Our findings establish a fundamental link between charge carrier lifetimes observed in photovoltaic perovskites and the A cation structure in ABX-type metal halide perovskites.

Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the ability to calculate them, which  comes at the cost of high accuracy first-principles calculations. Machine learning has recently emerged as a way to overcome the need for quantum chemical calculations, but for chemical shifts in solids it is hindered by the chemical and combinatorial space spanned by molecular solids, the strong dependency of chemical shifts on their environment, and the lack of an experimental database of shifts. We propose a machine learning method based on local environments to accurately predict chemical shifts of molecular solids and their polymorphs to within DFT accuracy. We also demonstrate that the trained model is able to determine, based on the match between experimentally measured and ML-predicted shifts, the structures of cocaine and the drug 4-[4-(2-adamantylcarbamoyl)-5-tert-butylpyrazol-1-yl]benzoic acid.

Based on an analysis of the short range chemical environment of each atom in a system, standard machine learning based approaches to the construction of interatomic potentials aim at determining directly the central quantity which is the total energy. This prevents for instance an accurate description of the energetics of systems where long range charge transfer is important as well as of ionized systems. We propose therefore not to target directly with machine learning methods the total energy but an intermediate physical quantity namely the charge density, which then in turn allows to determine the total energy. By allowing the electronic charge to distribute itself in an optimal way over the system, we can describe not only neutral but also ionized systems with unprecedented accuracy. We demonstrate the power of our approach for both neutral and ionized NaCl clusters where charge redistribution plays a decisive role for the energetics. We are able to obtain chemical accuracy, i.e. errors of less than a milli Hartree per atom compared to the reference density functional results. The introduction of physically motivated quantities which are determined by the short range atomic environment via a neural network leads also to an increased stability of the machine learning process and transferability of the potential. arXiv:1501.07344v1 [cond-mat.mtrl-sci]

Organic-inorganic lead halide perovskites are a promising family of light absorbers for a new generation of solar cells, with reported efficiencies currently exceeding 22%. A common problem of solar cells fabricated using these materials is that their efficiency depends on their cycling history, an effect known as current-voltage ( J- V) hysteresis. Potassium doping has recently emerged as a universal way to overcome this adverse phenomenon. While the atomistic origins of J- V hysteresis are still not fully understood, it is essential to rationalize the atomic-level effect of protocols that lead to its suppression. Here, using K MAS NMR at 21.1 T we provide for the first time atomic-level characterization of the potassium-containing phases that are formed upon KI doping of multication and multianion lead halide perovskites. We find no evidence of potassium incorporation into 3D perovskite lattices of the recently reported materials. Instead, we observe formation of a mixture of potassium-rich phases and unreacted KI. In the case of Br-containing lead halide perovskites doped with KI, a mixture of KI and KBr ensues, leading to a change in the Br/I ratio in the perovskite phase with respect to the undoped perovskite. Simultaneous Cs and K doping leads to the formation of nonperovskite Cs/K lead iodide phases.

Efforts to tune the bulk physical properties of concrete are hindered by a lack of knowledge related to the atomiclevel structure and growth of calcium silicate hydrate phases, which form about 50-60% by volume of cement paste. Here we describe the first synthesis of compositionally uniform calcium silicate hydrate phases with Ca:Si ratios tunable between 1.0 and 2.0. The calcium silicate hydrate synthesized here does not contain a secondary Ca(OH)2 phase, even in samples with Ca:Si ratios above 1.6, which is unprecedented for synthetic calcium silicate hydrate systems. We then solve the atomic-level three-dimensional structure of these materials using dynamic nuclear polarization enhanced 1 H and 29 Si nuclear magnetic resonance experiments in combination with atomistic simulations and density functional theory chemical shift calculations. We discover that bridging interlayer calcium ions are the defining structural characteristic of single-phase cementitious calcium silicate hydrate, inducing the strong hydrogen bonding that is responsible for stabilizing the structure at high Ca:Si ratios.

There has been an ongoing effort to overcome the limitations associated with the stability of hybrid organic-inorganic perovskite solar cells by using different organic agents as additives to the perovskite formulations. The functionality of organic additives has been predominantly limited to exploiting hydrogen bonding interactions, with the relevant atomic-level binding modes remaining elusive. Herein, we introduce a new bifunctional supramolecular modulator, 1,2,4,5-tetrafluoro-3,6-diiodobenzene, which interacts with the surface of the triple-cation double-halide perovskite material via halogen bonding. We elucidate its binding mode using two-dimensional solid-state 19 F NMR spectroscopy in conjunction with DFT calculations. As a result, we demonstrate a stability enhancement of the perovskite solar cells upon supramolecular modulation, without compromising the high photovoltaic performances. Supporting Information Materials and Methods, DFT Energy and NMR Chemical Shift Calculations, Supplementary Data, and Transient Capacitance Measurements. The Supporting Information is available free of charge on the ACS Publications website.