Database-driven high-throughput study for hybrid perovskite coating materials

TL;DR

This study employs a high-throughput screening approach to identify promising inorganic coating materials for hybrid perovskite solar cells from a large database, focusing on properties like band gap, toxicity, solubility, and lattice compatibility.

Contribution

It introduces a systematic, database-driven method to discover suitable coating materials for perovskites, narrowing down candidates based on multiple desirable properties.

Findings

Identified 93 candidate materials for perovskite coatings.

Candidates meet criteria such as wide band gaps and non-toxicity.

Materials show promising compatibility with halide perovskites.

Abstract

We developed a high-throughput screening scheme to acquire candidate coating materials for hybrid perovskites. From more than 1.8 million entries of an inorganic compound database, we collected 93 binary and ternary materials with promising properties for protectively coating halide-perovskite photoabsorbers in perovskite solar cells. These candidates fulfill a series of criteria, including wide band gaps, abundant and non-toxic elements, water-insoluble, and small lattice mismatch with surface models of halide perovskites.

Tables

Table 1. TABLE 1: Selected candidate coating materials for MAPbI 3 subscript MAPbI 3 \text{MAPbI}_{3} . Listed are their band gaps (in eV, data from Aflow Curtarolo et al. ( 2012 ) ), space groups, and dominant charge carriers (n- or p-type conductivity) with corresponding references. Intrinsic n- and p-type materials, and n- and p-type dopable materials, are labeled by N, P, n and p, respectively.

Coating	Space group	Gap	Cond.	Refs.		Coating	Space group	Gap	Cond.	Refs.
CaSiO3	P43̄m	$3.67$				PbO	P4/nmm	$1.66$	P	Droessler,2014
BaAl2S4	Pa3̄	$3.65$				ZnO	F4̄3m	$1.69$	N	Stevanović et al.,2014
SiO2	Fm3̄m	$1.73$				CaZrO3	Pm3̄m	$3.21$
MoF3	Pm3̄m	$1.74$				GaN	F4̄3m	$1.75$
NiO	Fm3̄m	$1.79$	P	Stevanović et al.,2014		ZrO2	P42/nmc	$3.86$
BiF3	Fm3̄m	$3.95$		Poole et al.,1976		HfO2	P42/nmc	$4.01$	p	Hildebrandt et al.,2014
BaTiO3	Pm3̄m	$2.13$				BN	F4̄3m	$4.46$	n,p
BN	P63mc	$5.21$	n,p			PbZrO3	Pm3̄m	$2.28$
HfSiO4	I41/amd	$5.31$				CaTiO3	Pm3̄m	$2.36$
BeO	Fm3̄m	$8.18$				Si3N4	Fm3̄m	$3.33$

Equations

$$\gamma \triangleq \frac{m{a}_{\text{c}}-a_{\text{p}}}{m{a}_{\text{c}}}\times 100\%,$$

Full text

Perovskite solar cells (PSCs) Snaith (2013); Green et al. (2014); Saliba (2018) have recently reached a power-conversion efficiency (PCE) of $>\!\!23\%$ only six years after the invention of the state-of-the-art PSC architecture in 2012 (PCE$\sim$10%) Kim et al. (2012); Lee et al. (2012). This has revived the hope for direct conversion of sustainable, affordable and environmentally friendly solar energy into electricity. The photoabsorbers in PSCs are hybrid (organic-inorganic) perovskites (denoted $\text{ABX}_{3}$ hereafter) especially methylammonium (MA) lead iodide ($\text{CH}_{3}\text{NH}_{3}\text{PbI}_{3}\!\equiv\!\text{MAPbI}_{3}$). The salient properties of these materials in optoelectronic applications are optimal band gaps, excellent absorption in the visible range of the solar spectrum, good transport properties for both electrons and holes, flexibility of composition engineering, as well as low costs in both raw materials and fabrication Snaith (2013); Green et al. (2014); Stranks et al. (2013); Xing et al. (2013); Eperon et al. (2015); Troughton et al. (2017).

Despite the excellent PSC-performance in the laboratory, stability problems limit the development and commercialization of this promising materials class. Hybrid perovskites degrade quickly in heat, oxygen and moisture Niu et al. (2014, 2015); Huang et al. (2017); Mesquita et al. (2018); Ciccioli and Latini (2018). With increasing exposure to any of these destabilizing factors, the structure of the hybrid perovskite degrades and the PCE reduces concomitantly after several days or even hours Kim et al. (2017); Li et al. (2018). Among the solutions that have been proposed to solve this stability and longevity problem are protective coating Matteocci et al. (2016); Cheacharoen et al. (2018a, b), the use of two-dimensional perovskites Quan et al. (2016); Dou (2017); Ran et al. (2018); Wang et al. (2018), and doping with small ions Noh et al. (2013); Yi et al. (2016); Zhou et al. (2016); Tan et al. (2017); Ciccioli and Latini (2018). Protective coating is particularly promising, as it can passivate the surface dangling bonds of the perovskite photoabsorber and insulate the perovskite from heat and small molecules from the environment. A good coating should have the following properties: (i) a wide band gap (>3 eV), (ii) little impact on the structure of the coated perovskite, (iii) good transport properties, and (iv) high stability in heat, air and water. It would be particularly attractive, if the coating material could also be used as a semiconducting interlayer, a key component in the modern perovskite-based device architectures. In this context, we are especially interested in cheap and efficient hole-transporting coatings, as Spiro-OMeTAD, the most common hole-transporting material (HTM) in PSCs since the birth of this technology Kim et al. (2012); Lee et al. (2012), is expensive, has low charge-carrier mobilities and a negative impact on PSC stability Saliba et al. (2016).

We here present a database-driven high-throughput study that explores a wide range of possible candidates to find inorganic materials that have the potential to protectively coat perovskites in PSCs. We take the inorganic materials from the “Automatic Flow for Materials Discovery” (aflow) database Curtarolo et al. (2012). aflow contains nearly 2 million material entries that were computed with density-functional-theory (DFT) using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional Perdew et al. (1996).

In the following, we will describe our filtering scheme with which we reduced the large number of database entries to only those material candidates with promising coating properties. The workflow is illustrated in Fig. 1. Since PBE generally underestimates band gaps by $\sim\!\!50\%$ Sham and Schlüter (1983), we set our first criterion (C1) to screen materials with “PBE band gap $>\!\!1.5\leavevmode\nobreak\ {\text{eV}}$”. Considering the technical difficulties of coating with quaternary or even more complicated compounds Ryou et al. (2009); Yeh et al. (2014), we limited our target materials to binary and ternary compounds in this work (C2). In C3 we excluded all compounds that contain toxic or rare elements, and in C4 we discarded the compounds that are unstable in contact with water. Details of how we implemented C3 and C4 are available in the Supplementary Material (SM). In C5, we selected candidates with appropriate lattices, meaning candidates with at least two perpendicular lattice vectors in the conventional cell. In the final step (C6), we calculated the lattice mismatch between selected perovskite substrates and the coating materials that survived from C5. This last step produced some coating materials with several phases. In such cases, we prioritized the phase with the least lattice mismatch to $\text{MAPbI}_{3}$. The other crystal phases are presented in the SM.

As substrates, we chose 12 $\text{ABX}_{3}$ perovskites (A = Cs/MA, B = Sn/Pb, and X = Cl/Br/I) that are commonly used in halide-perovskite-based devices. We optimized the structure of the tetragonal P4/mbm phase of $\text{CsBX}_{3}$ and the tetragonal (quasi) I4/mcm phase of $\text{MABX}_{3}$ using PBE Perdew et al. (1996) (to stay consistent with aflow data Taylor et al. (2014)) and the analytic stress tensor Knuth et al. (2015) implemented in the all-electron numeric-atom-centered orbital code fhi-aims Blum et al. (2009); Havu et al. (2009); Levchenko et al. (2015). Details of the DFT calculations are given in the SM. Upon a test calculation, we selected the $(001)$ crystal planes of the perovskites (Figs. 2a and b) since they are the most stable surface of these materials. We determined the lattice mismatch based on the lattice constants alone and did not carry out any interface calculations with DFT. Figure 2c shows the two “virtual surface models” considered in this work. We did not consider larger surface models, since they would make further computational modeling intractable.

From the PBE-optimized lattice constants, we calculated the lattice mismatch at each coating-perovskite interface. To avoid large strain, we required that the coatings should have rectangular lattice planes with small miller indices, e.g., the $(100)$ plane of the cubic lattice or the $(11\bar{2}0)$ plane of the hexagonal lattice. More details of this selection is given in the SM. If the lattice constant of the coating and the perovskite are $a_{\text{c}}$ and $a_{\text{p}}$ along one direction, then the lattice mismatch is,

[TABLE]

$m$ is the integer that minimizes $|\gamma|$. We set the criterion $\gamma\in(-5,+5)\%$ as shown in Fig. 1.

With the high-throughput screening scheme in Fig. 1, we extracted 93 inorganic semiconductor coating candidates (39 binaries and 54 ternaries) from Aflow. In addition, there are $\sim$1000 suitable ternary compounds, for which we could not find any data on their solubility in water. These remaining compounds will be investigated further in the future.

Figure 3 shows the calculated lattice mismatch between the candidates and the 12 $\text{ABX}_{3}$ perovskite substrates. Panels 3a and b reveal that several materials with cubic or tetragonal lattices can be used to coat most of the investigated perovskites: ZnS, BN, some fluorides ($\text{BiF}_{3}$, $\text{MoF}_{3}$ and $\text{AgSbF}_{6}$), some binary oxides ($\text{Bi}_{2}\text{O}_{3}$ in both cubic and tetragonal phases, $\text{Ce}_{2}\text{O}_{3}$, BeO, PbO, $\text{TiO}_{2}$-anatase, NiO and tetragonal $\text{SiO}_{2}$) and a large range of ternary oxides. In contrast, Figures. 3c and d show that most of the materials that are in neither the cubic nor the tetragonal phase can only cover a small range of perovskite substrates. This is because the $|\gamma|<5\%$ criterion must be satisfied by two lattice constants, which makes the coating less “versatile” in these phases.

From Figs. 3a and b, one can immediately deduce that the lattice mismatch increases from $-5$ to $5$ % as the lattice constant of the substrates increases. The yellow spots show the most promising candidates with mismatch $<1\%$. Only a few coating candidates with “non-square” planes survived our screening criteria. This is because in such materials, at least two lattice constants must have lattice mismatch within $-5$ and $5\%$. For instance, the $\gamma$ values for the interface between the hexagonal phase of $\text{Bi}_{2}\text{O}_{3}$ at $\text{MAPbBr}_{3}$ interface are $-6.5\%$ and $0.73\%$ along the $a$- and $c$-axis, respectively. Thus $\text{Bi}_{2}\text{O}_{3}$ would not be a suitable candidate to coat $\text{MAPbBr}_{3}$.

As a first consistency check, we compared the material candidates in Figs. 3a and b to materials that have already been used as transport or mesoporous scaffold layers in PSCs. We found that our search is consistent with common materials such as: NiO as HTM in PSCs Lai et al. (2018), as well as ZnO Lai et al. (2018) and $\text{TiO}_{2}$ Chen et al. (2014) as electron-transporting materials (ETMs). Similarly, our candidate materials included $\text{ZrO}_{2}$ Mejía Escobar et al. (2017) and $\text{Al}_{2}\text{O}_{3}$ Si et al. (2016) which are used as mesoporous scaffolds in PSCs.

Aside from the commonly known metal oxides used in PSCs, we discovered some surprising binary candidates ($\text{MoF}_{3}$, GaN, $\text{BiF}_{3}$, $\text{Si}_{3}\text{N}_{4}$ and BN) that have properties suitable to coat the photovoltaic-active halide perovskites (Fig. 3a). Similarly, for ternaries we found $\text{BaAl}_{2}\text{S}_{4}$, $\text{AgSbF}_{6}$, $\text{BaSiF}_{6}$ and $\text{BaGeF}_{6}$. These materials came as surprise since they are usually not considered in PSCs due to their high melting temperatures. However, with new coating techniques such as radio-frequency sputtering da Silva Filho et al. (2018), pulsed laser deposition Liang et al. (2016), vapor-deposition Ávila et al. (2017) and modified hybrid methods such as spin-coating/vapor-deposition Dong et al. (2015), these materials become contenders as effective coating materials for future PSC devices.

Of particular interest are the potential coating materials for $\text{MAPbI}_{3}$, the most common photoabsorber in PSCs. Interestingly, our screening procedure reveals that $\text{Al}_{2}\text{O}_{3}$ (Fig. 3c), which is the most common mesoporous material in today’s PSC architectures Lee et al. (2012), does not have the minimum lattice mismatch for coating $\text{MAPbI}_{3}$. ZnO, NiO, $\text{CaSiO}_{3}$, $\text{SiO}_{2}$, $\text{SrZrO}_{3}$, $\text{BaAl}_{2}\text{S}_{4}$, GaN, $\text{MoF}_{3}$, BN, $\text{Si}_{3}\text{N}_{4}$ and $\text{ZrO}_{2}$ lead to better lattice match. The actual strain values for $\text{MAPbI}_{3}$ can be found in the far right column of each panel in Fig. 3

Next we briefly address the charge carrier properties of the potential candidates. Table 1 lists the PBE band gaps of the found candidate coatings for $\text{MAPbI}_{3}$ provided by Aflow Curtarolo et al. (2012), together with the dominant charge carrier type (n- or p-type). Here, we observe that intrinsic p-type semiconductors such as NiO and PbO, will not only protect PSCs against ambient conditions, but could also serve as efficient HTMs to replace the inefficient Spiro-OMeTAD.

We also found insulators such as $\text{ZrO}_{2}$, $\text{Si}_{3}\text{N}_{4}$, and BeO (Table 1). Due to the large band gap of these materials and their insolubility in water, they can be used as efficient mesoporous scaffolds to passivate PSCs against degradation. Additionally, BN could be used as a p– or n–type semiconductor with different doping mechanisms (Table 1). It was recently reported that BiF3 has a high-lying valence band Feng et al. (2015); Poole et al. (1976) thus potentially being a good HTM. Also HfO2 could be engineered into a p-type material by controlling the oxygen vacancy content Hildebrandt et al. (2014).

Lastly, we briefly comment on realistic coating interfaces. The actual phase of the coating material and the structure of the interface depend on many factors such as the perovskite surface structure and properties, the deposition method, the deposition conditions, as well as the coating thickness. These factors are not included in our database study. An atomistic description of coating-perovskite interfaces requires further computational (e.g., DFT) and experimental work. Results from such future work, such as the stability of the coating materials, could then be incorporated as additional criteria in our screening procedure.

In summary, we have developed a systematic and efficient screening scheme for perovskite coating materials. Our scheme reduces the $\sim$1.8 million materials entries in Aflow to 93 possible coating candidates for a series of perovskite photoabsorbers in PSCs. We have identified inexpensive HTMs (NiO and PbO) that can replace the inefficient and expensive Spiro-OMeTAD, as well as several efficient ETMs (e.g., ZnO) for PSCs. Our results feature new materials beyond metal oxides that will not only enhance the stability of PSCs but also serve as a starting point in the search of novel device materials for emergent PSC technologies.

We gratefully thank M. Todorović and G.-X. Zhang for insightful discussions. We acknowledge the computing resources by the CSC-IT Center for Science and the Aalto Science-IT project. An award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 676580 with The Novel Materials Discovery (NOMAD) Laboratory, European Center of Excellence, the Väisälä Foundation, as well as the Academy of Finland through its Centres of Excellence Programme (2015-2017) under project number 284621 and its Key Project Funding scheme under project number 305632.