Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases

Putting error bars on density functional theory

Predicting the error in density functional theory (DFT) calculations due to the choice of exchange-correlation (XC) functional is crucial to the success of DFT, but currently, there are limited options to estimate this a priori. This is particularly important for high-throughput screening of new materials. In this work, the structure and elastic properties of binary and ternary oxides are computed using four XC functionals: LDA, PBE-GGA, PBEsol, and vdW-DF with C09 exchange. To analyze the systemic errors inherent to each XC functional, we employed materials informatics methods to predict the expected errors. The predicted errors were also used to better the DFT-predicted lattice parameters. Our results emphasize the link between the computed errors and the electron density and hybridization errors of a functional. In essence, these results provide "error bars" for choosing a functional for the creation of high-accuracy, high-throughput datasets as well as avenues for the development of XC functionals with enhanced performance, thereby enabling the accelerated discovery and design of new materials.

Design of New Inorganic Crystals with the Desired Composition Using Deep Learning

New solid-state materials have been discovered using various approaches from atom substitution in density functional theory (DFT) to generative models in machine learning. Recently, generative models have shown promising performance in finding new materials. Crystal generation with deep learning has been applied in various methods to discover new crystals. However, most generative models can only be applied to materials with specific elements or generate structures with random compositions. In this work, we developed a model that can generate crystals with desired compositions based on a crystal diffusion variational autoencoder. We generated crystal structures for 14 compositions of three types of materials in different applications. The generated structures were further stabilized using DFT calculations. We found the most stable structures in the existing database for all but one composition, even though eight compositions among them were not in the data set trained in a crystal diffusion variational autoencoder. This substantiates the prospect of the generation of an extensive range of compositions. Finally, 205 unique new crystal materials with energy above hull <100 meV/atom were generated. Moreover, we compared the average formation energy of the crystals generated from five compositions, two of which were hypothetical, with that of traditional methods like atom substitution and a generative model. The generated structures had lower formation energy than those of other models, except for one composition. These results demonstrate that our approach can be applied stably in various fields to design stable inorganic materials based on machine learning.

RbSnX3 (X = Cl, Br, I): promising lead-free metal halide perovskites for photovoltaics and optoelectronics

Lead (Pb) free metal halide perovskites by atomistic design are of strong interest to photovoltaics and optoelectronics industries because of the pressing need to resolve Pb-related toxicity and instability challenges. In this study, structural, mechanical, electronic, and optical properties of Pb-free RbSnX3 (X = Cl, Br, I) perovskites have been evaluated by using ab initio density functional theory (DFT) calculations. The computed elastic constants suggest that the Rb-based halide perovskites are mechanically stable and highly ductile, making them suitable as flexible thin films in optoelectronic devices. Besides, the investigated electronic band structures reveal that the RbSnX3 compounds are direct bandgap semiconductors, suitable for photovoltaic and optoelectronic applications. Furthermore, several optical parameters such as dielectric functions, reflectivity, photon absorptions, refractive index, optical conductivity, and loss functions have been investigated and the results predict the excellent optoelectronic efficiency of RbSnX3. Also, the computed mechanical and optical properties of RbSnX3 (X = Cl, Br, I) have been compared with the previously studied CsBX3 (B = Ge, Sn, Pb; X = Cl, Br, I) phases, revealing that the Rb-based perovskites are extremely ductile and possess excellent light absorption and optical conductivity compared to the Cs-based perovskites. Importantly, RbSnI3 shows superior ductility, absorption coefficient, and optical conductivity compared to the CsBX3 (B = Ge, Sn, Pb; X = Cl, Br, I) perovskites. Superior absorption at the ultraviolet region of RbSnI3 holds great promise of this perovskite to be used in next-generation ultraviolet photodetectors.

Significance of the Chemical Environment of an Element in Nonadiabatic Molecular Dynamics: Feature Selection and Dimensionality Reduction with Machine Learning

Using supervised and unsupervised machine learning (ML) on features generated from nonadiabatic (NA) molecular dynamics (MD) trajectories under the classical path approximation, we demonstrate that mutual information with the NA Hamiltonian can be used for feature selection and model simplification. Focusing on CsPbI₃, a popular metal halide perovskite, we observe that the chemical environment of a single element is sufficient for predicting the NA Hamiltonian. The conclusion applies even to Cs, although Cs does not contribute to the relevant wave functions. Interatomic distances between Cs and I or Pb and the octahedral tilt angle are the most important features. We reduce a typical 360-parameter ML force-field model to just a 12-parameter NA Hamiltonian model, while maintaining a high NA-MD simulation quality. Because NA-MD is a valuable tool for studying excited state processes, overcoming its high computational cost through simple ML models will streamline NA-MD simulations and expand the ranges of accessible system size and simulation time.

The effect of substitutional doping of Yb2+on structural, electronic, and optical properties of CsCaX3(X: Cl, Br, I) phosphors: a first-principles study

Using first-principles calculations, the effects of Yb²⁺substitutional doping on structural, electronic, and optical properties of a series of perovskite compounds CsCaX₃(X: Cl, Br, I), have been investigated. We employed generalized gradient approximation (GGA) and HSE hybrid functional to study the electronic and optical properties. A series of pristine CsCaX₃(X: Cl, Br, I) is characterized as a non-magnetic insulator with indirect bandgap perovskite materials. These phosphor materials are suitable candidates for doping with lanthanide series elements to tune their electronic bandgaps according to our requirements because of their wide bandgaps. The calculated electronic bandgaps of CsCaX₃(X: Cl, Br, I) are 3.7 eV (GGA) and 4.5 eV (HSE) for CsCaI₃, 4.5 eV (GGA) and 5.3 eV (HSE) for CsCaBr₃, and 5.4 eV (GGA) and 6.4 eV (HSE) for CsCaCl₃. According to formation energies, the Yb²⁺doped at the Ca-site is thermodynamically more stable as compared to all possible atomic sites. The electronic band structures show that the Yb²⁺doping induces defective states within the bandgaps of pristine CsCaX₃(X: Cl, Br, I). As a result, the Yb²⁺doped CsCaX₃(X: Cl, Br, I) become the direct bandgap semiconductors. The defective states above the valence band maximum are produced due to thef-orbital of the Yb atom. The impurity states near the conduction band minimum are induced due to the major contribution ofd-orbital of the Yb atom and the minor contribution ofs-orbital of the Cs atom. The real and imaginary parts of the dielectric function, optical reflectivity, electron energy loss spectrum, extinction coefficient, and refractive index of pristine and Yb²⁺doped CsCaX₃(X: Cl, Br, I) were studied. The optical dispersion results of dielectric susceptibility closely match their relevant electronic structure and align with previously reported theoretical and experimental data. We conclude that the Yb²⁺doped CsCaX₃(X: Cl, Br, I) are appealing candidates for optoelectronic devices.

Water-Dispersible CsPbBr3 Perovskite Nanocrystals with Ultra-Stability and its Application in Electrochemical CO2 Reduction

Thanks to the excellent optoelectronic properties, lead halide perovskites (LHPs) have been widely employed in high-performance optoelectronic devices such as solar cells and light-emitting diodes. However, overcoming their poor stability against water has been one of the biggest challenges for most applications. Herein, we report a novel hot-injection method in a Pb-poor environment combined with a well-designed purification process to synthesize water-dispersible CsPbBr₃ nanocrystals (NCs). The as-prepared NCs sustain their superior photoluminescence (91% quantum yield in water) for more than 200 days in an aqueous environment, which is attributed to a passivation effect induced by excess CsBr salts. Thanks to the ultra-stability of these LHP NCs, for the first time, we report a new application of LHP NCs, in which they are applied to electrocatalysis of CO₂ reduction reaction. Noticeably, they show significant electrocatalytic activity (faradaic yield: 32% for CH₄, 40% for CO) and operation stability (> 350 h).

Crystal-Plane Controlled Spontaneous Polarization of Inorganic Perovskite toward Boosting Triboelectric Surface Charge Density

Triboelectric generators (TENGs) have been extensively studied as a new energy for low cost and the universally applicable prospect. Meanwhile, perovskites have been applied in TENG and show a good performance in view of high carrier mobility, long life and dielectric properties. The asymmetry structure of the orthogonal phase CsPbBr₃ perovskite endows it with ferroelectric property and induces the misalignment of the positive and negative charge centers. Herein, the surface energy of halogen doped inorganic CsPbX₃ (X = Cl^-, Br^-) perovskites are theoretically investigated by density functional theory (DFT) calculation, the crystal polarizability of pristine CsPbBr₃ is improved from 0.47 Ry a.u. to 0.52 Ry a.u. (CsPbCl₃), indicating the polarizability of CsPbCl₃ is higher than CsPbBr₃. In addition, the build-in electric field (E_build-in) of perovskite materials can be enhanced by the spontaneous polarization and the aligned dipoles in the E_build-in could further improve the tribo-electrostatic electric field by retaining more triboelectric surface charges. In the end, CsPbCl₃ achieved a power of 3.06 W m^-2 compared to the power of 1.34 W m^-2 of CsPbBr₃. This work focuses on the regulation of crystal planes using spontaneous polarization of perovskite toward achieving a high built-in electric field for enhancing triboelectric surface charge density.

Energetic and electronic properties of CsPbBr3 surfaces: a first-principles study

Surface properties of all-inorganic halide perovskites play a crucial role in determining optoelectronic performance of these materials. We investigate the surface energies and electronic structures of cubic CsPbBr₃ surfaces systematically using density functional theory (DFT) methods. We calculate the surface phase diagrams of low-index surfaces of CsPbBr₃, i.e., (100), (110), (111) surfaces. We found that nonpolar (100) surfaces are more stable than polar (110) and (111) surfaces. The nonpolar CsBr-terminated (100) surface shows the best stability, which is attributed to the effect of surface relaxation and high ionicity of the surface layer. The electronic structures reveal that charge transfer to compensate the polarity raises the energy of polar surfaces, which makes polar surfaces unstable. Furthermore, we found that the modulation of surface chemical composition provides an effective way to compensate polarity and thus make polar surfaces of CsPbBr₃ stable. Our results provide physical insights into understanding and further enhancing the surface stability of all-inorganic halide perovskites. This would be helpful in promoting the advancement of all-inorganic halide perovskite-based materials and devices.