An Efficient ab Initio Scheme for Discovering Organic-Inorganic Hybrid Materials by Using Genetic Algorithms

Dimension-Controlled Synthesis of Hybrid-Mixed Halide Perovskites for Solar Cell Application

Despite the rapid improvement of photovoltaic (PV) efficiency in hybrid organic-inorganic metal halide perovskites (HOIPs), the fabrication procedure of a compact thin film in a large-area application is still a tedious work. Apart from the quality of the thin film, the stability of the perovskite materials and the expensive organic hole transport layer (HTL) within the HOIP-based PV device are the major issues that need to be addressed prior to their commercialization. Herein, a unique glass rod-based facile fabrication technique for producing a compact and stable thin film utilizing a mixed-halide-based perovskite precursor solution is demonstrated. The fabricated devices deliver high photoconversion efficiency (PCE) without the use of any HTL and show an excellent stability under ambient conditions. By varying the organic CH3NH3I (MAI) and inorganic PbBr2 content, perovskite materials with different dimensions, i.e., 3D, 2D, and 1D, are synthesized to produce an active layer for PV devices. Although a 2D single-halide perovskite is reported earlier, herein two different mixed-halide 2D perovskites, i.e., MA2PbI2Br2 and MAPb2IBr4, are synthesized successfully, and their performance is compared in detail along with that of 1D and 3D mixed-halide perovskites. The facile synthesized mixed-halide 2D-based MA2PbI2Br2 perovskite shows a PCE of 10.14% with a high stability of 92% after 100 days without encapsulation, which is much superior as compared to that of the mixed-halide 3D MAPbIBr2. The semiconducting behavior as well as the nature of the bandgap of the synthesized compounds is examined by pursuing density functional theory calculations. Specifically, the role of iodine doping to modify the electronic band structure is investigated, and introduction of iodine is found to reduce the effective masses of both electrons and holes in the perovskite material.

Simple Structural Descriptor Obtained from Symbolic Classification for Predicting the Oxygen Vacancy Defect Formation of Perovskites

Symbolic classification is an approach of interpretable machine learning for building mathematical formulas that fit certain data sets. In this work, symbolic classification is used to establish the relationship between oxygen vacancy defect formation energy and structural features. We find a structural descriptor n_a(r_a/E_na - r_b), where n_a is the valence of the a-site ion, r_a is the radius of the a-site ion, E_na is the electronegativity of the a-site ion, and r_b is the radius of the b-site ion. It accelerates the screening of defect-free oxide perovskites in advance of density functional theory (DFT) calculations and experimental characterization. Our results demonstrate the potential of symbolic classification for accelerating the data-driven design and discovery of materials with improved properties.

Crystal structure prediction of energetic materials and a twisted arene with Genarris and GAtor

A molecular crystal structure prediction workflow, based on the random structure generator, Genarris, and the genetic algorithm (GA), GAtor, is successfully applied to two energetic materials and a chiral arene.