Machine Learning for Halide Perovskite Materials ABX3 (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy

Abstract

The exact control of material properties essential for solar applications has been made possible because of perovskites’ compositional engineering. However, tackling efficiency, stability, and toxicity at the same time is still a difficulty. Mixed lead-free and inorganic perovskites have lately shown promise in addressing these problems, but their composition space is vast, making it challenging to find good candidates even with high-throughput approaches. We investigated two groups of halide perovskite compound data with the ABX3 formula to investigate the formation energy data for 81 compounds. The structural stability was analyzed over 63 compounds. For these perovskites, we used new library data extracted from a calculation using generalized-gradient approximation within the Perdew–Burke–Ernzerhof (PBE) functional established on density functional theory. As a second step, we built machine learning models, based on a kernel-based naive Bayes algorithm that anticipate a variety of target characteristics, including the mixing enthalpy, different octahedral distortions, and band gap calculations. In addition to laying the groundwork for observing new perovskites that go beyond currently available technical uses, this work creates a framework for finding and optimizing perovskites in a photovoltaic application.

A comprehensive review on the advancements and challenges in perovskite solar cell technology

“…In this range, the ionic sizes are suitable for forming a cubic lattice, where the BX 6 octahedra are undistorted, and the A-site cation fits snugly into the cuboctahedral void formed by the twelve X anions. 231,232 Alternatively, tetragonal or hexagonal structures are formed when it exceeds 1. This usually indicates that the A-site cation is too large for the ideal cubic structure, leading to a distortion in the perovskite structure.…”

The Pressure‐Induced Structural Stability, Mechanical, and Optoelectronic Properties of Pb2ScTaO6 Perovskite: a Density Functional Theory Study

“…[38] The structural optimization procedure integral to our calculations relied on Murnaghan's model of state. [39] This optimization aimed to determine the relaxed phase of Pb 2 ScTaO 6 with the minimum energy value, and the resulting lattice parameters are summarized in Table 2. Significantly, a comparison of these optimized parameters with both experimental and theoretical values reveals a remarkable degree of agreement.…”

Prediction model for device density of states for quantum-confined SiC nanotube with magnetic dopant: A Machine learning and DFT based combined framework

“…However, less theoretical researchers investigated the magnetic properties of 3d TM doped SiC nanotubes. SiC nanotubes have been synthesized by various research groups [30][31][32][33][34][35][36][37][38]. Experimental [39] and theoretical [40][41][42][43][44] research works devoted interactions of TM atoms with SWSiCNTs have reported appreciable changes in the structural, electronic, and magnetic properties of these systems.…”