Chemically-Disordered Transparent Conductive Perovskites With High Crystalline Fidelity.

This manuscript presents a working model linking chemical disorder and transport properties in correlated-electron perovskites with high-entropy formulations and a framework to actively design them. This work demonstrates this new learning in epitaxial Sr(Ti,Cr,Nb,Mo,W)O thin films that exhibit exceptional crystalline fidelity despite a diverse chemical formulation where most B-site species are highly misfit with respect to valence and radius. X-ray diffraction, X-ray photoelectron spectroscopy, and transmission electron microscopy confirm a unique combination of chemical disorder and structural perfection in thin and thick epitaxial layers.

Mixed 1D/0D Structures of Chiral Hybrid Lead Halides with an l-Nicotinium Spacer Exhibit Broadband Photoluminescence and Polarized Emission.

Hybrid lead halide materials have been highly studied for optoelectronics, but their use with chiral organics in chiro-optoelectronics remains limited. Understanding the ability to impart chirality into bulk materials by incorporating a chiral precursor is of great interest due to unique optical properties directly arising from chirality such as circular dichroism, optical rotatory dispersion, and circularly polarized luminescence. Herein, we used protonated l-nicotine (L-n) (nicotinium cations) as a spacer and templated a variety of structures including a series of (L-n)MX (M = Sn, Pb; X = Cl, Br, I) that take space group 6 in which the lead halide regions template two distinct moieties, both a 1D chain motif and a 0D cluster motif.

Interplay between Two Structure Types in the Incommensurately Modulated ATaSe Compounds (A = Rb, Cs).

Incommensurately modulated crystals are a rare class of materials that are notoriously difficult to characterize properly. We have synthesized two new incommensurately modulated compounds, RbTaSe and CsTaSe, based on the MQ (M = Nb, Ta; Q = S, Se) unit using high-temperature solid-state synthesis. Using superspace crystallography in combination with second harmonic generation measurements, we confirmed both materials to be noncentrosymmetric, falling into the superspace group 1(αβγ)0, while the basic cell suggests 2/.

High-entropy engineering of the crystal and electronic structures in a Dirac material.

Dirac and Weyl semimetals are a central topic of contemporary condensed matter physics, and the discovery of new compounds with Dirac/Weyl electronic states is crucial to the advancement of topological materials and quantum technologies. Here we show a widely applicable strategy that uses high configuration entropy to engineer relativistic electronic states. We take the AMnSb (A = Ba, Sr, Ca, Eu, and Yb) Dirac material family as an example and demonstrate that mixing of Ba, Sr, Ca, Eu and Yb at the A site generates the compound (BaSrCaEuYb)MnSb (denoted as AMnSb), giving access to a polar structure with a space group that is not present in any of the parent compounds.

Feasibility study of Mg storage in a bilayer silicene anode application of an external electric field.

With the goal of developing a Si-based anode for Mg-ion batteries (MIBs) that is both efficient and compatible with the current semiconductor industry, the current research utilized classical Molecular Dynamics (MD) simulation in investigating the intercalation of a Mg ion under an external electric field (E-field) in a 2D bilayer silicene anode (BSA). First principles density functional theory calculations were used to validate the implemented EDIP potentials. Our simulation shows that there exists an optimum E-field value in the range of 0.

Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics.

In this research, solar cell capacitance simulator-one-dimensional (SCAPS-1D) software was used to build and probe nontoxic Cs-based perovskite solar devices and investigate modulations of key material parameters on ultimate power conversion efficiency (PCE). The input material parameters of the absorber Cs-perovskite layer were incrementally changed, and with the various resulting combinations, 63,500 unique devices were formed and probed to produce device PCE. Versatile and well-established machine learning algorithms were thereafter utilized to train, test, and evaluate the output dataset with a focused goal to delineate and rank the input material parameters for their impact on ultimate device performance and PCE.

Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnIBr).

In this investigation, supervised machine learning (ML) was utilized to accurately predict the optimum bromine doping concentration in single-junction MASnIBr devices. Data-driven optimizations were carried out on 42 000 unique devices built utilizing a solar cell capacitance simulator (SCAPS). The devices were investigated through variations of bromine doping %, bandgap, electron affinity, series resistance, back-contact metal, and acceptor concentration─parameters that were specifically chosen because of their tunable nature and ability to be modified through facile experimental fabrication techniques of the device.