Related papers: Discovering Novel Halide Perovskite Alloys using M…
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each…
Materials databases built from calculations based on density functional approximations play an important role in the discovery of materials with improved properties. Most databases thus constructed rely on the generalized gradient…
We present a differentiation framework for plane-wave density-functional theory (DFT) that combines the strengths of forward-mode algorithmic differentiation (AD) and density-functional perturbation theory (DFPT). In the resulting AD-DFPT…
Using density functional theory (DFT)-based calculations, we propose a family of metastable, as-yet unmade V5+ and Cr6+ double perovskite compounds with low band gaps spanning much of the visible region of the solar spectrum. Through…
Like single perovskites, halide double perovskites (HDP) have truly emerged as efficient optoelectronic materials since they display superior stability and are free of toxicity. However, challenges still exist due to either wide and…
In this work, density functional theory (DFT) is used to find out the ground state structures of A2AuScX6 (A= Cs, Rb; X= Cl, Br, I) double Perovskite (DP) halides for the first time. The DP A2AuScX6 halides were studied for their structural…
We demonstrate a machine learning (ML) approach that accurately predicts the current-voltage behavior of 3D/2D-structured (FAMA)Pb(IBr)3/OABr hybrid organic-inorganic halide perovskite (HOIP) solar cells under AM1.5 illumination. Our neural…
Vacancy-ordered double perovskites (VODPs) are promising alternatives to three-dimensional lead halide perovskites for optoelectronic and photovoltaic applications. Mixing these materials creates a vast compositional space, allowing for…
In recent years, metal halide perovskites (MHPs) for optoelectronic applications have attracted the attention of the scientific community due to their outstanding performance. The fundamental understanding of their physicochemical features…
Chalcogenide perovskites have emerged as non-toxic and stable photovoltaic materials, acting as an alternative to lead halide hybrid perovskites having similar optoelectronic properties. In the present work, we report the electronic and…
Halide perovskites exhibit unpredictable properties in response to environmental stressors, due to several composition-dependent degradation mechanisms. In this work, we apply data visualization and machine learning (ML) techniques to…
Perovskite oxides have been extensively studied for their wide range of compositions and structures, as well as their valuable properties for various applications. Expanding from single perovskite ABO$_3$ to double perovskite…
Emergence of inorganic metal halide perovskites as multifunctional optoelectronic materials are due to their exceptional tunability in optoelectronic properties. This study sought to enhance the physical and mechanical properties of…
Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed through compositional engineering of perovskite materials, but…
We report the development of a combined machine-learning and high-throughput density functional theory (DFT) framework to accelerate the search for new ferroelectric materials. The framework can predict potential ferroelectric compounds…
We develop a probabilistic machine learning model and use it to screen for new hybrid organic-inorganic perovskites (HOIPs) with targeted electronic band gap. The data set used for this work is highly diverse, containing multiple atomic…
Recent developments in materials informatics and artificial intelligence has led to the emergence of foundational energy models for material chemistry, as represented by the suite of MACE-based foundation models, bringing a significant…
The oxygen vacancy formation energy ($\Delta E_{vf}$) governs defect dynamics and is a useful metric to perform materials selection for a variety of applications. However, density functional theory (DFT) calculations of $\Delta E_{vf}$ come…
Electronic and stability properties of quasi-2D alkylammonium perovskites are investigated using density functional theory (DFT) calculations and validated experimentally on selected classes of compounds. Our analysis is focused on…
Alloy-based perovskite solar cells offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development. We present a machine-learning (ML) accelerated atomistic modeling approach for…