Related papers: Discovering Novel Halide Perovskite Alloys using M…

The reliability with Machine Learning (ML) techniques in novel materials discovery often depend on the quality of the dataset, in addition to the relevant features used in describing the material. In this regard, the current study presents…

Perovskite stability is of the core importance and difficulty in current research and application of perovskite solar cells. Nevertheless, over the past century, the formability and stability of perovskite still relied on simplified factor…

Metal halide perovskites (MHPs) are nowadays one of the most studied semiconductors due to their exceptional performance as active layers in solar cells. Although MHPs are excellent solid-state semiconductors, they are also ionic compounds,…

Metal halide perovskite semiconductors have outstanding optoelectronic properties. Although these perovskites are defect-tolerant electronically, defects hamper their long-term stability and cause degradation. Density functional theory…

Since the very early stages of research on sustainable technologies, green energy conversion has always been a prime focus. With the discoveries of countless functional materials in recent years, significant progress has been made to meet…

Accurate optical constants are essential for modelling light propagation, absorption, and ultimately photovoltaic performance in state of the art perovskite solar cells and is especially important for multiple junction or tandem cells.…

Lead-free halide double perovskites are promising stable and non-toxic alternatives to methylammonium lead iodide in the field of photovoltaics. In this context, the most commonly used double perovskite is Cs$_2$AgBiBr$_6$, due to its…

In pursuit of an all-inorganic non-toxic perovskite solar cell (PSC) with enhanced performance, we have investigated the rhombohedral phase of the germanium-based rubidium halide perovskites RbGeX$_3$ (X = Cl, Br, I). The structural…

To be able to increase the efficiency of perovskite solar cells which is one of the most substantial challenges ahead in photovoltaic industry, the structural and optical properties of perovskite CH3NH3PbI3-xBrx for values x=1-3 have been…

The frequency-dependent optical spectrum is pivotal for a broad range of applications, from material characterization to optoelectronics and energy harvesting. Data-driven surrogate models, trained on density functional theory (DFT) data,…

Hybrid organic-inorganic halide perovskites have emerged as a disruptive new class of materials, exhibiting optimum properties for a broad range of optoelectronic applications, most notably for photovoltaics. The first report of highly…

We describe a first open-access database of experimentally investigated hybrid organic-inorganic materials with two-dimensional (2D) perovskite-like crystal structure. The database includes 515 compounds, containing 180 different organic…

Computational virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a…

The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending…

We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This…

The journey of theoretical study on semiconductors is reviewed in a non-conventional way. We have started with the basic introduction of Hartree-Fock method and introduce the fundamentals of Density Functional Theory (DFT). From the oldest…

Conventional generative models for materials discovery are predominantly trained and validated using data from Density Functional Theory (DFT) with approximate exchange-correlation functionals. This creates a fundamental bottleneck: these…

Wide bandgap semiconductors (WBGs) are predicted to be the potential materials for energy generation and storing. In this work, we used density functional theory (DFT) that incorporates generalized gradient approximation (GGA) and…

This study focuses on developing precise machine learning (ML) regression models for predicting energy bandgap values based on chemical compositions and crystal structures. The primary aim is to match the accuracy of predictions derived…

The material class of hybrid organic-inorganic perovskites has risen rapidly from a virtually unknown material in photovoltaic applications a short 7 years ago into a ~20% efficient thin-film solar cell material. As promising as this class…