Related papers: Probabilistic deep learning approach for targeted …
Low dimensional hybrid organic-inorganic perovskites (HOIPs) represent a promising class of electronically active materials for both light absorption and emission. The design space of HOIPs is extremely large, since a diverse space of…
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…
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…
Machine learning interatomic potentials (MLIPs) have been widely used to facilitate large-scale molecular simulations with accuracy comparable to ab initio methods. In practice, MLIP-based molecular simulations often encounter the issue of…
In solid-state materials science, substantial efforts have been devoted to the calculation and modeling of the electronic band gap. While a wide range of ab initio methods and machine learning algorithms have been created that can predict…
Compared to first principles studies like DFT (physically accurate, computationally expensive), the TB approach allows the disentanglement of the region of interest, to wit the fermi level. This allows the creation of simplified, highly…
Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a…
Prediction of the electronic structure of functional materials is essential for the engineering of new devices. Conventional electronic structure prediction methods based on density functional theory (DFT) suffer from not only high…
Organic-inorganic metal halide perovskites (HaPs) are intensively studied for their light-harvesting properties. Owing to the interplay between strong electron-electron interaction and spin-orbit coupling (SOC), their quantitative…
A two-layer neural network model that systematically includes correlations among input variables to arbitrary order and is designed to implement Bayes inference has been adapted to classify breast cancer tumors as malignant or benign,…
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase…
Halide perovskites constitute a chemically-diverse class of crystals with great promise as photovoltaic absorber materials, featuring band gaps between about 1 and 3.5 eV depending on composition. Their diversity calls for a general…
The unprecedented structural flexibility and diversity of inorganic frameworks of layered hybrid halide perovskites (LHHPs) rise up a wide range of useful optoelectronic properties thus predetermining the extraordinary high interest to this…
We develop a computationally efficient scheme to accurately determine finite-temperature band gaps. We here focus on materials belonging to the class ABX3 (A = Rb, Cs; B = Ge, Sn, Pb; and X = F, Cl, Br, I), which includes halide…
We verified that the deep learning method named reading periodic table introduced by ref. Deep Learning Model for Finding New Superconductors, which utilizes deep learning to read the periodic table and the laws of the elements, is…
Accurate prediction for the electronic structure properties of halide perovskites plays a significant role in the design of highly efficient and stable solar cells. While density functional theory (DFT) within the generalized gradient…
Hybrid organic-inorganic perovskites (HOIPs) have recently emerged as highly promising solution-processable materials for photovoltaic (PV) and other optoelectronic devices. HOIPs represent a broad family of materials with properties highly…
Accurate prediction of electronic band gaps in halide perovskites using density functional theory is crucial for optoelectronic applications. Standard hybrid functionals like HSE and PBE0 are becoming computationally accessible, yet can…
Development of next-generation electronic devices for applications call for the discovery of quantum materials hosting novel electronic, magnetic, and topological properties. Traditional electronic structure methods require expensive…
The unique aspect of the hybrid perovskites is their tunability, allowing to engineer the bandgap via substitution. From application viewpoint, this allows creation of the tandem cells between perovskites and silicon, or two or more…