Related papers: Predicting the thermodynamic stability of perovski…
Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a…
The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at…
This study uses plasma-profile data from the EUROfusion pedestal database, focusing on the electron-temperature and electron-density profiles in the edge region of H-mode ELMy JET ITER-Like-Wall (ILW) pulses. We make systematic predictions…
Despite vibrational properties being critical for the ab initio prediction of the finite temperature stability and transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive…
Porosity has been identified as the key indicator of the durability properties of concrete exposed to aggressive environments. This paper applies ensemble learning to predict porosity of high-performance concrete containing supplementary…
Corrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional…
Organic-inorganic halide perovskite solar cells have recently attracted much attention due to their low-cost fabrication, flexibility, and high-power conversion efficiency. The reduction from three- to two-dimension (2D) promises an…
Density-functional simulations are used to calculate structural properties and high-symmetry phonons of the hypothetical cubic phase, the stable orthorhombic phase and an intermediate tetragonal phase of magnesium silicate perovskite. We…
The reason behind the remarkable properties of High-Entropy Alloys (HEAs) is rooted in the diverse phases and the crystal structures they contain. In the realm of material informatics, employing machine learning (ML) techniques to classify…
This paper presents a non-invasive approach to estimate the layer thicknesses of perovskite solar cells. The thicknesses are predicted by a convolutional neural network that leverages the external quantum efficiency of a perovskite solar…
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…
Chalcogenide perovskites have emerged as promising absorber materials for next-generation photovoltaic devices, yet their experimental realization remains limited by competing phases, structural polymorphism, and synthetic challenges. Here,…
We screen a large chemical space of perovskite alloys for systems with the right properties to accommodate a morphotropic phase boundary (MPB) in their composition-temperature phase diagram, a crucial feature for high piezoelectric…
Density functional theory within the local or semilocal density approximations (DFT-LDA/GGA) has become a workhorse in electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet…
Structural phase transitions as a function of temperature dictate the structure--functionality relationships in many technologically important materials. Harmonic Hamiltonians have proven successful in predicting the vibrational properties…
A key challenge for computational discovery of electrocatalytic materials is the reliable prediction of thermodynamic stability in aqueous environment and under different electrochemical conditions. In this work, we first evaluate the…
Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles modeling of solid-state phase transformations, instead learning…
We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which enables an accurate expansion of the all-electron density in…
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…
Development of geothermal energy and basin-scale simulations of fluid and heat flow both suffer from uncertain physical rock properties at depth. Therefore, building better prognostic models are required. We analysed hydraulic and thermal…