Related papers: Computational screening of magnetocaloric alloys
Important phenomena such as magnetostriction, magnetocaloric, and magnetoelectric effects arise from, or could be enhanced by, the coupling of magnetic and structural degrees of freedom. The coupling of spin and lattice also influence…
Due to their chemical and structural diversity, nanoporous materials can be used in a wide variety of applications, including fluid separation, gas storage, heterogeneous catalysis, drug delivery, etc. Given the large and rapidly increasing…
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the…
Due to their abundant use in all-solid-state lasers, nonlinear optical (NLO) crystals are needed for many applications across diverse fields such as medicine and communication. However, because of conflicting requirements, the design of…
Atomic scale simulations are a key element of modern science in that they allow to understand, and even predict, complex physical or chemical phenomena on the basis of the fundamental laws of nature. Among the different existing atomic…
Decades of hardware, methodological, and algorithmic development have propelled molecular dynamics (MD) simulations to the forefront of materials-modeling techniques, bridging the gap between electronic-structure theory and continuum…
The metal-organic framework (MOF) MFU-4l containing Co(II) centers and Cl- ligands has recently shown promising redox activity. Aiming for further improved MOF catalysts for oxidation processes employing molecular oxygen we present a…
Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the computational cost of sampling the large supercell and…
The concept of mechanical screening is widely applied in solid-state systems. Examples include nucleation of defects in crystalline materials, scars and pleats in curved crystals, wrinkles in strongly confined thin sheets, and…
Compositionally complex materials (CCMs) present a potential paradigm shift in the design of magnetic materials. These alloys exhibit long-range structural order coupled with limited or no chemical order. As a result, extreme local…
In this work, we have proposed a data-driven screening framework combining the interpretable machine learning with high-throughput calculations to identify a series of metal oxides that exhibit both high-temperature tolerance and high power…
An integrated data-driven approach combined with a high-throughput framework based on first-principles calculations was used to discover novel rare-earth-free permanent magnets, focusing on binary alloys. Compounds were screened…
Magnetic 2D materials have achieved significantly consideration owing to their encouraging applications. A variation of these 2D materials by occurrence of defects, by the transition-metal doping or adsorption or by the surface…
Two-dimensional (2D) binary transition-metal chalcogenides (TMCs) like molybdenum disulfide exhibits excellent properties as materials for light adsorption devices. Alloying binary TMCs can form 2D compositionally complex TMC alloys…
High-throughput computational screening of polymers offers a powerful way to address the imbalance between the vast number of polymers synthesised for diverse applications and the relatively small subset that can be studied using atomistic…
Conventionally, high-throughput computational materials searches start from an input set of bulk compounds extracted from material databases, and this set is screened for candidate materials for specific applications. In contrast, many…
Computational screening for new and improved catalyst materials relies on accurate and low-cost predictions of key parameters such as adsorption energies. Here, we use recently developed compressed sensing methods to identify descriptors…
In an effort to explore high-throughput processing of microscopic image data, a method based on deep convolutional neural network is proposed. The state-of-the-art computer vision algorithm, Faster R-CNN, was trained for the detection of…
Predicting material properties of disordered systems remains a long-standing and formidable challenge in rational materials design. To address this issue, we introduce an automated software framework capable of modeling partial occupation…
Based on high-throughput density functional theory calculations, we have found 49 ferromag-netic cases in FexN1-x (0<x<1) compounds, focusing especially on permanent magnet and giant magnetocaloric effect applications. It is found that 15…