Related papers: Explainable Machine Learning for Oxygen Diffusion …
The diffusion path and diffusivity of oxygen in crystalline silicon are computed using an empirical interatomic potential which was recently developed for modelling the interactions between oxygen and silicon atoms. The diffusion path is…
While metastability enhanced water-oxidation activity was experimentally reported, the reason behind this effect is still unclear. We determine here, using density functional theory calculations, oxygen evolution reaction overpotentials for…
Building on the extensive exploration of metal oxide and metal halide perovskites, metal nitride perovskites represent a largely unexplored class of materials. We report a multi-tier computational screening of this chemical space. From a…
Iron oxides and oxyhydroxides are challenging to model computationally as competing phases may differ in formation energies by only several kJ/mol, they undergo magnetization transitions with temperature, their structures may contain…
The binding energy of an electron in a material is a fundamental characteristic, which determines a wealth of important chemical and physical properties. For metal-organic frameworks this quantity is hitherto unknown. We present a general…
The oxygen evolution reaction (OER) is considered a key reaction for electrochemical energy conversion but slow kinetics hamper application in electrolyzers, metal-air batteries and other applications that rely on sustainable protons from…
In this study, we present a novel approach along with the needed computational strategies for efficient and scalable feature engineering of the crystal structure in compounds of different chemical compositions. This approach utilizes a…
The accurate prediction of solvation free energy is of significant importance as it governs the behavior of solutes in solution. In this work, we apply a variety of machine learning techniques to predict and analyze the alchemical free…
Materials properties depend strongly on chemical composition, i.e., the relative amounts of each chemical element. Changes in composition lead to entirely different chemical arrangements, which vary in complexity from perfectly ordered…
Solar water splitting has attracted much attention as a clean and renewable route to produce hydrogen fuel. Since the oxygen evolution half-reaction (OER) requires high overpotentials, much research has focused on finding catalyst materials…
Perovskite Quantum Dots (PQDs) have a promising future for several applications due to their unique properties. This study investigates the effectiveness of Machine Learning (ML) in predicting the size, absorbance (1S abs) and…
Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These phenomena are strongly affected by the materials…
Material extrusion is one of the most commonly used approaches within the additive manufacturing processes available. Despite its popularity and related technical advancements, process reliability and quality assurance remain only partially…
Since electronic and magnetic properties of many transition-metal oxides can be efficiently controlled by external factors such as the temperature, pressure, electric or magnetic field, they are regarded as promising materials for various…
Focused ion beam (FIB) techniques have been frequently used to section metal-halide perovskites for microstructural investigations. However, the ion beams directly irradiated to the sample surface may alter the properties far different from…
Halide double perovskites are an emerging class of semiconductors with tremendous chemical and electronic diversity. While their bandstructure features can be understood from frontier-orbital models, chemical intuition for optical…
This Letter introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a diffusion denoising probabilistic model (DDPM). We created a dataset of synthetic…
Traditional materials discovery approaches - relying primarily on laborious experiments - have controlled the pace of technology. Instead, computational approaches offer an accelerated path: high-throughput exploration and characterization…
Simulations of exciton and charge hopping in amorphous organic materials involve numerous physical parameters. Each of these parameters must be computed from costly ab initio calculations before the simulation can commence, resulting in a…
Combustion is the primary process in gas turbine engines, where there is a need for efficient air-fuel mixing to enhance performance. High-shear swirl injectors are commonly used to improve fuel atomization and mixing, which are key factors…