Results 181 to 190 of about 617,908 (340)
Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani+2 more
wiley +1 more source
ErB4 and NdB4 nanostructured powders are produced by mechanochemical synthesis. 5 h mechanical alloying and 4 M HCl acid leaching are used in the production. ErB4 and NdB4 powders exhibit maximum magnetization of 0.4726 emu g−1 accompanied with an antiferromagnetic‐to‐paramagnetic phase transition at about TN = 18 K and 0.132 emu g−1 with a maximum at ...
Burçak Boztemur+5 more
wiley +1 more source
MACE: A Machine-learning Approach to Chemistry Emulation
The chemistry of an astrophysical environment is closely coupled to its dynamics, the latter often found to be complex. Hence, to properly model these environments a 3D context is necessary. However, solving chemical kinetics within a 3D hydro simulation
Silke Maes+3 more
doaj +1 more source
This article provides a comprehensive overview of fundamentals and recent advances of transparent thin‐film surface acoustic wave technologies on glass substrates for monitoring and prevention/elimination of fog, ice, and frost. Fogging, icing, or frosting on optical lenses, optics/photonics, windshields, vehicle/airplane windows, and solar panel ...
Hui Ling Ong+11 more
wiley +1 more source
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley +1 more source
The JigCell Model Builder: a spreadsheet interface for creating biochemical reaction network models
Marc Vass+4 more
semanticscholar +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani+4 more
wiley +1 more source
A numerical compass for experiment design in chemical kinetics and molecular property estimation
Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion
Matteo Krüger+4 more
doaj +1 more source
Low‐Activation Compositionally Complex Alloys for Advanced Nuclear Applications—A Review
Low‐activation compositionally complex alloys (LACCAs) are advanced metallic materials primarily composed of low‐activation elements, offering advantages such as rapid compliance with operational standards and safe recyclability. This review highlights their potential for extreme high‐temperature irradiation environments as structural materials for ...
Yangfan Wang+8 more
wiley +1 more source
This study explores aerosol jet‐printed (AJP) surface roughness, its effects on the performance of microwave electronics, and its process contributors. First, an electromagnetic model is vetted for AJP's unique roughness signature. Simulations are built which show process‐induced roughness is as significant as conductor resistivity in driving microwave
Christopher Areias, Alkim Akyurtlu
wiley +1 more source