Research news on Data-driven alloy design

Data-driven alloy design integrates computational methods, machine learning, and advanced manufacturing to create metallic alloys and composites with tailored properties. Emphasis is placed on metal 3D printing and related additive processes as platforms for controlling solidification, microstructure, and defect formation in systems such as aluminum, titanium, magnesium, high-entropy, and shape-memory alloys. By linking processing parameters, atomic-scale mechanisms, and multiscale models to mechanical, thermal, and environmental performance, this area accelerates discovery and optimization of structural and functional materials for demanding applications.

Engineering

Diffusion-based AI model successfully trained in electroplating

Electrochemical deposition, or electroplating, is a common industrial technique that coats materials to improve corrosion resistance and protection, durability and hardness, conductivity and more. A Los Alamos National Laboratory ...

Electronics & Semiconductors

Photonic chip packaging can withstand extreme environments

Researchers at the National Institute of Standards and Technology (NIST) have developed a new way to package photonic integrated circuits—tiny chips that convey information using light instead of electricity—so they can survive ...

Engineering

Eco-friendly plastic plates could replace steel bars in concrete

Researchers at the University of Sharjah have demonstrated that concrete can be reinforced using polymer plates instead of steel bars, with the new material showing superior strength, ductility, and energy dissipation. The ...

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