Medicine is rapidly evolving from statistical, evidence-based approaches to predictive, genotype-directed care, driven by ...
This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure. Discovering new materials with customizable and optimized properties, driven either by ...
Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...
Aerospace and Mechanical Insider on MSN
AI and machine learning transform materials testing
Materials testing remains a cornerstone of engineering and manufacturing, ensuring that components and structures—from ...
Machine learning models narrow down solutions in synthesis, compounding, product design, and more.
This review explains how soft materials, scalable manufacturing, energy-efficient hardware, and AI are converging to create ...
Chiral 2D metal halide perovskites (MHPs) are among the most promising materials for future technologies that exploit the ...
Tech Xplore on MSN
Blame the model, not the machine—better data helps 3D-printed metamaterials match predictions
Additive manufacturing, such as 3D printing, provides an excellent opportunity to design metamaterials: materials with an ...
The emerging convergence of AI-first design principles and environmental consciousness is reshaping how we think about ...
The field of advanced manufacturing for high-performance materials in extreme environments is rapidly evolving, driven by the need for materials that can withstand harsh conditions such as high ...
Researchers at IMDEA Materials Institute have developed an artificial intelligence (AI)-based strategy to predict and assess ...
A new study reveals that dual-atom catalysts behave in a fundamentally different way than scientists previously thought, challenging a long-standing model used to predict catalytic performance.
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