Discovering and characterizing new materials is important for unlocking advances in fields like clean energy, advanced ...

Machine learning has moved past its initial experimental phase. In earlier years, development often focused on creating the largest possible models to see what capabilities might appear. Today, the ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

Machine Learning (ML) and Artificial Intelligence (AI) have become essential technologies across industries, automating tasks at a speed and scale far beyond human capabilities. However, building ...

Materials with advanced customized properties drive innovation in a number of real-life applications across various fields, such as information technology, transportation, green energy and health ...

Why it’s important not to over-engineer. Equipped with suitable hardware, IDEs, development tools and kits, frameworks, datasets, and open-source models, engineers can develop ML/AI-enabled, ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

Engineers at NIMS Develop a System That Captures All the Elements of Trial and Error in Material Design, Enabling Reliable ...

Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper appearing in Patterns. Though large language models ...

Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper publishing April 22 in the Cell Press ...