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

Structural economic models, while parsimonious and interpretable, often exhibit poor data fit and limited forecasting performance. Machine learning models, by contrast, offer substantial flexibility ...

AI and machine learning are revolutionizing drug discovery, development, and lifecycle management, addressing industry ...

A team of researchers from the University of Chicago's Pritzker School of Molecular Engineering (UChicago PME) has used ...

A collaborative approach to training AI models can yield better results, but it requires finding partners with data that ...

Antimicrobial resistance (AMR) is an increasingly dangerous problem affecting global health. In 2019 alone, methicillin-resistant Staphylococcus aureus (MRSA) accounted for more than 100,000 global ...

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

When experiments are impractical, density functional theory (DFT) calculations can give researchers accurate approximations of chemical properties. The mathematical equations that underpin the ...

To create their contactless approach, the researchers created a machine learning algorithm capable of analyzing aspects of ...