Engineering economical and scalable processes for direct air capture (DAC) from humid air relies on atomistic modeling to search and identify promising sorbent materials from a va ...

A systematic review of 160 studies finds hybrid quantum machine learning competitive with classical models using fewer ...

Machine learning, with its ability to analyze large datasets and identify patterns, is particularly well-suited to address the challenges presented by the vast and complex data generated in ...

One of the shared, fundamental goals of most chemistry researchers is the need to predict a molecule's properties, such as its boiling or melting point. Once researchers can pinpoint that prediction, ...

A new machine learning model predicts strain-dependent soil damping from routine geotechnical data, reducing seismic ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of payments. We live in a world where machines can understand speech, recognize faces, and even generate ...

Overview Machine learning platforms support model development, training, testing, deployment, and performance ...

Machine learning components are enabling advances in self-driving cars, the power grid, and robotic medicine, but what are the implications for safety? Decades of research and practice in safety ...

A team of computer scientists, astronomers and historians in Berlin has used machine-learning applications to learn more about the evolutionary history of European astronomical thought in the 15th and ...

Are you contemplating a PhD and interested in economic or social science applications of machine learning? You might be a good fit for our pre-doc position. The Center for Applied Artificial ...

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