Polymers, such as plastics, are essential in many aspects of life and industry, from packaging and cars to medical devices and optic fibers.

The training process shapes a function that can map as much of the input onto its corresponding (known) output as possible. After that, the trained model labels unfamiliar examples. Unsupervised ...

The natural protein universe is vast, and yet, going beyond and designing new proteins not observed in nature can yield new ...

More information: Xinwei Su et al, Machine learning modeling assisted intelligent process analysis for high - performance virus filtration, Journal of Membrane Science (2025).

Like everything else associated with machine learning, deep learning, and large language models, the generative AI development process is subject to change, often with little or no notice.

Machine learning can be supervised, unsupervised, or semi-supervised. In supervised learning, models are trained on labeled data, meaning the input data is paired with the correct output.