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A multi-fidelity machine learning approach to high throughput materials screening

Engineering and Technology

A multi-fidelity machine learning approach to high throughput materials screening

C. Fare, P. Fenner, et al.

Dive into a groundbreaking multi-fidelity machine learning approach that revolutionizes high-throughput materials screening by dynamically learning relationships between experimental and computational data. This innovative research by Clyde Fare, Peter Fenner, Matthew Benatan, Alessandro Varsi, and Edward O. Pyzer-Knapp offers a remarkable three-fold reduction in optimization costs.

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