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Transferring chemical and energetic knowledge between molecular systems with machine learning

Chemistry

Transferring chemical and energetic knowledge between molecular systems with machine learning

S. Heydari, S. Raniolo, et al.

Discover a groundbreaking machine learning methodology that enhances knowledge transfer between molecular systems! This innovative approach, developed by Sajjad Heydari, Stefano Raniolo, Lorenzo Livi, and Vittorio Limongelli, focuses on classifying high and low free-energy conformations, boasting an impressive AUC of 0.92 in its predictions.

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