Active Learning Revolutionizes Molecular Spectroscopy with 90% Fewer Data Points
Researchers have developed a groundbreaking machine learning approach that dramatically reduces the computational cost of predicting molecular infrared spectra. The PALIRS framework achieves laboratory-grade accuracy while requiring 90% fewer data points than traditional methods, potentially revolut