Chiroptical spectroscopies are key to determine the absolute configuration of solvated compounds. Combining a genetic algorithm and a hierarchical clustering analysis enables a major improvement of the analysis of chiroptical spectra and the reliability of the assignment. Image from An Artificial Intelligence Approach for Tackling Conformational Energy Uncertainties in Chiroptical Spectroscopies, reproduced under a CC BY 4.0 licence. Researchers from the University of Amsterdam's Van't Hoff Institute for Molecular Sciences have developed a powerful machine learning approach to elucidate the absolute configuration and conformational landscape of complex chiral molecules. In a recent paper in Angewandte Chemie, they describe how the combination of a genetic algorithm with a hierarchical clustering algorithm can predict and boost the performance of chiroptical spectroscopies.