Researchers at Seoul National University in South Korea have proposed a deep contrast learning method called Local Atomic Circumstance Contrast Learning (LACL). It learns to mitigate the distributional differences between two geometrical conformations by comparing different conformational generation methods, thus solving the domain bias problem faced by data-driven deep learning algorithms in predicting molecular properties in advanced quantum chemistry.
