Group Publishes Work on S0-T1 Fragmentation
We’re thrilled to announce that our recent work “A Fragment Based Approach Towards Curating, Comparing and Developing Machine Learning Models Applied in Photochemistry.” was accepted into Chemical Science! In this work, we developed a novel fragmentation scheme to aid in the prediction of adiabatic singlet-triplet energy gaps. Abstract: The development of Graph Neural Networks for predicting molecular […]
