Research on ML in Quantum Science published in Nature Communications
BIFOLD Research Paper on Machine Learning for Quantum Chemistry published in Nature Communications
The Paper “Quantum chemical accuracy from density functional approximations via machine learning” by Mihail Bogojeski, Leslie Vogt-Maranto, Mark E. Tuckerman, Klaus-Robert Müller and Kieron Burke was published in Nature Communications (2020)11:5223. In this paper, the authors from the Machine Learning group at TU Berlin, New York University and University of California leveraged Machine Learning to calculate coupled-cluster energies from DFT densities, reaching much better quantum chemical accuracy on test data than achieved with previous available methods. Moreover, their approach significantly reduced the amount of training data required.