Quantum machine learning using atom-in-molecule-based fragments selected on the fly.

First-principles-based exploration of chemical space deepens our understanding of chemistry and might help with the design of new molecules, materials or experiments. Due to the computational cost of quantum chemistry methods and the immense number of theoretically possible stable compounds, comprehensive in silico screening remains prohibitive. To overcome this challenge, we combine atom-in-molecule-based fragments, dubbed 'amons' (A), with active learning in transferable quantum machine learning (ML) models. The efficiency, accuracy, scalability and transferability of the resulting AML models is demonstrated for important molecular quantum properties such as energies, forces, atomic charges, NMR shifts and polarizabilities and for systems including organic molecules, 2D materials, water clusters, Watson-Crick DNA base pairs and even ubiquitin. Conceptually, the AML approach extends Mendeleev's table to account effectively for chemical environments, which allows the systematic reconstruction of many chemistries from local building blocks. Image credit: ESA/Hubble & NASA, Acknowledgement: Judy Schmidt.