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