7. Precise control of molecular biology and nanochemistry. Our increasing ability to manipulate and control the molecular biology of cells is an example of successful molecular engineering exploiting an existing molecular machinery to produce new molecules, often with predefined function. The recently developed CRISPR/Cas gene editing technique is an easy to use approach with incredibly far reaching consequences. It is not only an amazing example for our understanding and capabilities to modify molecular processes in functional and living matter, it is also a remarkable advance in bio-engineering technology that allows us to modify the genome of an organism at single-nucleotide precision. In this respect it straightforwardly extents the more traditional biochemical toolbox bio-engineering that can be used to produce specific molecules. Combined with experimental automation and control through a virtual reality set-up this is a way to actually produce molecular structures designed in silico. Naturally, such a level of sophistication in understanding and manipulating the biochemical machinery of cells is an example for what would like to achieve in artificial soft and hard nanochemistry settings. And surely, matter simulation will be a decisive tool to point the way. 8. Disruptive computing technologies. Quantum computers employ the rules of quantum physics such as entanglement and superposition to carry out computations. The earliest suggested application for them is the simulation of quantum matter. Quantum computers can provide an exponential advantage over classical computers for the simulation of chemistry . This has been shown theoretically and experimentally realized in several occasions in demonstration experiments . Although no quantum computer with error-corrected qubits has been built so far, the severe efforts in major companies, start-ups, and academia make the emergence of moderately sized quantum computers for actual applications in chemistry in the near future rather likely. As quantum computers will then increase in power, they will be disruptive for materials and chemical simulation as they will provide exact, rather than approximate answers for the solution of the Schrödinger equation. 9. Matter imaging technology at the atomistic and larger length scales. Coupled to driver 6, the emergence of advanced spectroscopy techniques for the study of matter has led to an increase in the available experimental data both in terms of resolution, area studied and variety of signals that are recorded. This makes characterization a prime source of data that can directly interact with the output of simulation. In this way, it can help to constrain models "on the fly" and refine digital chemical hypothesis.