Efficient exploitation of the massive amount of modern-day life science data
Studying chemistry has become easier and faster thanks to the development of a dedicated Python library to set up and run advanced calculations on parallel supercomputers. Whereas researchers in the past had to spend a large fraction of their time in making the appropriate inputs for modelling programs and then again in retrieving and processing the resulting data, many of these steps can now be run automatically. This makes it possible to study much larger sets of reactions in the computer to better identify trends in chemical reactivity or to calculate carrier cooling or hot-injection processes in semiconductor nanocrystals. The resulting software QMFlows is available as open source and will be used by company and academic researchers. The Noodles software that was initiated in this project finds also applicability in modelling areas outside chemistry in automating also other types of complex software.