No other energy technology has such a widespread scientific demand, ranging from nuclear physics, chemistry, material science, biology, medicine and others to any aspect of engineering, computer science, simulation and ending in social science and social behavior.
We introduce atomate, an open-source Python framework for computational materials science simulation, analysis, and design with an emphasis on automation and extensibility.
Things like atomic and atmospheric science simulations, for instance, that would take years to grind through on a desktop but can be turned around in days on a supercomputer.
In computer science, simulations of evolution using evolutionary algorithms and artificial life started in the 1960s and were extended with simulation of artificial selection.
Because well-designed games keep players coming back for more, we wanted to know if "stitching" together short science simulations with a narrative storyline would positively affect engagement and learning.
Moreover, many existing workflow engines do not leverage parallel and distributed computers, making them unsuitable for executing computational science simulations.
For completeness, we mention here that other projects have also invested effort in standardising ontologies for computational materials science simulations, especially for atomistic and/or DFT-based methods.
This science simulation advice is also touted by the PhET group that creates high quality science simulations https://phet.colorado.edu (Johnson-Glenberg et al., 2014a, 2014b).
To that end, many science simulations are being created that are also gamified.
Students are provided an automatic assessment of the computational artifacts they build (games or science simulations).
