1. Uses dynamic models, where things evolve over time (rather than everything being at-equilibrium).
2. Involves a large number of interacting systems (rather than just one system by itself).
3. Involves a lot more species, reactions, and complexity than one'd generally see in a classroom.
4. Involves spatially-variable concentrations, where chemicals have different concentrations at different points in space, rather than being the same throughout (well-mixed). (Actually, they did this both ways: they did the simpler well-mixed model without needing a GPU at all, then they used a GPU in their non-mixed models.)
Each one of those generalizations can make the problem, say, an order-of-magnitude more complex, if not more. Combined, they create a much larger model.
However, it's still largely based around that phase-level scale that students might be familiar with.
Like, in highschool/undergraduate Chemistry classes, students sometimes calculate chemical-concentrations for [equilibrium reactions](https://en.wikipedia.org/wiki/Determination_of_equilibrium_c... ). This paper's basically at the same level.
Unlike highschool modeling, though, this paper:
1. Uses dynamic models, where things evolve over time (rather than everything being at-equilibrium).
2. Involves a large number of interacting systems (rather than just one system by itself).
3. Involves a lot more species, reactions, and complexity than one'd generally see in a classroom.
4. Involves spatially-variable concentrations, where chemicals have different concentrations at different points in space, rather than being the same throughout (well-mixed). (Actually, they did this both ways: they did the simpler well-mixed model without needing a GPU at all, then they used a GPU in their non-mixed models.)
Each one of those generalizations can make the problem, say, an order-of-magnitude more complex, if not more. Combined, they create a much larger model.
However, it's still largely based around that phase-level scale that students might be familiar with.