Hi Ingmar,
Thanks for sharing your great perspective! To be honest, I am also relatively new to Julia and I am constantly learning it throughout research code. Here are some of my temporal thoughts on those points.
It is so true that a lot of initiative packages will be phased out, even backed up by a big name, just like a startup. For the story of Arrhenius.jl, I was intended to write and I have spent quite a lot of time finding existing solutions. It is really difficult to come up with a new package while there existing a lot of great ones, like Canetra Chemkin Pro (Ansys) Converge, etc. I believe we need some killer apps to motivate a new app. That's to say if, in Julia, it runs two times faster, people may not buy it. Not to mention that it is not easy since the language of C++ is fast enough. The chemistry acceleration, like mechanism reduction, QSSA, better ODE solver with pre-condition/sparsity, etc can be built on top of Cantera as well.
What I see as something promising with Julia is to bring in more data science algorithms, like machine learning, auto differentiation, neural networks, to do some tasks that we can not do in the past. Those directions are actually once popped out twenty years ago in combustion and popular again thosedays. But, I guess too many people in combustion, it is still unclear how far this wave could travel. Therefore, the challenge of discussing what to make with Julia is a little bit different from the past when we know what the task is. But the good news is that TensorFlow and PyTorch have already told us what's the core functionally for machine learning. I am trying to build some killer apps of hybrid existing kinetic models with neural network models for missing species and pathways for modeling complex systems like condensed phase fuels, surface chemistry. In such a task, we must have to enable auto-differentiation over the entire reaction source term. Quite a lot of ML research is learning sub-models inside a big model. Those research directions could be the driving force.
My personal experience with Arrhenius.jl is that implementing equations is relatively easier compared to interpreting reaction mechanisms. Not even to mention defining a standard for mechanism files, like YAML. This could add to your points on more efforts into the functionality of Cantera's inflexible interpretation of reactions and exchanging information with other platforms.
Regards,
Weiqi