There is a lot of outdated code in physics and chemistry. It is in Fortran because no one wants to rewrite it.

It is actually more difficult to parallelize fortran code on supercomputers compared to modern distributed computing libraries like HPX, which is a C++ library. Fortran will also perform much worse as you won't beat the DAG schedulers in HPX.

Fortran is also slower, because you can't write generic high performance code. This is also true of C. You need something akin to templating which allows you to optimize expressions at compile-time like you can do in C++. You need generic code for all but the most trivial operations, else it is very difficult and time consuming to build more complex operations like those that build into neural networks.

Additionally, CUDA is native C++ (and again can be optimized generically which you can't do once you have C or Fortran APIs), so if you want to seriously take advantage of vectorization, you should look at using GPUs. It is likely that if a place has a supercomputer, they probably also have a GPU cluster.

All these reasons are why C++ is the king of performance programming.