The past several decades have witnessed tremendous strides in the capabilities of computational chemistry simulations, driven in large part by the extensive parallelism offered by powerful computer clusters and scalable programming methods in high performance computing (HPC). However, such massively parallel simulations increasingly require more complicated software to achieve good performance across the vastly diverse ecosystem of modern heterogeneous computer systems. Furthermore, advanced "multi-resolution" methods for modeling atoms and molecules continue to evolve, and scientific software developers struggle to keep up with the hardships involved with building, scaling, and maintaining these coupled code systems.

This dissertation describes these challenges facing the computational chemistry community in detail, along with recent solutions and techniques that circumvent some primary obstacles. In particular, I describe several projects and classify them by the 3 primary models used to simulate atoms and molecules: quantum mechanics (QM), molecular mechanics (MM), and coarse-grained (CG) models. Initially, the projects investigate methods for scaling simulations to larger and more relevant chemical applications within the same resolution model of iveither QM, MM, or CG. However, the grand challenge lies in effectively bridging these scales, both spatially and temporally, to study richer chemical models that go beyond single-scale physics and toward hybrid QM/MM/CG models. This dissertation concludes with an analysis of the state of the art in multiscale computational chemistry, with an eye toward improving developer productivity on upcoming computer architectures, in which we require productive software environments, enhanced support for coupled scientific workflows, useful abstractions to aid with data transfer, adaptive runtime systems, and extreme scalability. This dissertation includes previously published and co-authored material, as well as unpublished co-authored material.