The Middle Science: Traversing scale in complex many-body systems
The challenge of appropriately including many-body affects are well-known throughout the electronic structure and condensed matter scientific communities. Describing the interactions amongst particles (be they electrons, molecules, colloids...) beyond simple pairwise correlations underpins the most pressing theoretical and modeling tasks of our generation. Accurate many-body theories are required to predict and understand phenomena that traverse length and timescales - from the behavior of quantum materials to photosynthesis. This article provides a fresh perspective on the development of new theories that leverage the most recent advances to data science - a roadmap of how graph theory, computational topology, machine learning, and other methods are dramatically accelerating scientific innovation. Historical emphasis has been placed upon many-body methods at the smallest scale of electrons (partially inspired by Feynman's famous lecture of 1959 entitled “There’s Plenty of Room at the Bottom: An Invitation to Enter a New Field of Physics”). Advanced modeling and simulation capabilities, however, alongside mathematical methods that identify complex multidimensional correlations, create a framework for expanding our consideration of many-body affects to systems that approach realistic complexity and size and evolve across timescales of many orders of magnitude - a “Middle Science”.