Navigating the BERMUDA triangle of scientific software: A modular approach to data, hardware, and algorithm design in quantum chemistry and its realization in Python

Modern quantum-chemistry software must support increasingly complex methods, heterogeneous hardware, reusable data structures, and user-facing workflows without losing maintainability. Using PyBEST as a case study, this work discusses how such complexity can be organized within the Python programming language through modular interfaces rather than scattered across method-specific implementations. The central motif is the Balanced pythonic Environment for Research with Modular Unified Design and Abstractions (BERMUDA) Triangle: data representation, computational execution, and algorithmic organization, showcasing how implemented components and ongoing architectural developments—including backend-independent tensor contractions, an effective-Hamiltonian infrastructure, a domain-aware linear-algebra layer, the DAISpY charge-transfer analysis, and graphical input preparation—can be organized within one coherent ecosystem. The resulting perspective treats scalability not only as a numerical goal, but also as a software-design property: scientific codes remain sustainable when new features can be added without obscuring the route from theory to implementation and interpretation.