The Road to Affordable Accuracy beyond Small Molecules: From Energetics toward Molecular Structures

ConspectusAccurate equilibrium geometries are fundamental to predictive spectroscopy, reliable thermochemistry, and rational molecular design. Yet achieving high accuracy beyond small molecules remains a formidable challenge. High-level wave function methods, while exceptionally accurate, are computationally prohibitive for systems containing dozens of atoms. Density functional approaches, though efficient, often lack consistent reliability across diverse chemical environments. Though reduced-scaling strategies have enabled precise energy calculations for large systems, equivalent progress in the determination of equilibrium structures has been slower, leaving a persistent gap between the predictive accuracy and computational feasibility.This Account presents an integrated framework that tries to bridge this gap by combining composite quantum-chemical methods, data-driven corrections and fragment-based modeling. At its core lie explicitly correlated composite schemes capable of delivering mÅ/mrad accuracy (usually referred to as spectroscopic accuracy) for the geometrical parameters of molecules with up to about 20 atoms. These methods underpin the construction of a benchmark-quality geometry library (LCB25), comprising nearly 400 fragments encompassing all major functional groups and ring systems relevant to chemical, biological and pharmaceutical applications.Building on this reference, systematic bond-based corrections derived from LCB25 transfer spectroscopic-level accuracy to more affordable double-hybrid and hybrid functionals. Linear regressions suffice for double-hybrid models, while machine-learning Δ-corrections extend the same accuracy to hybrid functionals. Together, these refinements lead to geometries of near-spectroscopic accuracy for medium and large molecules (50-100 atoms) at a fraction of the cost of high-level composite methods. For larger architectures, the Nano-LEGO platform automates the assembly of accurate molecular geometries from preoptimized fragments, preserving the local structural fidelity within complex frameworks.Within this modular and hierarchical approach, continuous chemically meaningful descriptors known as <i>synthons</i> serve as the common language linking fragment-based modeling, data-driven corrections, and machine-learning predictions. This representation facilitates the transfer of local structural information across chemical families and supports the exploration of vast regions of chemical space with controlled accuracy.The same principles extend naturally to the design of functional and sustainable materials. Spectroscopically accurate yet affordable structural predictions are instrumental in the rational development of organocatalysts, molecular components for optoelectronic devices and supramolecular frameworks for applications aligned with the goals of circular economy.These methodological advances are complemented by efficient optimization algorithms, vibrational corrections based on second-order vibrational perturbation theory, and fully interoperable workflows that ensure scalability and robustness. Collectively, they establish a hierarchical data-enriched ecosystem delivering accurate, transferable, and cost-effective molecular geometries. Applications range from atmospheric and astrochemical intermediates to biomolecules, pharmaceuticals, and sustainable molecular materials, paving the way for predictive spectroscopy and structure-based design. All components of this framework are openly available through web platforms and GitHub, promoting transparency, accessibility, and community development.