One Conductor and One Score to Rule Them All: A Backend Agnostic Optimizer Employing Nonredundant Symmetrized Coordinates for Exploring Accuracy Ladders

Abstract

Abstract Reliable composite geometries require stationary points of one potential energy surface assembled from derivatives at the same nuclear configuration. We introduce an open-source, multiplatform framework combining an optimization driver, local, symmetrized, nonredundant internal or Cartesian coordinates, and interchangeable electronic-structure backends. The driver exchanges Cartesian geometries with the backends and assembles their energies and derivatives according to machine-readable frozen protocols specifying electron correlation, basis-set completion, and core–valence effects. A standard reference test (referred to as Baker-30) compares the internal-coordinate optimizer with the Gaussian optimizer. Larger systems illustrate symmetrized Cartesian coordinates; five composite methods are then assessed on a common 20-molecule panel. The resulting composite-gradient optimization is auditable and extensible while allowing each backend to contribute its strongest capabilities. The tested models give bond-length errors on the milliangstrom scale; reported core–valence shifts range from 0.8 to 4.2 mÅ.