Analytic Gradients and Geometry Optimization for Orbital-Optimized Pair Coupled Cluster Doubles
Saman Behjou, Iulia Emilia Brumboiu, Katharina Boguslawski

TL;DR
This paper presents a new geometry-optimization engine in PyBEST that utilizes analytic gradients for orbital-optimized pair coupled-cluster doubles, enabling accurate and efficient molecular structure optimization with validated results close to high-level references.
Contribution
The first implementation of analytic OOpCCD nuclear gradients within a Lagrangian formalism, integrated with a robust geometry optimizer, applicable to seniority-zero wavefunctions with orbital optimization.
Findings
Converges robustly on representative systems.
Reproduces reference geometries within tight tolerances.
Deviates minimally from high-level reference structures.
Abstract
We introduce a reusable geometry-optimization engine in PyBEST for analytic, gradient-driven molecular structure optimization, with particular emphasis on orbital-optimized pair coupled-cluster doubles (OOpCCD/AP1roG). The engine interfaces PyBEST with the \texttt{geomeTRIC} optimizer, combining analytic electronic-structure gradients from PyBEST with the translation-rotation-internal coordinate (TRIC) framework, step control, and convergence machinery provided by \texttt{geomeTRIC}. Specifically, we present the first implementation of analytic OOpCCD nuclear gradients within a Lagrangian formalism. Our approach and implementation are generally applicable to any seniority-zero wavefunctions that feature orbital optimization and allow for the evaluation of response one- and two-particle reduced density matrices. Owing to the seniority-zero structure of pCCD and the orbital stationarity…
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Taxonomy
TopicsAdvanced Chemical Physics Studies · Machine Learning in Materials Science · Crystallography and molecular interactions
