Analytic gradients and geometry optimization for orbital-optimized pair coupled cluster doubles.

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Molecular Modeling

Background:

• Accurate molecular structure determination is crucial for understanding chemical properties and reactions.
• Analytic gradients are essential for efficient geometry optimization in computational chemistry.
• Orbital-optimized pair coupled-cluster doubles (OOpCCD) offers a promising approach for high-accuracy electronic structure calculations.

Purpose of the Study:

• To introduce a reusable geometry-optimization engine in PyBEST for analytic, gradient-driven molecular structure optimization.
• To implement and validate the first analytic OOpCCD nuclear gradients within a Lagrangian formalism.
• To combine the strengths of PyBEST's electronic structure calculations with geomeTRIC's optimization capabilities.

Main Methods:

• Integration of the PyBEST software with the geomeTRIC optimizer.
• Development of analytic nuclear gradients for OOpCCD using a Lagrangian formalism.
• Utilizing the translation-rotation-internal coordinate (TRIC) framework for efficient optimization steps.

Main Results:

• The OOpCCD-based PyBEST-geomeTRIC workflow demonstrates robust convergence for geometry optimization.
• The method accurately reproduces reference equilibrium geometries and energies within tight tolerances.
• OOpCCD structural parameters show excellent agreement with high-level correlated methods like CCSD(F12c)(T*) and MP2.

Conclusions:

• The developed engine provides an efficient and accurate tool for molecular geometry optimization.
• Analytic OOpCCD gradients offer a significant advancement for high-accuracy electronic structure calculations.
• This work enables more reliable predictions of molecular structures and properties in computational chemistry.