A Scalable Route to First-Order Response Properties with Correlated Sampling Phaseless Auxiliary-Field Quantum Monte...

Area of Science:

• Computational Chemistry
• Quantum Mechanics
• Electronic Structure Theory

Background:

• Accurate prediction of chemical properties is crucial for connecting theoretical models with experimental data.
• Correlated electronic structure theories are essential for reliable energy and property predictions.

Purpose of the Study:

• To develop and validate a finite-difference algorithm for computing first-order response properties using phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC).
• To assess the accuracy of ph-AFQMC for predicting electric dipole moments in molecules.

Main Methods:

• Implementation of a finite-difference algorithm for response properties within the ph-AFQMC framework.
• Utilizing a branching correlated sampling approach for efficient computation.
• Comparison with coupled cluster singles and doubles with perturbative triples (CCSD(T)) and experimental data.

Main Results:

• Ph-AFQMC with mean-field trial wave functions accurately predicts electric dipole moments for a diverse set of molecules.
• The accuracy of dipole moment predictions systematically improves with correlated trial wave functions, especially in strongly correlated systems.
• The developed ph-AFQMC approach overcomes limitations of perturbation theories in describing challenging cases like HF dissociation.

Conclusions:

• The presented ph-AFQMC algorithm provides a scalable and accurate method for calculating electric dipole moments.
• The approach demonstrates robustness across various molecular sizes and correlation regimes.
• This work enhances the capability of quantum Monte Carlo methods for predicting molecular properties relevant to experimental measurements.