Real-Space Methods for Ab Initio Modeling of Surfaces and Interfaces under External Potential Bias

Area of Science:

• Computational Materials Science
• Surface Science
• Computational Chemistry

Background:

• Accurate modeling of surfaces and interfaces under external potential bias is crucial for electronic, catalytic, and energy storage devices.
• Existing plane-wave density functional theory (DFT) methods face limitations due to periodic boundary conditions and scalability issues.
• Real-space DFT offers flexibility for generic boundary conditions, making it suitable for surface and interface studies.

Purpose of the Study:

• To introduce and validate two new real-space DFT methods for applying external potential bias to surfaces and interfaces.
• To provide an alternative to plane-wave DFT methods, overcoming their inherent restrictions.
• To enable more accurate and scalable simulations of surface and interface phenomena.

Main Methods:

• Developed two real-space finite-element DFT (DFT-FE) methods for applying external potential bias.
• Method 1: Applied a constant electric field by modifying the DFT Hamiltonian with an auxiliary linear potential.
• Method 2: Directly enforced external potential bias by imposing constraints on the electrostatic potential.

Main Results:

• Validated the constant electric field method against plane-wave DFT on benchmark systems (Li7La3Zr2O12, GaAs, Al).
• Evaluated both methods for ground-state properties like surface and adsorption energies.
• Demonstrated the ability to constrain electrostatic potential in localized regions, a challenge for periodic codes.

Conclusions:

• The developed real-space DFT methods offer a robust alternative to plane-wave approaches for modeling surfaces and interfaces under potential bias.
• These methods overcome periodic boundary condition limitations and offer better scalability for larger systems.
• The framework facilitates accurate investigation of surface and interface phenomena without assumptions or correction schemes.