An accurate and efficient framework for modelling the surface chemistry of ionic materials

Area of Science:

• Computational chemistry
• Materials science
• Surface science

Background:

• Quantum-mechanical simulations provide atomic-level insights into surface chemical processes vital for catalysis, energy storage, and carbon capture.
• Achieving high accuracy in these simulations is challenging; while density functional theory (DFT) is efficient, its predictions can be inconsistent.
• Correlated wavefunction theory (WFT) methods offer higher accuracy but are computationally expensive and require significant user input, limiting their application to surfaces.

Purpose of the Study:

• To develop an automated computational framework for applying correlated wavefunction theory to the surfaces of ionic materials.
• To achieve computational costs comparable to DFT while maintaining high accuracy.
• To facilitate routine application of accurate WFT methods to complex surface chemistry problems.

Main Methods:

• Developed an automated framework utilizing multilevel embedding approaches.
• Applied correlated wavefunction theory to ionic material surfaces.
• Validated the framework against experimental adsorption enthalpies.

Main Results:

• Reproduced experimental adsorption enthalpies for 19 diverse adsorbate-surface systems.
• Resolved ambiguities in adsorption configurations for several systems.
• Provided benchmarks for assessing the accuracy of density functional theory methods.
• Demonstrated computational costs approaching those of DFT.

Conclusions:

• The automated framework makes correlated wavefunction theory practical for studying ionic material surfaces.
• This advancement enables more reliable predictions for heterogeneous catalysis, energy storage, and greenhouse gas sequestration.
• The open-source nature of the framework promotes wider adoption and further research in surface chemistry.