AIASSE-Abinitio augmented structure solving engine.

Area of Science:

• Materials Science
• Computational Chemistry
• Condensed Matter Physics

Background:

• Disordered materials present challenges in characterization due to their lack of long-range order.
• Understanding these materials requires bridging macroscopic properties with atomic and electronic behavior.

Purpose of the Study:

• To introduce a novel hybrid methodology for the holistic description of disordered materials.
• To integrate empirical potential-based refinement of total scattering data with first-principles calculations.
• To provide a software package implementing this methodology.

Main Methods:

• Hybrid methodology combining empirical potential refinement of total scattering data.
• Self-consistent integration with first-principles density functional theory (DFT)-based molecular dynamics.
• Application to diverse disordered systems including dense fluid krypton, SiO2 glass, amorphous ice, and liquid mixtures.

Main Results:

• Demonstration of the methodology's capability to provide a coherent description across multiple scales.
• Successful application to previously studied systems, validating the approach.
• Establishment of a framework for detailed atomic and electronic-level analysis of disordered materials.

Conclusions:

• The developed hybrid methodology offers a powerful tool for studying disordered materials.
• This approach enables a unified understanding from macroscopic diffraction data to atomic and electronic structures.
• The software package facilitates the application of this advanced computational technique.