Structural searches using isopointal sets as generators: densest packings for binary hard sphere mixtures.

Area of Science:

• Crystallography
• Materials Science
• Computational Chemistry

Background:

• Crystal structure prediction often involves computationally intensive searches.
• Existing algorithms utilize random particle reorganizations and numerical techniques like simulated annealing.
• High-dimensional search spaces pose significant challenges in optimizing crystal properties.

Purpose of the Study:

• To develop a dimensionality reduction technique for crystal structure searches.
• To exploit crystal symmetries for more efficient optimization of extensive properties.
• To identify densest packing structures for binary mixtures of hard spheres.

Main Methods:

• Restricting structure searches to predefined isopointal sets.
• Breaking down the search problem into smaller, manageable sub-problems.
• Applying the method to binary hard sphere mixtures with varying radius ratios.

Main Results:

• The method successfully identifies densest packed structures for hard sphere mixtures.
• For the A(2)B system, optimal packings were found within a single isopointal set across a wide radius ratio range.
• For the AB composition, two isopointal sets were sufficient to find densest packings over an extended radius ratio range.

Conclusions:

• Restricting searches to isopointal sets is an effective strategy for crystal structure prediction.
• This approach significantly reduces the search space dimensionality by leveraging crystal symmetries.
• The method advances the efficiency and scope of discovering optimal crystal structures, particularly for complex mixtures.