Ab initio random structure searching.

Area of Science:

• Materials Science
• Computational Chemistry
• Solid-State Physics

Background:

• Understanding atomic arrangement is crucial for predicting material properties.
• First-principles electronic structure methods, like density-functional theory (DFT), are increasingly used for materials structure prediction.
• A need exists for efficient and robust methods to search for stable material structures.

Purpose of the Study:

• To present a novel, powerful, and simple approach for searching material structures using DFT.
• To introduce the ab initio random structure searching (AIRSS) methodology.
• To demonstrate the broad applicability of AIRSS across various material systems.

Main Methods:

• Utilizing density-functional theory (DFT) as the underlying electronic structure method.
• Implementing an ab initio random structure searching (AIRSS) algorithm.
• Applying AIRSS to diverse systems including solids, point defects, surfaces, and clusters.

Main Results:

• AIRSS has been successfully applied to discover stable structures in various material systems.
• New structural findings are presented for iron clusters on graphene, silicon clusters, polymeric nitrogen, hydrogen-rich lithium hydrides, and boron.
• The method proves effective for exploring complex structural landscapes.

Conclusions:

• Ab initio random structure searching (AIRSS) is an effective and versatile tool for materials discovery.
• The AIRSS approach facilitates the understanding of material properties through accurate structure prediction.
• This methodology advances the field of computational materials science.