Cluster Analysis in Liquids: A Novel Tool in TRAVIS

Area of Science:

• Computational chemistry
• Materials science
• Chemical physics

Background:

• Analyzing molecular clustering is crucial for understanding chemical systems.
• Existing methods may lack universality or applicability to complex mixtures.

Purpose of the Study:

• To introduce a novel, open-source cluster analysis tool (TRAVIS) based solely on atom distances.
• To demonstrate the universality and size-independence of this cluster analysis method.
• To apply the method to complex chemical systems, including ionic liquids and their mixtures with water.

Main Methods:

• Development of a cluster analysis algorithm based exclusively on atom distances.
• Application to model systems, pure water simulations (classical and ab initio MD), and ionic liquid/water mixtures (ab initio MD).
• Utilizing the open-source TRAVIS software for implementation and analysis.

Main Results:

• The cluster analysis is demonstrated to be size-independent for pure water.
• Consistent clustering behavior observed in both classical and ab initio molecular dynamics simulations, indicating universality.
• Successful analysis of individual component clustering and inter-component clustering in ionic liquid-water mixtures.

Conclusions:

• The novel atom-distance-based cluster analysis is a universal and size-independent method.
• TRAVIS software provides a powerful tool for analyzing molecular clustering in diverse and complex chemical environments.
• The method effectively characterizes clustering in mixtures, offering insights into their structural properties.