Graph theory for automatic structural recognition in molecular dynamics simulations.

Area of Science:

• Computational Chemistry
• Chemical Physics
• Materials Science

Background:

• Molecular dynamics (MD) simulations generate vast amounts of data on molecular behavior.
• Analyzing conformational changes in MD trajectories is computationally intensive and complex.
• Existing methods often struggle to maintain atomistic detail or handle multiple trajectories efficiently.

Purpose of the Study:

• To develop novel graph theory algorithms for analyzing MD simulations.
• To enable the identification, temporal tracking, and statistical analysis of molecular conformations.
• To provide a robust and scalable method for understanding dynamic molecular processes.

Main Methods:

• Utilizing graph theory with atomistic granularity, maintaining chemical identity.
• Employing graph isomorphism as a key component for conformation recognition.
• Introducing "orbits" and "reference snapshots" to reduce isomorphism complexity.
• Applying algorithms to diverse MD trajectories, including gas-phase molecules, clusters, and condensed matter.

Main Results:

• Successful identification and tracking of conformational changes such as hydrogen bonding, proton transfer, and coordination number variations.
• Construction of transition graphs to map molecular rearrangements over time.
• Demonstrated applicability to both *ab initio* and classical MD simulations.
• Enabled parallel analysis of multiple trajectories, significantly improving efficiency.

Conclusions:

• The proposed graph theory algorithms offer an effective and automated approach to analyzing MD simulations.
• These methods preserve essential chemical information while efficiently managing computational complexity.
• The algorithms are versatile, applicable to a wide range of systems and simulation types, including coarse-grained models.