Behavior of Linear and Nonlinear Dimensionality Reduction for Collective Variable Identification of Small Molecule...

Area of Science:

• Computational Chemistry
• Chemical Physics
• Molecular Dynamics

Background:

• Identifying collective variables (CVs) is crucial for understanding chemical reactions by simplifying complex energy landscapes.
• Bulk solvent motions in condensed-phase reactions often obscure important molecular dynamics, hindering CV identification with standard dimensionality reduction methods.
• While principal component analysis (PCA) is well-studied, the application and effectiveness of nonlinear methods like uniform manifold approximation and projection (UMAP) in solvent-rich systems are less understood.

Purpose of the Study:

• To develop and evaluate novel distance-attenuated projection methods for identifying collective variables (CVs) in chemical reactions within explicit solvent.
• To assess the performance of both linear (PCA) and nonlinear (UMAP) dimensionality reduction techniques when applied to solvent-influenced molecular dynamics data.
• To enable the identification of specific solvent molecules that play significant roles in chemical reactivity.

Main Methods:

• Development of distance-attenuated projection methods for atomic coordinates to preprocess molecular dynamics data.
• Application and comparison of both Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) on raw and preprocessed data.
• Analysis of two distinct chemical reactions to evaluate the methods' performance in different solvent-interaction scenarios.

Main Results:

• Raw molecular dynamics data in solution, when analyzed with PCA and UMAP, are overwhelmingly dominated by bulk solvent motions.
• The proposed distance-attenuated projection methods effectively enable both PCA and UMAP to identify relevant collective variables (CVs) for chemical reactions.
• The enhanced methods successfully pinpoint specific solvent molecules that actively participate in and influence the identified collective variables and reaction pathways.

Conclusions:

• Distance-attenuated projection is a powerful preprocessing step for dimensionality reduction in condensed-phase chemical reactions.
• This approach improves the ability of both PCA and UMAP to extract meaningful collective variables from complex molecular dynamics simulations.
• The methodology facilitates a deeper understanding of solvent effects on reactivity by identifying key solvent-molecule interactions.