OpenPathSampling: A Python Framework for Path Sampling Simulations. 2. Building and Customizing Path Ensembles and...

Area of Science:

• Computational chemistry
• Molecular dynamics
• Statistical mechanics

Background:

• Transition path sampling (TPS) is crucial for studying molecular processes.
• Existing frameworks may lack flexibility for novel algorithm development.
• The OpenPathSampling (OPS) package aims to address these limitations.

Purpose of the Study:

• To detail theoretical advancements in the OpenPathSampling (OPS) package.
• To introduce a novel set-based notation for constructing path ensembles.
• To describe customizable Monte Carlo sampling procedures within OPS.

Main Methods:

• Development of a set-based notation for path ensemble construction.
• Implementation of MoveScheme and PathMover objects for customizable Monte Carlo sampling.
• Application of OPS to complex molecular systems and exploration of new algorithms.

Main Results:

• A flexible framework for building complex path ensembles from fundamental ones.
• Detailed description of OPS objects for customizing Monte Carlo sampling procedures.
• Demonstration of path ensemble construction and sampling customization through examples.

Conclusions:

• OPS facilitates standard TPS applications in complex molecular systems.
• OPS supports the development of novel path sampling methodologies.
• The package enhances the exploration of molecular dynamics beyond default methods.