ASH: A Multi-Scale, Multi-Theory Modeling Program.

Area of Science:

• Computational Chemistry
• Quantum Mechanics
• Molecular Dynamics

Background:

• The computational chemistry software landscape is diverse, with numerous quantum mechanics (QM) and molecular mechanics (MM) programs.
• Machine learning interatomic potentials (MLIP) are increasingly used in computational chemistry.
• Existing software often tightly couples calculation methods with specific computational tasks.

Purpose of the Study:

• Introduce ASH, a flexible Python modeling program for multi-scale, multi-theory calculations.
• Provide a unified environment for diverse computational chemistry workflows.
• Facilitate advanced simulations like QM/MM and molecular dynamics.

Main Methods:

• ASH is a Python library separating computational jobs from calculation methods (QM, MM, ML).
• It interfaces with various QM programs (ORCA, pySCF, etc.) and OpenMM for MM.
• Supports hybrid QM/MM, QM/ML, ML/MM, and ONIOM calculations.
• Integrates with Plumed for enhanced sampling and free-energy simulations.

Main Results:

• ASH enables flexible integration of QM, MM, and ML methods.
• Facilitates hybrid simulations for complex systems like proteins and molecular crystals.
• Supports advanced molecular dynamics and enhanced sampling techniques.
• Provides interfaces to a wide range of established computational chemistry software.

Conclusions:

• ASH offers a highly flexible computational chemistry environment.
• It empowers researchers to perform complex, multi-theory simulations.
• ASH is a powerful tool for advancing computational chemistry research and discovery.