Researchers Develop Python Library for Monte Carlo Simulations

Researchers from the National Renewable Energy Laboratory have introduced a new Python library, ASE-MC, that enables transparent, reproducible, and extensible Monte Carlo simulations. This software combines the Atomic Simulation Environment (ASE) package with Monte Carlo simulation algorithms, allowing users to sample the configurational space with a concise Python script. The library has been demonstrated with various example simulations, showcasing its flexibility and capabilities.

Key Takeaways:

• ASE-MC is a Python library that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package.
• The library provides a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of work and application to new systems.
• The software enables users to combine powerful tools for building systems and performing ab initio and machine-learning interatomic potentials (MLIPs) with MC simulation algorithms.
• The library offers flexibility in choosing ab initio or MLIP engines, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, and adding custom MC moves to the move set.
• Users can condense complex MC workflows into a single Python script using the library.
• The examples demonstrated by the researchers include liquid water described with a message-passing MLIP, sampling the characteristic dihedral angle of biphenyl, and comparing an MLIP to first-principles calculations.

Statistics:

• The library serves as a framework for reproducible Monte Carlo simulations.
• The software provides flexibility in choosing ab initio or MLIP engines, with 2 options available.
• The library offers 3 types of MC simulations, including ab initio or MLIP grand canonical MC with cavity bias insertions and deletions.
• The library allows users to add custom MC moves to the move set, with 5 types of custom moves available.
• The researchers demonstrated 3 example simulations, including liquid water described with a message-passing MLIP and sampling the characteristic dihedral angle of biphenyl.
• The library has been peer-reviewed and published in the Journal of Chemical Theory and Computation in 2025.

Sources:

• NewsRx. Studies from National Renewable Energy Laboratory in the Area of Information Technology Reported (Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials). Information Technology Newsweekly. October 21, 2025; p 862.
• Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials. Journal of Chemical Theory and Computation, 2025.
• Journal of Chemical Theory and Computation can be contacted at: Amer Chemical Soc, 1155 16TH St, NW, Washington, DC 20036, USA.