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Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials.

Woodrow N Wilson1,2, Vivek S Bharadwaj3, Neeraj Rai1

  • 1Dave C. Swalm School of Chemical Engineering and Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, Mississippi 39762, United States.

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Summary
This summary is machine-generated.

A new Python library, ASE-MC, enables transparent and reproducible Monte Carlo (MC) simulations using ab initio methods and machine-learning interatomic potentials (MLIPs). This framework simplifies complex simulations for researchers, enhancing the discoverability of scientific insights.

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Area of Science:

  • Computational chemistry and materials science.
  • Development of simulation software and algorithms.

Background:

  • The simulation community requires transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) frameworks.
  • Integrating ab initio methods and machine-learning interatomic potentials (MLIPs) into MC simulations is crucial for advancing computational studies.

Purpose of the Study:

  • To introduce ASE-MC, a Python library enhancing the Atomic Simulation Environment (ASE) with MC simulation capabilities.
  • To provide a flexible and extensible framework for diverse MC simulations using various energy engines.

Main Methods:

  • Developed ASE-MC, a Python library integrating MC algorithms with ASE.
  • Demonstrated flexibility through simulations of liquid water, biphenyl dihedral angles, and ammonia adsorption on Pt(111).
  • Incorporated ab initio and MLIP engines, grand canonical MC with cavity bias, and custom MC move additions.

Main Results:

  • Showcased the ability to combine ASE's system-building and calculation tools with MC algorithms.
  • Successfully performed simulations in canonical, isothermal-isobaric, and grand canonical ensembles.
  • Highlighted the flexibility in engine choice, MC ensemble, and custom move integration.

Conclusions:

  • ASE-MC offers a concise Python scripting approach for complex MC workflows.
  • The library facilitates reproducible MC simulations, enabling easier application to new research systems.
  • This framework supports the transparent and extensible sampling of configurational space in materials simulations.