There is a growing need in the simulation community for software that provides a transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) simulation framework employing energies from ab initio methods and machine-learning interatomic potentials (MLIPs). We introduce a Python library (ASE-MC) that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package. Now, we can combine the powerful tools used to build systems and perform ab initio and MLIP in ASE with MC simulation algorithms to sample the configurational space with a concise Python script. After presenting the design philosophy, we demonstrate the flexibility of our approach using selected examples. These example simulations include liquid water described with a message-passing MLIP in the canonical and isothermal–isobaric ensembles, sampling the characteristic dihedral angle of biphenyl and comparing an MLIP to first-principles calculations, and a grand canonical Monte Carlo simulation of ammonia adsorption on Pt(111). These examples showcase the main features of the software, which include flexibility in the choice of ab initio or MLIP engine, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, the ability to add custom MC moves to the move set, and how users can condense complex MC workflows into a single Python script. Finally, this library serves as a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of the work and application to new systems.
Wilson, Woodrow N., et al. "Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials." Journal of Chemical Theory and Computation, vol. 21, no. 20, Oct. 2025. https://doi.org/10.1021/acs.jctc.5c01148
Wilson, Woodrow N., Bharadwaj, Vivek S., & Rai, Neeraj (2025). Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials. Journal of Chemical Theory and Computation, 21(20). https://doi.org/10.1021/acs.jctc.5c01148
Wilson, Woodrow N., Bharadwaj, Vivek S., and Rai, Neeraj, "Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials," Journal of Chemical Theory and Computation 21, no. 20 (2025), https://doi.org/10.1021/acs.jctc.5c01148
@article{osti_3011911,
author = {Wilson, Woodrow N. and Bharadwaj, Vivek S. and Rai, Neeraj},
title = {Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials},
annote = {There is a growing need in the simulation community for software that provides a transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) simulation framework employing energies from ab initio methods and machine-learning interatomic potentials (MLIPs). We introduce a Python library (ASE-MC) that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package. Now, we can combine the powerful tools used to build systems and perform ab initio and MLIP in ASE with MC simulation algorithms to sample the configurational space with a concise Python script. After presenting the design philosophy, we demonstrate the flexibility of our approach using selected examples. These example simulations include liquid water described with a message-passing MLIP in the canonical and isothermal–isobaric ensembles, sampling the characteristic dihedral angle of biphenyl and comparing an MLIP to first-principles calculations, and a grand canonical Monte Carlo simulation of ammonia adsorption on Pt(111). These examples showcase the main features of the software, which include flexibility in the choice of ab initio or MLIP engine, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, the ability to add custom MC moves to the move set, and how users can condense complex MC workflows into a single Python script. Finally, this library serves as a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of the work and application to new systems.},
doi = {10.1021/acs.jctc.5c01148},
url = {https://www.osti.gov/biblio/3011911},
journal = {Journal of Chemical Theory and Computation},
issn = {ISSN 1549-9626},
number = {20},
volume = {21},
place = {United States},
publisher = {American Chemical Society},
year = {2025},
month = {10}}
Mississippi State University, MS (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Office of Workforce Development for Teachers & Scientists (WDTS)
Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 20 Vol. 21; ISSN 1549-9618; ISSN 1549-9626