Efficient Implementation of Monte Carlo Algorithms on Graphical Processing Units for Simulation of Adsorption in Porous Materials

• Published in: Journal of chemical theory and computation 2024-12, Vol.20 (23), p.10649-10666
• Main Authors: Li, Zhao, Shi, Kaihang, Dubbeldam, David, Dewing, Mark, Knight, Christopher, Vázquez-Mayagoitia, Álvaro, Snurr, Randall Q.
• Format: Article
• Language: English
• Subjects: Adsorption; Algorithms; C++ (programming language); Computer simulation; Condensed Matter, Interfaces, and Materials; Free energy; Graphics processing units; Machine learning; Metal-organic frameworks; Monte Carlo simulation; Open source software; Porous materials; Source code
• ISSN: 1549-9618; 1549-9626; 1549-9626
• DOI: 10.1021/acs.jctc.4c01058

We present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle insertions, and continuous-fractional component Monte Carlo. Implementation of grand canonical transition matrix Monte Carlo (GC-TMMC) and a novel feature to allow different moves for the different components of metal–organic framework (MOF) structures exemplify the capabilities of gRASPA for precise free energy calculations and enhanced adsorption studies, respectively. The introduction of a High-Throughput Computing (HTC) mode permits many Monte Carlo simulations on a single GPU device for accelerated materials discovery. The code can incorporate machine learning (ML) potentials, and this is illustrated with grand canonical Monte Carlo simulations of CO2 adsorption in Mg-MOF-74 that show much better agreement with experiment than simulations using a traditional force field. The open-source nature of gRASPA promotes reproducibility and openness in science, and users may add features to the code and optimize it for their own purposes. The code is written in CUDA/C++ and SYCL/C++ to support different GPU vendors. The gRASPA code is publicly available at https://github.com/snurr-group/gRASPA.