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.

中文翻译：

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在图形处理单元上高效实现蒙特卡洛算法，以模拟多孔材料中的吸附。


我们在开源代码 gRASPA 中展示了 Monte Carlo 仿真速度和功能的增强功能，与 Monte Carlo 的串行 CPU 实现相比，该代码使用图形处理单元 （GPU） 实现了显著的性能改进。该代码支持各种 Monte Carlo 仿真，包括规范集成 （NVT）、大规范、NVT Gibbs、Widom 测试粒子插入和连续分数分量 Monte Carlo。大经典过渡矩阵蒙特卡洛 （GC-TMMC） 的实现和允许金属有机框架 （MOF） 结构的不同成分不同移动的新功能分别体现了 gRASPA 用于精确自由能计算和增强吸附研究的能力。高吞吐量计算 （HTC） 模式的引入允许在单个 GPU 设备上进行许多 Monte Carlo 模拟，以加速材料发现。该代码可以结合机器学习 （ML） 电位，这可以通过 Mg-MOF-74 中 CO2 吸附的大规范蒙特卡洛模拟来说明，与使用传统力场的模拟相比，它与实验的一致性要好得多。gRASPA 的开源性质促进了科学的可重复性和开放性，用户可以向代码添加功能并针对自己的目的进行优化。该代码是用 CUDA/C++ 和 SYCL/C++ 编写的，以支持不同的 GPU 供应商。gRASPA 代码在 https://github.com/snurr-group/gRASPA 上公开提供。