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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.22951 |
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| _version_ | 1866911292085764096 |
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| author | Galtsov, I. S. Muratov, R. V. Vyskvarko, G. V. Murzov, S. A. Dyachkov, S. A. Levashov, P. R. |
| author_facet | Galtsov, I. S. Muratov, R. V. Vyskvarko, G. V. Murzov, S. A. Dyachkov, S. A. Levashov, P. R. |
| contents | Molecular dynamics is widely used to study various phenomena, such as diffusion, shock wave propagation, and plasma dynamics. A wide range of software packages supports the expanding scope of molecular dynamics applications. However, the quality of simulations depends on force field approximations, ranging from simple models to direct quantum solutions. Recently, machine learning approaches for constructing accurate interatomic potentials have received significant attention. In MDcraft, we integrate these advances into a scalable, physically accurate framework. MDcraft is a comprehensive, modern molecular dynamics platform. It offers a high-level Python API with a user-friendly, script-based interface. The core simulation algorithms are implemented in C++ to ensure robustness and computational efficiency. MDcraft is built for high-performance computing on modern clusters and supports dynamic domain decomposition and load balancing via the Message Passing Interface (MPI) for scalable parallelization. Additionally, MDcraft leverages multithreading within nodes through standard C++ parallelism, enabling efficient use of heterogeneous architectures. We demonstrate the code's capabilities through several examples, including the shock response in aluminum, the shock Hugoniot in argon, and the cold curve of copper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22951 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MDcraft -- a modern molecular dynamics simulation package with machine learning potentials support Galtsov, I. S. Muratov, R. V. Vyskvarko, G. V. Murzov, S. A. Dyachkov, S. A. Levashov, P. R. Computational Physics Molecular dynamics is widely used to study various phenomena, such as diffusion, shock wave propagation, and plasma dynamics. A wide range of software packages supports the expanding scope of molecular dynamics applications. However, the quality of simulations depends on force field approximations, ranging from simple models to direct quantum solutions. Recently, machine learning approaches for constructing accurate interatomic potentials have received significant attention. In MDcraft, we integrate these advances into a scalable, physically accurate framework. MDcraft is a comprehensive, modern molecular dynamics platform. It offers a high-level Python API with a user-friendly, script-based interface. The core simulation algorithms are implemented in C++ to ensure robustness and computational efficiency. MDcraft is built for high-performance computing on modern clusters and supports dynamic domain decomposition and load balancing via the Message Passing Interface (MPI) for scalable parallelization. Additionally, MDcraft leverages multithreading within nodes through standard C++ parallelism, enabling efficient use of heterogeneous architectures. We demonstrate the code's capabilities through several examples, including the shock response in aluminum, the shock Hugoniot in argon, and the cold curve of copper. |
| title | MDcraft -- a modern molecular dynamics simulation package with machine learning potentials support |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2511.22951 |