i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913565220274176 |
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| author | Litman, Yair Kapil, Venkat Feldman, Yotam M. Y. Tisi, Davide Begušić, Tomislav Fidanyan, Karen Fraux, Guillaume Higer, Jacob Kellner, Matthias Li, Tao E. Pós, Eszter S. Stocco, Elia Trenins, George Hirshberg, Barak Rossi, Mariana Ceriotti, Michele |
| author_facet | Litman, Yair Kapil, Venkat Feldman, Yotam M. Y. Tisi, Davide Begušić, Tomislav Fidanyan, Karen Fraux, Guillaume Higer, Jacob Kellner, Matthias Li, Tao E. Pós, Eszter S. Stocco, Elia Trenins, George Hirshberg, Barak Rossi, Mariana Ceriotti, Michele |
| contents | Atomic-scale simulations have progressed tremendously over the past decade, largely due to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach extensive length and time scales. The i-PI package facilitates integrating the latest developments in this field with advanced modeling techniques, thanks to a modular software architecture based on inter-process communication through a socket interface. The choice of Python for implementation facilitates rapid prototyping but can add computational overhead. In this new release, we carefully benchmarked and optimized i-PI for several common simulation scenarios, making such overhead negligible when i-PI is used to model systems up to tens of thousands of atoms using widely adopted machine learning interatomic potentials, such as Behler-Parinello, DeePMD and MACE neural networks. We also present the implementation of several new features, including an efficient algorithm to model bosonic and fermionic exchange, a framework for uncertainty quantification to be used in conjunction with machine-learning potentials, a communication infrastructure that allows deeper integration with electronic-driven simulations, and an approach to simulate coupled photon-nuclear dynamics in optical or plasmonic cavities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15224 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations Litman, Yair Kapil, Venkat Feldman, Yotam M. Y. Tisi, Davide Begušić, Tomislav Fidanyan, Karen Fraux, Guillaume Higer, Jacob Kellner, Matthias Li, Tao E. Pós, Eszter S. Stocco, Elia Trenins, George Hirshberg, Barak Rossi, Mariana Ceriotti, Michele Chemical Physics Materials Science Atomic-scale simulations have progressed tremendously over the past decade, largely due to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach extensive length and time scales. The i-PI package facilitates integrating the latest developments in this field with advanced modeling techniques, thanks to a modular software architecture based on inter-process communication through a socket interface. The choice of Python for implementation facilitates rapid prototyping but can add computational overhead. In this new release, we carefully benchmarked and optimized i-PI for several common simulation scenarios, making such overhead negligible when i-PI is used to model systems up to tens of thousands of atoms using widely adopted machine learning interatomic potentials, such as Behler-Parinello, DeePMD and MACE neural networks. We also present the implementation of several new features, including an efficient algorithm to model bosonic and fermionic exchange, a framework for uncertainty quantification to be used in conjunction with machine-learning potentials, a communication infrastructure that allows deeper integration with electronic-driven simulations, and an approach to simulate coupled photon-nuclear dynamics in optical or plasmonic cavities. |
| title | i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations |
| topic | Chemical Physics Materials Science |
| url | https://arxiv.org/abs/2405.15224 |