i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations
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| Published in: | arXiv.org (Jul 10, 2024), p. n/a |
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| Main Author: | |
| Other Authors: | , , , , , , , , , , , , , , |
| Published: |
Cornell University Library, arXiv.org
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| Online Access: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3078791340 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 024 | 7 | |a 10.1063/5.0215869 |2 doi | |
| 035 | |a 3078791340 | ||
| 045 | 0 | |b d20240710 | |
| 100 | 1 | |a Litman, Yair | |
| 245 | 1 | |a i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations | |
| 260 | |b Cornell University Library, arXiv.org |c Jul 10, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Simulation | ||
| 653 | |a Algorithms | ||
| 653 | |a Python | ||
| 653 | |a Rapid prototyping | ||
| 653 | |a Neural networks | ||
| 653 | |a Modular structures | ||
| 653 | |a Machine learning | ||
| 653 | |a Electronic structure | ||
| 653 | |a Computer simulation | ||
| 700 | 1 | |a Kapil, Venkat | |
| 700 | 1 | |a Feldman, Yotam M Y | |
| 700 | 1 | |a Tisi, Davide | |
| 700 | 1 | |a Begušić, Tomislav | |
| 700 | 1 | |a Fidanyan, Karen | |
| 700 | 1 | |a Fraux, Guillaume | |
| 700 | 1 | |a Higer, Jacob | |
| 700 | 1 | |a Kellner, Matthias | |
| 700 | 1 | |a Li, Tao E | |
| 700 | 1 | |a Pós, Eszter S | |
| 700 | 1 | |a Stocco, Elia | |
| 700 | 1 | |a Trenins, George | |
| 700 | 1 | |a Hirshberg, Barak | |
| 700 | 1 | |a Rossi, Mariana | |
| 700 | 1 | |a Ceriotti, Michele | |
| 773 | 0 | |t arXiv.org |g (Jul 10, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3078791340/abstract/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2405.15224 |