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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.08886 |
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| _version_ | 1866909314979987456 |
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| author | Shao, Yunqi Zhang, Chao |
| author_facet | Shao, Yunqi Zhang, Chao |
| contents | PiNNAcLe is an implementation of our adaptive learn-on-the-fly algorithm for running machine-learning potential (MLP)-based molecular dynamics (MD) simulations -- an emerging approach to simulate the large-scale and long-time dynamics of systems where empirical forms of the PES are difficult to obtain.
The algorithm aims to solve the challenge of parameterizing MLPs for large-time-scale MD simulations, by validating simulation results at adaptive time intervals. This approach eliminates the need of uncertainty quantification methods for labelling new data, and thus avoids the additional computational cost and arbitrariness thereof.
The algorithm is implemented in the NextFlow workflow language (Di Tommaso et al., 2017). Components such as MD simulation and MLP engines are designed in a modular fashion, and the workflows are agnostic to the implementation of such modules. This makes it easy to apply the same algorithm to different references, as well as scaling the workflow to a variety of computational resources.
The code is published under BSD 3-Clause License, the source code and documentation are hosted on Github. It currently supports MLP generation with the atomistic machine learning package PiNN (Shao et al., 2020), electronic structure calculations with CP2K (Kühne et al., 2020) and DFTB+ (Hourahine et al., 2020), and MD simulation with ASE (Larsen et al., 2017). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08886 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PiNNAcLe: Adaptive Learn-On-The-Fly Algorithm for Machine-Learning Potential Shao, Yunqi Zhang, Chao Statistical Mechanics Computational Physics PiNNAcLe is an implementation of our adaptive learn-on-the-fly algorithm for running machine-learning potential (MLP)-based molecular dynamics (MD) simulations -- an emerging approach to simulate the large-scale and long-time dynamics of systems where empirical forms of the PES are difficult to obtain. The algorithm aims to solve the challenge of parameterizing MLPs for large-time-scale MD simulations, by validating simulation results at adaptive time intervals. This approach eliminates the need of uncertainty quantification methods for labelling new data, and thus avoids the additional computational cost and arbitrariness thereof. The algorithm is implemented in the NextFlow workflow language (Di Tommaso et al., 2017). Components such as MD simulation and MLP engines are designed in a modular fashion, and the workflows are agnostic to the implementation of such modules. This makes it easy to apply the same algorithm to different references, as well as scaling the workflow to a variety of computational resources. The code is published under BSD 3-Clause License, the source code and documentation are hosted on Github. It currently supports MLP generation with the atomistic machine learning package PiNN (Shao et al., 2020), electronic structure calculations with CP2K (Kühne et al., 2020) and DFTB+ (Hourahine et al., 2020), and MD simulation with ASE (Larsen et al., 2017). |
| title | PiNNAcLe: Adaptive Learn-On-The-Fly Algorithm for Machine-Learning Potential |
| topic | Statistical Mechanics Computational Physics |
| url | https://arxiv.org/abs/2409.08886 |