High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networksopen access
- Authors
- Winkler, Ludwig; Mueller, Klaus-Robert; Sauceda, Huziel E.
- Issue Date
- 1-6월-2022
- Publisher
- IOP Publishing Ltd
- Keywords
- super-resolution; molecular dynamics; bi-directional recurrent neural networks; trajectory learning; LSTM; machine learning; molecular systems
- Citation
- MACHINE LEARNING-SCIENCE AND TECHNOLOGY, v.3, no.2
- Indexed
- SCIE
SCOPUS
- Journal Title
- MACHINE LEARNING-SCIENCE AND TECHNOLOGY
- Volume
- 3
- Number
- 2
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/142977
- DOI
- 10.1088/2632-2153/ac6ec6
- ISSN
- 2632-2153
- Abstract
- Molecular dynamics (MD) simulations are a cornerstone in science, enabling the investigation of a system's thermodynamics all the way to analyzing intricate molecular interactions. In general, creating extended molecular trajectories can be a computationally expensive process, for example, when running ab-initio simulations. Hence, repeating such calculations to either obtain more accurate thermodynamics or to get a higher resolution in the dynamics generated by a fine-grained quantum interaction can be time- and computational resource-consuming. In this work, we explore different machine learning methodologies to increase the resolution of MD trajectories on-demand within a post-processing step. As a proof of concept, we analyse the performance of bi-directional neural networks (NNs) such as neural ODEs, Hamiltonian networks, recurrent NNs and long short-term memories, as well as the uni-directional variants as a reference, for MD simulations (here: the MD17 dataset). We have found that Bi-LSTMs are the best performing models; by utilizing the local time-symmetry of thermostated trajectories they can even learn long-range correlations and display high robustness to noisy dynamics across molecular complexity. Our models can reach accuracies of up to 10(-4) angstrom in trajectory interpolation, which leads to the faithful reconstruction of several unseen high-frequency molecular vibration cycles. This renders the comparison between the learned and reference trajectories indistinguishable. The results reported in this work can serve (1) as a baseline for larger systems, as well as (2) for the construction of better MD integrators.
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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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