Speeding up quantum dissipative dynamics of open systems with kernel methods

Ullah, Arif; Dral, Pavlo O.

doi:10.1088/1367-2630/ac3261

Cited by 26 publications

(57 citation statements)

References 98 publications

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“…It was generated as detailed below. 33 Firstly, HEOM calculations for all combinations of the following system and bath parameters: /∆ = {0, 1}, λ/∆ = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, ω c /∆ = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, and β∆ = {0.1, 0.25, 0.5, 0.75, 1}, were performed with QuTiP software package. 158 In our calculations we set ∆ = 1.0.…”

Section: Computational Details a Data Sets For Training Validation An...mentioning

confidence: 99%

“…Secondly, σz (t) are calculated from RDMs and processed into shorter sequences of length T by window slicing. 32,33,123 Namely, for a time series x = x (1) , . .…”

Section: Computational Details a Data Sets For Training Validation An...mentioning

confidence: 99%

“…In general, the size of the window P − 1, or equivalently T , should be treated as a hyperparameter but, following previous work, 33 we set to T = 0.2L. The window slicing is applied to all 1,000 RDMs obtained in HEOM calculations with different system and system-bath parameters.…”

Section: Computational Details a Data Sets For Training Validation An...mentioning

confidence: 99%

“…Later Ullah et al 33 illustrated that KRR methods can also predict long-time dynamics of the spin-boson model very accurately. Recently, CNNs is used to study the excitation energy transfer in Fenna-Matthews-Olson light-harvesting complex.…”

Section: Introductionmentioning

confidence: 99%

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A comparative study of different machine learning methods for dissipative quantum dynamics

́iguez

Ullah

Espinosa

et al. 2022

Preprint

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It has been recently shown that supervised machine learning (ML) algorithms can accurately and efficiently predict the long-time populations dynamics of dissipative quantum systems given only short-time population dynamics. In the present article, we benchmaked 22 ML models on their ability to predict long-time dynamics of a two-level quantum system linearly coupled to harmonic bath. The models include uni- and bidirectional recurrent, convolutional, and fully-connected feed-forward artificial neural networks (ANNs) and kernel ridge regression (KRR) with linear and most commonly used nonlinear kernels. Our results suggest that KRR with nonlinear kernels can serve as inexpensive yet accurate way to simulate long-time dynamics in cases where the constant length of input trajectories is appropriate. Convolutional Gated Recurrent Unit model is found to be the most efficient ANN model.

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Section: Computational Details a Data Sets For Training Validation An...mentioning

confidence: 99%

“…Secondly, σz (t) are calculated from RDMs and processed into shorter sequences of length T by window slicing. 32,33,123 Namely, for a time series x = x (1) , . .…”

Section: Computational Details a Data Sets For Training Validation An...mentioning

confidence: 99%

Section: Computational Details a Data Sets For Training Validation An...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparative study of different machine learning methods for dissipative quantum dynamics

́iguez

Ullah

Espinosa

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Beyond the well known techniques developed within the field of statistical thermodynamics to discretize and integrate Newton's equations of motion [1], in recent years, innovative machine learning methods have appeared to accelerate and improve MD simulations ranging from force fields learning [2,[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] to avoid expensive ab initio evaluations, direct free energy sampling techniques [23][24][25][26] as well as new integrator learning [27][28][29][30][31]. Additionally, extensive software has been developed coupling modern machine learning models with MD techniques [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks

Winkler

Mueller

Sauceda

2022

Mach. Learn.: Sci. Technol.

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Molecular dynamics 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 (ML) methodologies to increase the resolution of molecular dynamics trajectories on-demand within a post-processing step. As a proof of concept, we analyze the performance of bi-directional neural networks such as neural ODEs, Hamiltonian networks, recurrent neural networks and LSTMs, as well as the uni-directional variants as a reference, for molecular dynamics 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 Å 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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