Iterative training set refinement enables reactive molecular dynamics <i>via</i> machine learned forces

Chen, Lei; Sukuba, Ivan; Probst, Michael; Kaiser, Alexander

doi:10.1039/c9ra09935b

Cited by 17 publications

(24 citation statements)

References 35 publications

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“…The trajectories of MD runs with different angles of the incoming deuterium atoms are analysed to obtain density distributions (histograms) of the angles with which Be atoms are sputtered away [622]. Similar studies have been performed also for other surfaces as well [623].…”

Section: Data Management In Manufacturingmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Section: Data Management In Manufacturingmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Machine learning potential is one of the most critical calculations advances in recent years and has been intensively studied and applied in catalysis [112][113][114][115][116][117][118][119][120][121][122][123][124][125][126][127]. The machine learning potential is a method that uses the machine learning algorithm to find the underneath relationship of the atomic configuration and energy [128].…”

Section: Applications Of Machine Learning In Catalysis 321 Machine Learning Potentialsmentioning

confidence: 99%

Molecular Dynamics and Machine Learning in Catalysts

Liu

Zhu

et al. 2021

Catalysts

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Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.

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“…As discussed in the Introduction, the selection of configurations for the training of machine learning potentials has recently seen great progress towards data-driven and automated approaches. 19,43,45,47,49,50,56,57 Here we use C-NNPs to pursue similar ideas, mainly building upon the workflow established for the automated development of NNPs at coupled cluster level of theory for gas-phase clusters. 47,58 We make use of two basic properties of the committee model to automate the development of C-NNPs.…”

Section: Active Learning Procedures For Committee Neural Network Pote...mentioning

confidence: 99%

“…32,41 Despite the rise of machine learning in molecular simulations and materials science, committee models have not yet been widely used in these fields. Notable exceptions are the well established practice for NNPs to use the difference between two models for the manual improvement of the training set 21,42,43 as first described in Ref. 44, however without combining the predictions of the two models.…”

Section: Introductionmentioning

confidence: 99%

Committee neural network potentials control generalization errors and enable active learning

Schran,

Brezina,

Marsalek

2020

Preprint

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Iterative training set refinement enables reactive molecular dynamics via machine learned forces

Abstract: Reactive self-sputtering from a Be surface is simulated using neural network trained forces with high accuracy. The key in machine learning from DFT calculations is a well-balanced and complete training set of energies and forces obtained by iterative refinement.

Cited by 17 publications

References 35 publications

2022 Review of Data-Driven Plasma Science

2022 Review of Data-Driven Plasma Science

Molecular Dynamics and Machine Learning in Catalysts

Committee neural network potentials control generalization errors and enable active learning

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