A New Version of the Plasma Database for Plasma Physics in the Data Center for Plasma Properties

Park, Jun-Hyoung; Choi, Hyung Rim; Chang, Won‐Seok; Chung, Sang Young; Kwon, Deuk-Chul; Song, Mi‐Young; Yoon, and Jung-Sik

doi:10.5757/asct.2020.29.1.005

Cited by 10 publications

(8 citation statements)

References 5 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we extracted these from various databases. All the data used for training and testing the regression models were automatically scraped from the following databases: QDB [8], NFRI [19], KIDA [14], and UDfA [16]. These four widely-used databases provide a good quantity of kinetic data a for binary heavy-species collisions at or near room-temperature.…”

Section: Training Datamentioning

confidence: 99%

“…However, for this to work all the three parameters also need to be present in the training dataset as targets for supervised learning and most of our data sources do not give a full set of Arrhenius coefficients; in practice, the majority of reactions in our dataset are characterized by a single reaction rate constant parameter, α. The kinetic data in the NFRI database [19] are provided as a series of reaction rate coefficient values for different temperatures. In principle, the desired Arrhenius coefficients could be fitted to these data, but this would require at least three data points.…”

Section: Targetsmentioning

confidence: 99%

“…Each reaction needs to have its kinetic parameters specified and these data are not always readily available. The are a number of online databases of kinetic processes for modeling low-temperature plasmas such as Quantemol Database (QDB) [9], and LXCat [12] which largely contains data on electron collisions, Phys4Entry [13] for modeling atmospheric re-entry plasmas; databases for astrochemical modeling include KIDA [14,15], UDfA [16] and BASECOL [17]; fusion oriented databases include the Japanese NIFS database [18], the Korean NFRI database [19] and the ALADDIN database maintained by the International Atomic Energy Agency [20]. These databaes provide only a finite and limited set of data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A regression model for plasma reaction kinetics

Haničinec

Mohr

Tennyson

2023

J. Phys. D: Appl. Phys.

View full text Add to dashboard Cite

Machine learning (ML) is used to provide reactions rates appropriate for models of low temperature plasmaswith a focus on A + B $\rightarrow$ C + D binary chemical reactions. The regression model is trainedon data extracted from the QBD, KIDA, NFRI and UfDA databases. The regression model used a variety of dataon the reactant and product species, some of which also had to be estimated using ML.The final model is a voting regressor comprising three distinct optimized regression models: a support vectorregressor, random forest regressor and a gradient-boosted trees regressor model; this model is made freely available via a github repository.As a use case, the ML results are used to augment the chemistry of a BCl$_3$/H$_2$ gas mixture.

show abstract

Section: Training Datamentioning

confidence: 99%

Section: Targetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A regression model for plasma reaction kinetics

Haničinec

Mohr

Tennyson

2023

J. Phys. D: Appl. Phys.

View full text Add to dashboard Cite

show abstract

“…In their work, ML was used to provide rate coefficients of binary chemical reactions using three distinct optimized regression models: an SVM model, a random forest model, and a gradient-boosted trees model. The models were trained on kinetic data for binary heavy-species collisions at or near room-temperature extracted from the QBD, 74 KIDA, 75 NFRI, 76 and UfDA 77 databases. After removing duplicate reactions, the final data set consisted of 9470 reactions involving 1080 distinct species.…”

Section: Reviewmentioning

confidence: 99%

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Trieschmann,

Vialetto,

Gergs

2023

J. Micro/Nanopattern. Mats. Metro.

View full text Add to dashboard Cite

“…Such chemical databases are expected to increase in coming years as the chemistry induced by plasmas is utilised in more applications, including medical processes [636,716] and waste treatment [717,718]. [689] Excitation processes Plasma physics Atomic cross sections QDB [707] Excitation processes Technological plasmas Chemical reaction rates NIFS [710] Excitation processes Fusion Chemical reaction rates NFRI [711] Excitation processes Fusion Chemical reaction rates ALADDIN [712] Excitation processes Fusion Chemical reaction rates Phys4Entry [713] Vibrational excitation Atmospheric re-entry Heavy particle inelastic cross sections. BASECOL [714] Rotational excitation Astrophysics Heavy particle inelastic cross sections.…”

Section: Materials Databasementioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

View full text Add to dashboard Cite

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...

show abstract

A New Version of the Plasma Database for Plasma Physics in the Data Center for Plasma Properties

Cited by 10 publications

References 5 publications

A regression model for plasma reaction kinetics

A regression model for plasma reaction kinetics

Review: Machine learning for advancing low-temperature plasma modeling and simulation

2022 Review of Data-Driven Plasma Science

Contact Info

Product

Resources

About