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@article{2222179,
author = {Ulbrich, Pavol and Waldner, Manuela and Furmanová, Katarína and Marques, Sérgio Manuel and Bednář, David and Kozlíková, Barbora and Byška, Jan},
article_location = {United States},
article_number = {1},
doi = {http://dx.doi.org/10.1109/TVCG.2022.3209411},
keywords = {Molecular dynamics;structure;node-based visualization;progressive analytics},
language = {eng},
issn = {1077-2626},
journal = {IEEE Transactions on Visualization and Computer Graphics},
title = {sMolBoxes: Dataflow Model for Molecular Dynamics Exploration},
url = {https://ieeexplore.ieee.org/abstract/document/9903280},
volume = {29},
year = {2023}
}
TY - JOUR
ID - 2222179
AU - Ulbrich, Pavol - Waldner, Manuela - Furmanová, Katarína - Marques, Sérgio Manuel - Bednář, David - Kozlíková, Barbora - Byška, Jan
PY - 2023
TI - sMolBoxes: Dataflow Model for Molecular Dynamics Exploration
JF - IEEE Transactions on Visualization and Computer Graphics
VL - 29
IS - 1
SP - 581-590
EP - 581-590
PB - IEEE Computer Society
SN - 10772626
KW - Molecular dynamics;structure;node-based visualization;progressive analytics
UR - https://ieeexplore.ieee.org/abstract/document/9903280
N2 - We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.
ER -
ULBRICH, Pavol, Manuela WALDNER, Katarína FURMANOVÁ, Sérgio Manuel MARQUES, David BEDNÁŘ, Barbora KOZLÍKOVÁ a Jan BYŠKA. sMolBoxes: Dataflow Model for Molecular Dynamics Exploration. \textit{IEEE Transactions on Visualization and Computer Graphics}. United States: IEEE Computer Society, 2023, roč.~29, č.~1, s.~581-590. ISSN~1077-2626. Dostupné z: https://dx.doi.org/10.1109/TVCG.2022.3209411.
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