Data-driven uncertainty quantification for systematic coarse-grained models-Reference-Cited by-同舟云学术

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Data-driven uncertainty quantification for systematic coarse-grained models

Published:2020-07-02 Issue:2-3 Volume:18 Page:348-368
ISSN:1539-445X
Container-title:Soft Materials
language:en
Short-container-title:Soft Materials

Author:

Jin Tangxin¹,Chazirakis Anthony²³,Kalligiannaki Evangelia²^ORCID,Harmandaris Vagelis²³⁴^ORCID,Katsoulakis Markos A.¹^ORCID

Affiliation:

1. Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA, USA

2. Institute of Applied and Computational Mathematics, Foundation for Research and Technology - Hellas, Heraklion, Greece

3. Department of Mathematics and Applied Mathematics, University of Crete, Heraklion, Greece

4. Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus

Publisher

Informa UK Limited

Subject

Condensed Matter Physics,General Materials Science,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/1539445X.2020.1765803

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Bottom-Up Transient Time Models in Coarse-Graining Molecular Systems;Multiscale Modeling & Simulation;2023-12-06

2. Understanding the Interaction between Polybutadiene and Alumina via Density Functional Theory Calculations and Machine-Learned Atomistic Simulations;The Journal of Physical Chemistry C;2022-09-27

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