Data centric nanocomposites design via mixed-variable Bayesian optimization-Reference-Cited by-同舟云学术

Data centric nanocomposites design via mixed-variable Bayesian optimization

Published:2020 Issue:8 Volume:5 Page:1376-1390
ISSN:2058-9689
Container-title:Molecular Systems Design & Engineering
language:en
Short-container-title:Mol. Syst. Des. Eng.

Author:

Iyer Akshay¹²³^ORCID,Zhang Yichi¹²³,Prasad Aditya⁴³,Gupta Praveen⁴³,Tao Siyu¹²³^ORCID,Wang Yixing¹²³,Prabhune Prajakta⁵⁶³^ORCID,Schadler Linda S.¹⁷³,Brinson L. Catherine⁵⁶³,Chen Wei¹²³^ORCID

Affiliation:

1. Department of Mechanical Engineering

2. Northwestern University

3. USA

4. Department of Material Science & Engineering Rensselaer Polytechnic Institute

5. Department of Mechanical Engineering & Material Science

6. Duke University

7. The University of Vermont

Abstract

Integrating experimental data with computational methods enables multicriteria design of nanocomposites using quantitative and qualitative design variables.

Funder

National Science Foundation

Center for Hierarchical Materials Design

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Industrial and Manufacturing Engineering,Process Chemistry and Technology,Energy Engineering and Power Technology,Biomedical Engineering,Chemical Engineering (miscellaneous),Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2020/ME/D0ME00079E

Reference49 articles.

1. J. P. Holdren , Materials genome initiative for global competitiveness, National Science and technology council OSTP , Washington, USA , 2011

2. Computational Design of Hierarchically Structured Materials

3. Dielectric spectroscopy analysis using viscoelasticity-inspired relaxation theory with finite element modeling

4. Predicting the breakdown strength and lifetime of nanocomposites using a multi-scale modeling approach

5. X. Li , et al. , Rethinking Interphase Representations for Modeling Viscoelastic Properties for Polymer Nanocomposites, 2018 , arXiv preprint arXiv:1811.06238

Cited by 19 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. AI for dielectric capacitors;Energy Storage Materials;2024-08

2. Marginal Probability-Based Integer Handling for CMA-ES Tackling Single- and Multi-Objective Mixed-Integer Black-Box Optimization;ACM Transactions on Evolutionary Learning and Optimization;2024-06-08

3. Development of Pd-immobilized porous polymer catalysts via Bayesian optimization;Polymer Journal;2024-06-06

4. Prevention of Leakage in Machine Learning Prediction for Polymer Composite Properties;Journal of Chemical Information and Modeling;2024-04-20

5. Benchmarking inverse optimization algorithms for materials design;APL Materials;2024-02-01