MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning

Hasan M. Jamil; Lan Li; Amin Mirkouei

doi:10.1109/BigData55660.2022.10020246

MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning

Hasan M. Jamil, Lan Li, Amin Mirkouei

Micron School of Materials Science and Engineering

University of Idaho

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Designing novel materials and analyzing their properties is a computation intensive process. Increasingly modern machine learning techniques are being exploited in contemporary research to expedite and advance materials studies. One powerful tool available to researchers is the body of scientific knowledge that aids in selecting design models, algorithms, meta-data, and visualization tools to process and analyze experimental and empirical data for an iterative design process, potentially involving a human in the loop, In this preliminary research paper, our goal is to introduce a new machine learning platform, called MatFlow, for automated and knowledge driven design of novel materials and their usage. We outline its architecture and illustrate its functionality with an application in Transition Metal Dichalcogenide (TMD) Heterostructures design of electronic and energy devices.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3423-3431
Number of pages	9
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020246
State	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	17/12/22 → 20/12/22

Keywords

automation
data integration
improved usability
knowledge-based design
machine learning
Material Science
optimization
reverse engineering
workflow

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BigData55660.2022.10020246

Cite this

Jamil, H. M., Li, L., & Mirkouei, A. (2022). MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 3423-3431). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020246

Jamil, Hasan M. ; Li, Lan ; Mirkouei, Amin. / MatFlow : A System for Knowledge-based Novel Materials Design using Machine Learning. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 3423-3431 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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title = "MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning",

abstract = "Designing novel materials and analyzing their properties is a computation intensive process. Increasingly modern machine learning techniques are being exploited in contemporary research to expedite and advance materials studies. One powerful tool available to researchers is the body of scientific knowledge that aids in selecting design models, algorithms, meta-data, and visualization tools to process and analyze experimental and empirical data for an iterative design process, potentially involving a human in the loop, In this preliminary research paper, our goal is to introduce a new machine learning platform, called MatFlow, for automated and knowledge driven design of novel materials and their usage. We outline its architecture and illustrate its functionality with an application in Transition Metal Dichalcogenide (TMD) Heterostructures design of electronic and energy devices.",

keywords = "automation, data integration, improved usability, knowledge-based design, machine learning, Material Science, optimization, reverse engineering, workflow",

author = "Jamil, {Hasan M.} and Lan Li and Amin Mirkouei",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/BigData55660.2022.10020246",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3423--3431",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

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Jamil, HM, Li, L & Mirkouei, A 2022, MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 3423-3431, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17/12/22. https://doi.org/10.1109/BigData55660.2022.10020246

MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning. / Jamil, Hasan M.; Li, Lan; Mirkouei, Amin.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3423-3431 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - MatFlow

T2 - 2022 IEEE International Conference on Big Data, Big Data 2022

AU - Jamil, Hasan M.

AU - Li, Lan

AU - Mirkouei, Amin

PY - 2022

Y1 - 2022

N2 - Designing novel materials and analyzing their properties is a computation intensive process. Increasingly modern machine learning techniques are being exploited in contemporary research to expedite and advance materials studies. One powerful tool available to researchers is the body of scientific knowledge that aids in selecting design models, algorithms, meta-data, and visualization tools to process and analyze experimental and empirical data for an iterative design process, potentially involving a human in the loop, In this preliminary research paper, our goal is to introduce a new machine learning platform, called MatFlow, for automated and knowledge driven design of novel materials and their usage. We outline its architecture and illustrate its functionality with an application in Transition Metal Dichalcogenide (TMD) Heterostructures design of electronic and energy devices.

AB - Designing novel materials and analyzing their properties is a computation intensive process. Increasingly modern machine learning techniques are being exploited in contemporary research to expedite and advance materials studies. One powerful tool available to researchers is the body of scientific knowledge that aids in selecting design models, algorithms, meta-data, and visualization tools to process and analyze experimental and empirical data for an iterative design process, potentially involving a human in the loop, In this preliminary research paper, our goal is to introduce a new machine learning platform, called MatFlow, for automated and knowledge driven design of novel materials and their usage. We outline its architecture and illustrate its functionality with an application in Transition Metal Dichalcogenide (TMD) Heterostructures design of electronic and energy devices.

KW - automation

KW - data integration

KW - improved usability

KW - knowledge-based design

KW - machine learning

KW - Material Science

KW - optimization

KW - reverse engineering

KW - workflow

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U2 - 10.1109/BigData55660.2022.10020246

DO - 10.1109/BigData55660.2022.10020246

M3 - Conference contribution

AN - SCOPUS:85147947153

T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

SP - 3423

EP - 3431

BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 17 December 2022 through 20 December 2022

ER -

Jamil HM, Li L, Mirkouei A. MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 3423-3431. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020246

MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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