2026-09-21T01:21:50Zhttps://ecommons.cornell.edu/server/oai/requestoai:ecommons.cornell.edu:1813/1144952026-05-15T19:42:46Zcom_1813_35col_1813_47
Wang, Menglin
Yeo, Jingjie
Gomes, Carla
2024-04-05T18:36:35Z
2024-04-05T18:36:35Z
2023-08
ProQuest Submission ID: 11899
ProQuest Publication ID: 30575584
https://hdl.handle.net/1813/114495
https://doi.org/10.7298/azec-bp06
16219275
52 pages
Supplemental file(s) description: None.
High-entropy ceramics (HEC) materials have gained significant attention for their unique composition and diverse properties. Low thermal conductivity (κ) makes them promising materials for high-temperature applications. However, predicting the thermal properties of HECs is challenging due to the complex nature of their atomic interactions. This project explores the use of machine learning (ML) techniques to discover new compositions of HEC materials with low κ. To achieve this, a dataset combining experimental κ values of 166 HECs and conventional ceramics at various temperatures (5 K-1832 K) is constructed. The input features are then generated based on chemical composition. In the next learning process, various nonlinear regression ML models, including Kernel Ridge Regression (KRR), Random Forest Regression (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost) are employed. As a result, the RF, KRR, and XGBoost exhibit excellent performance, achieving high R2 scores over 0.90. Additionally, the accuracy of this model was tested using new cases of four compounds, which was not seen for the model before, where a good matching between experimental and predicted κ of the new HECs was found. To gain further insights into the ML models, feature importance analyses are conducted to identify key compositional features that influence κ in HECs. As an application, ML models are leveraged to explore a large composition space and screen promising HECs (30,382) to find candidates with ultralow κ (<1 W m-1K-1) at room temperature. Among the screened HECs, the relationship between important features and the predicted κ is investigated. Size disorder is identified as an important feature in lower κ values of HECs. Overall, this study provides valuable insights into the thermal properties of HECs and demonstrates the efficacy of ML in HECs discovery and design. Accordingly, using the periodic table, compositional information, and ML models, the κ-temperature behavior of a huge number of HECs can be accurately predicted.
en
High Enotropy Ceramics
Machine Learning
Thermal Conductivity
DISCOVERY OF HIGH ENTROPY CERAMICS WITH LOW THERMAL CONDUCTIVITY THROUGH MACHINE LEARNING
dissertation or thesis
https://newcatalog.library.cornell.edu/catalog/16219275
application/pdf
application/zip
Materials Science and Engineering
Cornell University
Master of Science
M.S., Materials Science and Engineering
https://hdl.handle.net/1813/59810.2
Publication
Cornell Theses and Dissertations
Wang, Menglin
5893a6ea-7af3-41d7-abc6-04bcd26ab5df
open.access
open.access
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="0d7cede0-b36c-40a4-8ad3-61fe4b98714a">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>en</Language>
<Title>DISCOVERY OF HIGH ENTROPY CERAMICS WITH LOW THERMAL CONDUCTIVITY THROUGH MACHINE LEARNING</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2023-08</PublicationDate>
<DOI>https://doi.org/10.7298/azec-bp06</DOI>
<Authors>
<Author>
<DisplayName>Wang, Menglin</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<OrgUnit />
</Publisher>
</Publishers>
<Keyword>High Enotropy Ceramics</Keyword>
<Keyword>Machine Learning</Keyword>
<Keyword>Thermal Conductivity</Keyword>
<Abstract>High-entropy ceramics (HEC) materials have gained significant attention for their unique composition and diverse properties. Low thermal conductivity (κ) makes them promising materials for high-temperature applications. However, predicting the thermal properties of HECs is challenging due to the complex nature of their atomic interactions. This project explores the use of machine learning (ML) techniques to discover new compositions of HEC materials with low κ. To achieve this, a dataset combining experimental κ values of 166 HECs and conventional ceramics at various temperatures (5 K-1832 K) is constructed. The input features are then generated based on chemical composition. In the next learning process, various nonlinear regression ML models, including Kernel Ridge Regression (KRR), Random Forest Regression (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost) are employed. As a result, the RF, KRR, and XGBoost exhibit excellent performance, achieving high R2 scores over 0.90. Additionally, the accuracy of this model was tested using new cases of four compounds, which was not seen for the model before, where a good matching between experimental and predicted κ of the new HECs was found. To gain further insights into the ML models, feature importance analyses are conducted to identify key compositional features that influence κ in HECs. As an application, ML models are leveraged to explore a large composition space and screen promising HECs (30,382) to find candidates with ultralow κ (<1 W m-1K-1) at room temperature. Among the screened HECs, the relationship between important features and the predicted κ is investigated. Size disorder is identified as an important feature in lower κ values of HECs. Overall, this study provides valuable insights into the thermal properties of HECs and demonstrates the efficacy of ML in HECs discovery and design. Accordingly, using the periodic table, compositional information, and ML models, the κ-temperature behavior of a huge number of HECs can be accurately predicted.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>