Data-driven discovery of a formation prediction rule on high-entropy ceramics

Yan, Yonggang; Pei, Zongrui; Gao, Michael C.; Misture, Scott; Wang, Kun

doi:10.1016/j.actamat.2023.118955

Title: Data-driven discovery of a formation prediction rule on high-entropy ceramics

Journal Article · 30 April 2023 · Acta Materialia

DOI: https://doi.org/10.1016/j.actamat.2023.118955 · OSTI ID:1975791

^[1]; Pei, Zongrui ^[2]; Gao, Michael C. ^[3]; Misture, Scott ^[1];

^[1]

Alfred University, NY (United States)
New York University, NY (United States)
National Energy Technology Lab. (NETL), Albany, OR (United States)

The interest in high entropy ceramics (HECs) has increased steadily due to their superior properties. However, the prediction of their formation still poses challenges for the discovery of new systems. Here, we discover a rational rule for designing single-phase high entropy metal diborides (HEBs) using data-driven approach. The machine learning (ML) model is trained on data collected via high-throughput experiments (HTEs). K nearest neighbor (KNN) model shows an experimental validation accuracy of 93.75%. By implementing interpretable ML method, we demonstrate that a mismatch of the bonds between boron and transition metals (δ_B-TM) dominates the formation of HEBs. We propose an empirical rule that HEBs favor forming a single phase when δ_B-TM < 3.66; otherwise, multiphase. The rule has a high accuracy of 93.33% for new HEBs predictions. In addition, we contribute 165 high quality HEBs data in total, which can promote the development of materials informatics in HEBs. Furthermore, this data-driven strategy can be expanded to accelerate the search for new HECs, paving a pathway to design novel HECs with superior properties rapidly.

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Research Organization:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE); National Science Foundation (NSF)

Grant/Contract Number:: 2018306

OSTI ID:: 1975791

Report Number(s):: DOE/NETL-2023/3863

Journal Information:: Acta Materialia, Vol. 253; ISSN 1359-6454

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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