Predicting mechanical properties of non-equimolar high-entropy carbides using machine learning

Authors

S. F. Yu (ORCID: https://orcid.org/0000-0003-0354-3767)
Shu-guang Cheng (ORCID: https://orcid.org/0000-0003-3823-4569)
Meng Guo (ORCID: https://orcid.org/0000-0003-1180-8104)
Ruizhi Zhang (ORCID: https://orcid.org/0000-0001-7229-769X)
Xi Zhao (ORCID: https://orcid.org/0000-0001-6680-1685)
Jiming Zheng (ORCID: https://orcid.org/0009-0000-8251-2154)

Institutions

Northwest University (CN)
Northwest Institute For Non-Ferrous Metal Research (CN)
Jinan Institute of Quantum Technology (CN)

Publication Details

Journal: Digital Discovery
Published: 2024-12-12
DOI: https://doi.org/10.1039/d4dd00243a
Citations: 4
Primary Topic: High Entropy Alloys Studies
Type: article
Field-Weighted Citation Impact: 0.85

Funders

Key Industry Innovation Chain of Shaanxi
National Key Research and Development Program of China
Key Research and Development Projects of Shaanxi Province

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Predicting mechanical properties of non-equimolar high-entropy carbides using machine learning

S. F. Yu, Shu-guang Cheng, Meng Guo, Ruizhi Zhang et al.

Dec 12, 2024

4 citations

Digital Discovery

High Entropy Alloys Studies

0.85

article

Predicting mechanical properties of non-equimolar high-entropy carbides using machine learning

S. F. Yu, Shu-guang Cheng, Meng Guo, Ruizhi Zhang, Xi Zhao, Jiming Zheng

December 12, 2024article en

4 citations

Abstract

No abstract available for this paper.

Digital DiscoveryVol. 4(1)

Northwest University (CN), Northwest Institute For Non-Ferrous Metal Research (CN), Jinan Institute of Quantum Technology (CN)

Key Industry Innovation Chain of Shaanxi, National Key Research and Development Program of China, Key Research and Development Projects of Shaanxi Province

Openalex Percentile: Top 29%

High Entropy Alloys Studies

0.85

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.