Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy-Reference-Cited by-同舟云学术

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Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy

Published:2022-09-26 Issue:22 Volume:17 Page:
ISSN:1861-4728
Container-title:Chemistry – An Asian Journal
language:en
Short-container-title:Chemistry An Asian Journal

Author:

Wang Xiangdong¹²,Lu Tian¹,Zhou Wenyan³,Ji Xiaobo⁴,Lu Wencong¹⁴^ORCID,Yang Jiong¹^ORCID

Affiliation:

1. Shanghai Materials Genome Institute Shanghai University 200444 Shanghai P. R. China

2. School of Physics and Electronic Science East China Normal University 200241 Shanghai P. R. China

3. Department Sino-Platinum Metals Co., Ltd 650106 Kunming P. R. China

4. Department of Chemistry College of Sciences Shanghai University 200444 Shanghai P. R. China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Chemistry,Biochemistry,Organic Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/asia.202200771

Reference46 articles.

1. Selecting metal alloy electric contact materials for MEMS switches

2. Solvent-Free Oxidation of Primary Carbon-Hydrogen Bonds in Toluene Using Au-Pd Alloy Nanoparticles

Cited by 6 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. Accurate prediction of magnetocaloric effect in NiMn-based Heusler alloys by prioritizing phase transitions through explainable machine learning;Rare Metals;2024-08-29

2. Inverse Design of Low-Resistivity Ternary Gold Alloys via Interpretable Machine Learning and Proactive Search Progress;Materials;2024-07-22

3. Interpretable Predictive Model for Inclusions in Electroslag Remelting Based on XGBoost and SHAP Analysis;Metallurgical and Materials Transactions B;2024-03-26

4. Discovery of the Layered Thermoelectric Compound GeBi₂Se₄ and Accelerating Its Performance Optimization by Machine Learning;Advanced Materials Technologies;2023-12-03

5. Interpretable Machine Learning Workflow for Evaluating and Analyzing the Performance of High-Entropy GeTe-Based Thermoelectric Materials;ACS Applied Electronic Materials;2023-07-27

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