Inverse Design of Low-Resistivity Ternary Gold Alloys via Interpretable Machine Learning and Proactive Search Progress.

Hang Che, Tian Lu, Shumin Cai, Minjie Li, Wencong Lu

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Data-Mining-Aided-Material Design of Doped LaMnOMaterials (Basel, Switzerland) · 2025
Article

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hang CheDepartment of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.

Tian LuShanghai Shuzhiwei Information Technology Co., Ltd., 668 ShangDa Road, Shanghai 200444, China.

Shumin CaiDepartment of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.

Minjie LiDepartment of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.

Wencong LuDepartment of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.ORCID 0000-0001-5361-6122

Funding

Key Research Project of Zhejiang Laboratory No.2021PE0AC02Major Science and Technology Projects of Yunnan Precious Metals Laboratory No. YPML-2023050205National Key Research and Development Program of China 2022YFB3707800Yunnan Precious Metals Laboratory Science and Technology Plan Project No.YPML-2023050208, No. 2023050280

6 · The paper itself

Abstract

Ternary gold alloys (TGAs) are highly regarded for their excellent electrical properties. Electrical resistivity is a crucial indicator for evaluating the electrical performance of TGAs. To explore new promising TGAs with lower resistivity, we developed a reverse design approach integrating machine learning techniques and proactive searching progress (PSP) method. Compared with other models, the support vector regression (SVR) was determined to be the most optimal model for resistivity prediction. The training and test sets yielded R

Indexed as

inverse designmachine learningmaterial discoveryTGAsρ

Identifiers

PMID39063905

DOI10.3390/ma17143614

PMCPMC11278811

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.