Physics-Guided Machine Learning for Performance Prediction and Multi-Objective Optimization of High-Conductivity Aluminum Conductors.

Yaojun Miao, Zhikang Cao, Tong Yao, Yufei Wang, Haiyan Gao, Jun Wang, Baode Sun

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Yaojun MiaoShanghai Key Lab of Advanced High-Temperature Materials and Precision Forming and State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Zhikang CaoShanghai Key Lab of Advanced High-Temperature Materials and Precision Forming and State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0009-0004-6413-1365

Tong YaoShanghai Key Lab of Advanced High-Temperature Materials and Precision Forming and State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Yufei WangShanghai Key Lab of Advanced High-Temperature Materials and Precision Forming and State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Haiyan GaoShanghai Key Lab of Advanced High-Temperature Materials and Precision Forming and State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Jun WangShanghai Key Lab of Advanced High-Temperature Materials and Precision Forming and State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Baode SunShanghai Key Lab of Advanced High-Temperature Materials and Precision Forming and State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0002-7155-5391

Funding

Inner Mongolia Research Institute, Shanghai Jiao Tong University 2023XYJG0001-01-01Ministry of Science and Technology of the People's Republic of China 2026ZD0814301National Natural Science Foundation of China 52573367

6 · The paper itself

Abstract

Producing high-conductivity aluminum conductors for power transmission involves 23 trace elements and multiple interconnected thermo-mechanical stages. The ultra-low alloying levels required to preserve high electrical conductivity create a narrow compositional window and highly imbalanced distributions, which hinder traditional data-driven learning. Here, we developed a physics-guided machine-learning framework based on 4458 valid industrial production records to predict tensile strength and electrical resistivity. In addition to raw composition and process parameters, we introduce ratio descriptors (e.g., Fe/Si and Al/Si) and propose a physics-informed metric termed the Equivalent Solute-Heat Index (ESHI) to couple key solute chemistry (Si, Fe, B) with normalized thermal-history intensity. Fe and Si primarily influence resistivity through impurity/solute scattering, while B mainly affects microstructural uniformity via grain refinement. Incorporating ESHI as an augmented signal into the best-performing XGB surrogate markedly improves generalizability, increasing the tensile strength R

Indexed as

aluminum conductorEquivalent Solute–Heat Indexmulti-objective optimizationphysics-guided machine learning

Identifiers

PMID42124245

DOI10.3390/ma19091839

PMCPMC13164778

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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.