This study examines the electrical resistivity of metals and binary,ternary alloy thin films across a broad range of compositions and microstructures through data-driven approaches.Electrical resistivity values for ov...This study examines the electrical resistivity of metals and binary,ternary alloy thin films across a broad range of compositions and microstructures through data-driven approaches.Electrical resistivity values for over 70,000 alloy compositions were measured through high-throughput experiments on combinatorially synthesized specimens.A machine learning prediction model was developed,and an explainable artificial intelligence(XAI)algorithm was utilized to identify the key features influencing electrical resistivity.The results demonstrate that the average valence electron concentration(VECavg)is the most significant descriptor governing the electrical resistivity of these alloys.Electronegativity difference(ΔEN)and mixing entropy(ΔS)were identified as collaborative features contributing to resistivity.The relationships between these features and resistivity are discussed in the context of traditional theoretical frameworks to provide a comprehensive understanding of the electrical behavior of alloys.展开更多
基金supported by the Basic Science Research Program and Creative Materials Discovery Program through the National Research Foundation of Korea(NRF)funded by Ministry of Science and ICT(2020M3D1A1016092)Samsung Research Funding&Incubation Center of Samsung Electronics(SRFC-MA2202-01)+1 种基金Samsung Electronics Co.,Ltd.(IO201211-08077-01)the Institute of Information&Communications Technology Planning&Evaluation(IITP),grant funded by the Korea government(MSIT)under Grant No.RS-2025-02306043.
文摘This study examines the electrical resistivity of metals and binary,ternary alloy thin films across a broad range of compositions and microstructures through data-driven approaches.Electrical resistivity values for over 70,000 alloy compositions were measured through high-throughput experiments on combinatorially synthesized specimens.A machine learning prediction model was developed,and an explainable artificial intelligence(XAI)algorithm was utilized to identify the key features influencing electrical resistivity.The results demonstrate that the average valence electron concentration(VECavg)is the most significant descriptor governing the electrical resistivity of these alloys.Electronegativity difference(ΔEN)and mixing entropy(ΔS)were identified as collaborative features contributing to resistivity.The relationships between these features and resistivity are discussed in the context of traditional theoretical frameworks to provide a comprehensive understanding of the electrical behavior of alloys.
