[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117824-en":3,"doc-seo-117824-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117824,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning Accelerated Discovery of Corrosion-resistant High-entropy Alloys - Research","Corrosion drives catastrophic failure in structurally engineered components, motivating the search for corrosion-resistant high-entropy alloys. High-dimensional composition and configuration spaces make conventional experimental trial-and-error and brute-force ab initio approaches impractical. The work introduces a physics-informed machine-learning framework that predicts corrosion resistance using formability metrics, surface energy, and Pilling-Bedworth ratios, combining random-forest models with high-fidelity machine-learning interatomic potentials trained on first-principles data.","arXiv :2307 .06384v1 [ cond-mat .mtrl-sci ] 12 Jul 2023  \nMachine Learning Accelerated Discovery of Corrosion-resistant High-entropy Alloys  \nCheng Zeng* , Andrew Neils, Jack Lesko, Nathan Post*  \nThe Roux Institute, Northeastern University, Portland, Maine, 04101, United States.  \n*Corresponding authors: Email: [c.zeng@northeastern.edu and n.post@northeastern.edu](c.zeng@northeastern.edu and n.post@northeastern.edu), Tel: +1  \n401-396-6668 and +1 781-605-8671  \nJuly 14, 2023  \nAbstract  \nCorrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional composition and configuration space, making materials designs via experimental trial-and-error or brute-force ab initio calculations almost impossible. Here we develop a physics-informed machine-learning framework to identify corrosion-resistant high-entropy alloys. Three metrics are used to evaluate the corrosion resistance, including single-phase formability, surface energy and Pilling-Bedworth ratios. We used random forest models to predict the single-phase formability, trained on an experimental dataset. Machine learning inter-atomic potentials were employed to calculate surface energies and Pilling-Bedworth ratios, which are trained on first-principles data fast sampled using embedded atom models. A combination of random forest models and high-fidelity machine learning potentials represents the first of its kind to relate chemical compositions to corrosion resistance of high-entropy alloys, paving the way for automatic design of materials with superior corrosion protection. This framework was demonstrated on AlCrFeCoNi high-entropy alloys and we identified composition regions with high corrosion resistance. Machine learning predicted lattice constants and surface energies are consistent with values by first-principles calculations. The predicted single-phase formability and corrosion-resistant compositions of AlCrFeCoNi agree well with experiments. This framework is general in its application and applicable to other materials, enabling high-throughput screening of material candidates and potentially reducing the turnaround time for integrated computational materials engineering.  \nKeywords: High-entropy alloy, Corrosion protection, Machine learning potential, Random forest classification Graphical TOC:  \nAl alloys  \nTi  \nalloys  \nNi alloys  \nStainless  \nsteels  \nalloys  \nHigh-entropy  \nCorrosion resistance  \nPitting potential [V]  \nCorrosion density [ A/cm 2 ]  \n1 Introduction  \nHigh-entropy alloys are generally defined as alloys comprising no less than four elements and the percentage of each principal element is between 5 at.% and 35 at.% . The high-entropy concept was coined by Cantor [1] and Yeh [2] for equiatomic alloys with no less than five elements in 2004 almost the same time. The definition has been slightly extended to non-equimolar alloys with no less than four principal elements. This new class of materials has attracted increasing attention, found to display superior materials performance for mechanical properties [3–7], radiation resistance [8, 9] and corrosion resistance [10–12] . The high entropy of mixing usually leads to the formation of a disordered single phase for high-entropy alloys, such as face-centered cubic (FCC), body-centered cubic (BCC) and hexagonal closely-packed structures (HCP) [13, 14] . The homogeneous single phase improves passivity. In addition, high-entropy alloys can consist of elements with high passivation potency such as nickel, chromium, aluminum and titanium, leading to high pitting corrosion resistance.  \nConventional corrosion-resistant alloys are mostly found by serendipity. Advances in physical theories, computational hardware and algorithms allow for rapid screening of candidate materials, paving the way for integrated computational materials engineering which aims ","cbCaiu5Nmz77vKI3","https://ap.wps.com/l/cbCaiu5Nmz77vKI3","pdf",885246,1,26,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why are high-entropy alloys challenging to design for corrosion resistance?\",\"answer\":\"Their chemical composition and atomic configuration spaces are high-dimensional, making experimental trial-and-error and brute-force ab initio calculations nearly impractical.\"},{\"question\":\"What metrics does the framework use to evaluate corrosion resistance?\",\"answer\":\"It uses single-phase formability, surface energy, and Pilling-Bedworth ratios to quantify corrosion resistance.\"},{\"question\":\"How does the method combine different machine-learning components?\",\"answer\":\"Random-forest models predict single-phase formability from experimental data, while machine-learning inter-atomic potentials compute surface energies and Pilling-Bedworth ratios trained on first-principles data.\"}]","Machine Learning Accelerated Discovery of Corrosion-resistant High-entropy Alloys - 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