Machine Learning Assisted Design of Complex and High Entropy Alloys by Hybrid HiPIMS/Pulsed-DC PVD Process for Low Carbon Energy Applications in Extreme Environments
機械学習支援による複雑・高エントロピー合金の設計：低炭素エネルギー向け極限環境用ハイブリッドHiPIMS/パルスDC PVDプロセス (AI 翻訳)
Paul Foulquier, Ryma Haddad, Ali Assem Mahmoud, Eric Monsifrot, Fanny Balbaud-Celerier, Jean-Philippe Poli, Frederic Schuster
🤖 gxceed AI 要約
日本語
本論文は、機械学習を用いて複雑・高エントロピー合金の設計を効率化し、低炭素エネルギー用途（原子力、高温電解など）向けの保護コーティング開発を加速する。フランスのDIADEMプロジェクトの取り組みを紹介し、AI駆動のPVDプロセスで合金組成と耐食性の関係をモデル化。実験によりその精度と実現可能性を示した。
English
This paper presents a machine learning approach to accelerate the design of complex and high entropy alloys for protective coatings in low-carbon energy applications (nuclear, high-temperature electrolysis). It introduces the French DIADEM initiative and an AI-driven PVD process that models composition-property relationships, demonstrating feasibility and accuracy through experiments.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX文脈では、水素社会や原子力再稼働に伴う極限環境材料の需要が高まっており、本研究成果は国内の材料開発やエネルギーインフラの信頼性向上に寄与する可能性がある。また、AIを活用した材料開発は、日本の製造業の競争力強化にもつながる。
In the global GX context
In the global GX context, this work supports the development of materials for carbon-free energy systems, aligning with international efforts to enhance the durability and safety of nuclear and hydrogen infrastructure. It showcases a data-driven approach that can accelerate materials discovery, relevant to global sustainability goals.
👥 読者別の含意
🔬研究者:Materials scientists and ML researchers can learn about a practical application of AI to alloy design for energy applications.
🏢実務担当者:Corporate R&D teams in energy or materials sectors can explore AI-driven coating development for extreme environments.
🏛政策担当者:Policymakers may note the potential of AI in advancing low-carbon energy technologies and consider supporting similar initiatives.
📄 Abstract（原文）
Complex and high entropy alloys are attracting much attention currently thanks to their mechanical and corrosion resistance properties in harsh environments, in particular needed for carbon-free energy applications. However, their elaboration in bulk and in thin film form in a trial-and-error approach is impractical due to their complexity and the cocktail effect. The recent development of artificial intelligence brings a new possibility for their elaboration and adjustment of their properties. Firstly, we present an overview of Materials and data science research. Then we describe how DIADEM - French initiative for Materials and Data science convergence - tackles the development of innovative coatings for carbon-free energy applications (nuclear, high temperature electrolysis, ...) thanks to the development of a nationwide network of synthesis and characterization platforms - the DIADEM discovery hub. We describe in particular DIADEM-2D, an AI-driven Hybrid HiPIMS/Pulsed-DC PVD process using 4 cathodes in confocal combinatorial configuration. We present the high entropy alloy determination using data from the literature for corrosion resistance in molten salt media and nuclear accidental conditions. An element-independent model gathering deposition parameters and coating properties has been implemented allowing the design of protective coatings with a particular composition. We demonstrate the feasibility of this process and its accuracy.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.48550/arxiv.2608.01903first seen 2026-08-06 04:59:19
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