機械学習による高エントロピー合金発見
Ziyuan Rao1, Po-Yen Tung1,2, Ruiwen Xie3
1Max-Planck-Institut für Eisenforschung GmbH, Düsseldorf, Germany.
Science (New York, N.Y.)
|October 6, 2022
まとめ
新しい高エントロピーのインヴァー合金を見つけるための 積極的な学習戦略を開発しました この方法では 熱膨張が非常に低い2つの合金が 素早く発見され 材料の発見が加速されました
科学分野:
- 材料科学
- 金属工学
- コンピュータ材料科学
背景:
- 高エントロピー合金 (HEA) は,従来の材料では利用できないユニークな特性を持っています.
- HEAの設計は,広大な構成空間と伝統的な熱力学的ルールの限界のために困難です.
- 特定の特性を有する HEA の発見はしばしば 偶然に起因する.
研究 の 目的:
- 高エントロピーのインヴァール合金の 設計と発見を加速する
- 複雑な作曲風景を把握するためのアクティブ・ラーニング戦略を開発する.
- 非常に低い熱膨張係数を持つ高熱電池を特定する.
主な方法:
- 密度関数理論,熱力学的計算,実験的検証を統合したアクティブ・ラーニング.
- 繰り返しの材料設計と特徴付けのための閉ループアプローチを採用しました.
- 機械学習を使って 数百万のコンポジションを スパースデータでスクリーニングしました
主要な成果:
- 2つの新しい高エントロピーインヴァー合金を発見した.
- 非常に低い熱膨張係数 (約. 2 × 10−6 K−1 300 Kで
- 高次元空間でのアクティブ・ラーニング戦略の有効性を実証した.
結論:
- 提案されているアクティブ・ラーニング・ストラテジーは,高速で自動化されたHEAの発見を可能にします.
- このアプローチは,熱,磁気,電気的特性を最適化するのに適しています.
- 特定された合金は,低熱膨張材料の重要な進歩を表しています.
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