機械学習による超非圧縮性超硬質材料の検索
Aria Mansouri Tehrani1, Anton O Oliynyk1, Marcus Parry2
1Department of Chemistry , University of Houston , Houston , Texas 77204 , United States.
Journal of the American Chemical Society
|July 17, 2018
まとめ
機械学習により 新しい超硬質物質の発見が加速されます 研究者らは,レニウム・ボルンガム炭化物とモリブデン・ボルンガムボルンガム炭化物を合成し,その硬さは40GPaを超える.
科学分野:
- 材料科学
- コンピュータ材料科学
- 固体化学
背景:
- 特殊な機械的特性,特に高い硬さを持つ材料の開発は大きな課題です.
- 予測モデリングは 新しく機能する無機材料の発見を加速させることができます
研究 の 目的:
- 材料の硬さのプロキシとして弾性モジュールを予測する機械学習モデルを開発する.
- 超非圧縮性および超硬質の新種の無機化合物を特定し合成する.
主な方法:
- 水晶構造データベースから118,287の化合物をスクリーニングするために,サポートベクトルマシン回帰モデルが使用されました.
- テルナリウム・ボルンガム・カーバイドとクォーターナリウム・ボルンガム・ボルンガム・カーバイドは,環境圧で合成された.
- 高圧ダイヤモンド・アンビル・セル測定とビッカース微硬性試験が行われた.
主要な成果:
- 機械学習は10%未満の誤差で 合成化合物の質量モジュールを正確に予測しました
- 両合成化合物は超不圧縮性であり,非常に高い硬さ (> 40 GPa) を示した.
- 特定された材料は,低インデント負荷で超硬度値を超えました.
結論:
- 機械学習は 先進的な機能的な無機材料の発見を加速させるための 効果的な戦略です
- 開発されたモデルは,例外的な機械的性質を持つ新しい超硬質化合物を成功裏に特定しました.
- この研究は,対象となる材料の設計と合成のための強力なアプローチを示しています.
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