"物理情報に基づく機械学習モデルを使用して,材料の特性予測を進める"に関するコメント
Maximilian Fleck1, Samir Darouich2,3, Marcelle B M Spera1
1Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569, Stuttgart, Germany.
Journal of cheminformatics
|August 28, 2025
まとめ
物理的な機械学習は データが不足したときに粘度予測を 改善します 蒸発熱のような 分子ダイナミクスの記述を統合することで 純粋な液体の精度が向上します
科学分野:
- 物理化学
- 材料科学
- コンピュータ化学
背景:
- データ駆動型機械学習 (ML) は,限られたデータでプロパティ予測の課題に直面しています.
- 物理に基づいたモデルと MLを統合することで,予測性能を向上させることができます.
- 噛んでる 液体の粘度を予測するために,定量的な構造-性質関係 (QSPR) の分子動力学 (MD) ディスクリプターを使用した.
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