Bhupalee Kalita1, Hatice Gokcan1, Olexandr Isayev2,3

  • 1Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA, USA.

PubMed
まとめ

機械学習原子間ポテンシャルは、分子モデリングにおいて量子精度と古典的速度を組み合わせます。将来のフレームワークは、予測可能で、転移可能で、物理的に根拠のある計算化学を目指しています。

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