ArcaNN:

Rolf David1, Miguel de la Puente1, Axel Gomez1

  • 1PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS 75005 Paris France rolf.david@ens.psl.eu guillaume.stirnemann@ens.psl.eu damien.laage@ens.psl.eu.

Digital discovery
|November 18, 2024
PubMed
概括

ArcaNN生成了对机器学习反应性原子间潜力 (MLIP) 的关键训练数据集. 这个框架准确地捕捉了高能化学反应几何形状,改进了分子模拟.

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