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Updated: Jun 22, 2025

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy
14:55

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Published on: September 17, 2017

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深度学习的原子间潜力将分子结构排序与多烯二的宏观性质联系起来

Rajni Chahal1, Michael D Toomey1, Logan T Kearney1

  • 1Chemical Science Division, Oak Ridge National Laboratory (ORNL), Oak Ridge, Tennessee 37830, United States.

ACS applied materials & interfaces
|July 3, 2024
PubMed
概括

在小规模数据上训练的神经网络原子间潜力 (NNIP) 可以准确地预测大规模的聚烯二 (PAN) 聚合物结构和特性. 这一突破使PAN和类似材料的结构属性关系的成本效益和准确预测成为可能.

关键词:
在ReaxFFFF中使用.机器学习 原子间潜力机械性能 机械性能 机械性能分子动力学分子动力学聚烯基烯二烯的使用方法聚合物是一种聚合物.

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