DASH:为部分电荷分配提供基于注意力的动态子结构层次结构
Marc T Lehner1, Paul Katzberger1, Niels Maeder1
1Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland.
Journal of chemical information and modeling
|September 22, 2023
概括
我们开发了一种快速而准确的方法来使用图形神经网络 (GNN) 和基于注意力的新型动态子结构层次结构 (DASH) 来分配原子部分电荷. 这种方法可以无地集成到现有的分子建模工作流程中.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在化学中的应用
- 分子建模分子建模
背景情况:
- 准确的部分电荷对于分子模拟至关重要.
- 现有的方法可能在计算上昂贵或缺乏通用性.
- 图形神经网络 (GNN) 在预测分子性质方面表现有前途.
研究的目的:
- 开发一种计算效率高,准确的方法来分配原子的部分电荷.
- 创建一个软件独立的方法,与现有的参数化管道集成.
- 为了利用GNN来改进分子电荷分配.
主要方法:
- 一个图形神经网络 (GNN) 被训练使用量子力学 (QM) 计算来预测原子部分电荷.
- 从GNN的注意值构建了一个基于注意力的动态子结构层次结构 (DASH).
- DASH方法被整合到开放力场 (OpenFF) 参数化管道中.
主要成果:
- DASH方法实现了与母GNN模型相同的准确性.
- 该方法在部分充电分配中提供了显著的加快速度.
- DASH是独立于软件的,可以轻松集成到现有的工作流程中.
结论:
- DASH方法为原子部分电荷分配提供了强大,高效和准确的解决方案.
- 实现,树和培训数据的开源可用性促进了采用.
- 这项工作推进了分子参数化和计算化学领域.
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