精确的神经网络微调方法可用于跨不同分子和晶体尺度的可转移的Ab Initio能量预测
Wai-Pan Ng1, Zili Zhang1, Jun Yang1,2
1Department of Chemistry, The University of Hong Kong, Hong Kong 999077, P. R. China.
Journal of chemical theory and computation
|February 4, 2025
概括
这项研究介绍了一种可转移的神经网络模型,用于预测分子能量. 该模型通过学习双对能量校正来准确预测大型系统,将小分子和大分子电子结构连接起来.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 机器学习是机器学习.
背景情况:
- 准确预测大分子和晶体的能量是计算上昂贵的.
- 现有的机器学习模型随着系统大小的增加而难以准确.
研究的目的:
- 开发一种可转移的神经网络模型,用于在大分子系统中准确的能量预测.
- 为了弥合小型和大型系统之间的电子结构模式的差距.
主要方法:
- 轨道对对分解的相关性能量.
- 预训练的神经网络与残余连接用于双向能量校正.
- 低级别的再培训技术.
主要成果:
- 该模型准确地预测了MP2相关性能量对于像 (H2O) 64这样的大型系统的化学准确性.
- 对于质子集群和多糖氨酸链来说,已证明有效的可转移性.
- 精确预测冰XV亚层的能量排序.
结论:
- 开发的神经网络模型为量子化学提供了具有成本效益和高度准确的方法.
- 该模型成功地将小型和大型系统之间的电子结构模式连接起来.
- 这项工作推动了计算机化学中可转移的机器学习模型的开发.
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