基于范围校正的深潜力模型的机器学习方法,用于有效的振动频率计算
Jitai Yang1, Yang Cong1, You Li1
1Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, P. R. China.
我们开发了一种机器学习方法,使用范围校正深潜力 (DPRc) 模型来加快振动频谱模拟. 这种方法显著减少了计算时间,同时保持了分子系统的准确性.
科学领域:
- 计算化学是一种计算化学.
- 在光谱学中的机器学习应用.
背景情况:
- 使用高精度方法精确的振动频谱模拟在计算上是昂贵的.
- 现有的方法在平衡精度和计算效率方面面临挑战.
研究的目的:
- 引入一种机器学习方法,即调整范围的深潜力 (DPRc) 模型,用于加速振动频谱模拟.
- 为了提高计算振动频率的计算效率.
主要方法:
- 实施基于DPRC模型的机器学习方法,将系统分为"探针"和"溶剂"区域.
- 在酸CO和MeCN CN上训练并测试了模型,在水中拉伸振动频率变化.
- 研究了区域划分,单体校正,切割范围和训练数据大小的影响.
主要成果:
- 使用单分子"探测区域"的DPRC模型实现了稳定的准确性.
- 该方法表明,与常规深潜相比,速度大约增加了10倍.
- 培训时间缩短了大约四倍.
结论:
- DPRc模型为振动频谱模拟提供了一种高效准确的方法.
- 该方法易于应用,可扩展到各种光谱计算.
- 这种机器学习策略显著提高了光谱学中的计算效率.
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