用数据驱动的合集群探索双电子激发空间
P D Varuna S Pathirage1, Justin T Phillips1, Konstantinos D Vogiatzis1
1Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37996-1600, United States.
The journal of physical chemistry. A
|February 29, 2024
概括
数据驱动的合集群 (DDCC) 使用机器学习来预测分子振幅. 有效的采样技术显著减少了训练数据,提高了复杂分子系统的准确性和适用性.
科学领域:
- 计算化学是一种计算化学.
- 量子化学是一种量子化学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 结合集群 (CC) 方法对于大分子来说是准确的,但在计算上昂贵.
- 哈特里-福克后的方法面临着可扩展性的挑战.
- 数据驱动的合集群 (DDCC) 旨在通过机器学习来减轻这些成本.
研究的目的:
- 调查数据驱动合集群 (DDCC) 模型的有效幅度采样技术.
- 为了减少训练数据大小与系统大小的指数增长.
- 为了提高DDCC的可移植性和准确性,用于更大的分子系统.
主要方法:
- 为DDCC培训开发和评估了五种振幅选择技术.
- 应用机器学习,从MP2数据中预测合集群的两电子振幅 (t2).
- 整合了局部轨道形式主义,用于预测CCSD t2幅度.
主要成果:
- 确定了有效的抽样策略,减少培训数据集大小.
- 已证明可以防止模型过度装配,并改善DDCC模型的便携性.
- 在将采样与局部轨道结合时,在能源计算中实现了10倍的误差减少.
结论:
- 有效的振幅采样对于可扩展的DDCC至关重要.
- 开发的方法提高了数据驱动合集群的效率和适用性.
- 这种方法显著提高了量子化学能量计算的准确性.
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