将复合方法和机器学习的电子相关模型扩展到第四时期的元素.
Ryo Fujisawa1, Mikito Fujinami2, Hiromi Nakai1,2
1Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, Tokyo, Japan.
Journal of computational chemistry
|July 29, 2025
概括
我们开发了一个扩展的机器学习电子相关性 (ML-EC) 模型,用于准确的量子化学计算. 这个模型有效地预测了较重元素的相关能量,超过了DFT方法并降低了计算成本.
科学领域:
- 量子化学 是一个量子化学.
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 精确计算电子相关能量是量子化学的一个重大挑战.
- 现有的方法在准确性,效率或适用于较重元素方面往往面临局限性.
研究的目的:
- 扩展机器学习的电子相关性 (ML-EC) 模型,以准确高效地估计与扰乱三倍/完整基础集 (CCSD) /CBS) 相关性能量的合集单个和双个.
- 为了使该模型能够应用于第四期元素,克服了以前对第三期元素的限制.
主要方法:
- 开发了一个扩展的ML-EC模型,使用来自Hartree-Fock (HF) 计算的描述符,使用双泽塔基数组.
- 修改了复合方法参数,以将适用性扩展到第四期元素.
- 在G3/05数据集和测试分子上训练并验证了模型.
主要成果:
- 扩展的ML-EC模型准确地复制了CCSD (T) /CBS相关能量和相关能量密度.
- 该模型在预测测试分子的相关能量方面表现出很高的准确性,优于反应能量密度函数理论 (DFT) 方法.
- 与传统的CCSD (T) /CBS计算相比,实现了超过50倍的计算速度.
结论:
- 扩展的ML-EC模型提供了一种可靠且计算效率高的方法来计算相关性能量,特别是在涉及更重元素的系统中.
- 这一进步为各种化学应用中高精度量子化学计算提供了有希望的替代方案.
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