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神经网络的潜在能量表面和SO₂(H₂O) 和SO₂(H₂O) ₂复合物的二极矩表面

Liangfei Fu^1,2, Shuo Yang¹, Dong H Zhang¹

¹State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China. zhangdh@dicp.ac.cn.

Physical chemistry chemical physics : PCCP

|August 16, 2023

概括

这项研究使用神经网络为二氧化硫-水复合体提供了准确的潜在能量和二极矩表面. 这些表面允许精确计算SO2的分子性质H2O) n (n=1,2).

科学领域:

计算化学计算化学
理论化学理论化学
分子建模分子建模

背景情况:

准确的潜在能量表面 (PES) 和二极 Moment 表面 (DMS) 对于理解分子相互作用和动态至关重要.
之前的研究往往依赖于不那么全面或计算上昂贵的方法来构建这些表面.

研究的目的:

为了开发一个全维的,基于ab initio的多体PES和DMS,用于SO2O) n (n=1,2) 复合体.
为了利用神经网络方法进行高效和准确的表面构造.
通过将计算的波频率和离散能量与初始结果进行比较来验证开发的表面.

主要方法:

神经网络方法用于构建多体潜在能量表面 (PES) 和二极 Moment 表面 (DMS).
在 Ab initio 计算中,使用单,双和扰动三重 (CCSD ((T)) 和双混合密度函数 (DSD-PBEP86) 方法的合集群计算.
创建了广泛的1体,2体和3体能量和双极时刻数据库.

主要成果:

创建了关于ab initio能量 (总数超过175,000) 和二极点的全面数据库.
基于神经网络的PES和DMS已经成功地构建了SO₂(H₂O) 和SO₂(H₂O) ₂.
计算的波频率和离散能量与直接的初始计算有很好的一致性,证实了构造的表面的忠实性.

结论:

开发的基于神经网络的PES和DMS为未来对SO₂-水复合体的理论研究提供了可靠和高效的工具.
这些表面允许准确的量子动态模拟和属性预测.
该方法证明了机器学习在加速生成高质量的分子潜在能量和二极矩表面方面的力量.

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