激发DeepPMD:学习激发状态的能量,力量和非adiabatic合
Lucien Dupuy1, Neepa T Maitra1
1Department of Physics, Rutgers University, Newark, New Jersey 07102, USA.
The Journal of chemical physics
|October 1, 2024
概括
这项研究通过学习非adiabatic合向量 (NACVs) 来增强DeePMD神经网络,用于准确的非adiabatic动态模拟. 这种新方法改善了对激发状态特性的预测,这种特性在甲minium离子上得到了证明.
科学领域:
- 计算化学的计算化学
- 量子力学就是量子力学.
- 材料科学 材料科学 材料科学
背景情况:
- 非adiabatic动力学模拟对于理解化学反应和激发状态过程至关重要.
- 现有的机器学习方法通常近似非adiabatic合向量 (NACVs),限制准确性.
- DeePMD架构对于基本状态属性是有效的,但对于激发状态需要扩展.
研究的目的:
- 扩展DeePMD神经网络架构,以准确预测非adiabatic动态中的电子结构属性.
- 开发一种可靠的方法,用于从局部化学环境中学习非adiabatic合向量 (NACV).
- 克服NACVs现有的机器学习方法中的近似性.
主要方法:
- 扩展DeePMD神经网络,以预测兴奋状态的能量和力量.
- 实施理查森的方法来学习能量差异缩放的NACV的对称二.
- 在DeePMD框架内使用本地化学环境描述符.
主要成果:
- 成功学习了NACV和当地的化学环境描述器之间的地图.
- 证明了扩展的DeePMD架构的效率和准确性.
- 通过使用甲酸 (CH2NH2+) 作为测试案例验证了该方法.
结论:
- 开发的神经网络架构准确地预测了非adiabatic动态的电子结构属性.
- 该方法克服了NACVs之前近似的局限性.
- 这一进步使激发状态化学过程的模拟更加可靠.
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