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机器学习电子密度预测使用原子位置的加权平滑重叠
Siddarth K Achar1,2, Leonardo Bernasconi3, J Karl Johnson2
1Computational Modeling & Simulation Program, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Nanomaterials (Basel, Switzerland)
|June 27, 2023
概括
我们开发了DeepCDP,这是一种深度学习方法,用于预测化学系统的电子密度. 这种方法为材料化学应用提供了准确,计算高效的预测,克服了传统量子力学方法的局限性.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 机器学习 机器学习
背景情况:
- 精确的电子密度对于理解化学反应,离子运输和材料中的电荷转移至关重要.
- 传统的量子力学 (QM) 方法,如密度函数理论 (DFT),在大型系统和长时间尺度上难以实现可扩展性.
- 质量管理方法的局限性阻碍了对复杂的动态化学系统的研究.
研究的目的:
- 开发一种新的深度神经网络机器学习形式主义,DeepCDP,用于预测电荷密度.
- 克服动态化学系统的传统QM方法的计算局限性.
- 为了使电子密度的准确和有效的预测只使用原子位置.
主要方法:
- 开发了深度电荷密度预测 (DeepCDP),一种深度神经网络形式主义.
- 在网格点基础上利用原子位置与指纹环境的加权光滑重叠.
- 将原子指纹映射到电子密度数据中,这些数据来自QM模拟,用于训练.
- 在各种系统上训练模型,包括散装材料 (Cu,LiF,Si),分子系统 (水) 和2D材料 (石墨烯).
主要成果:
- 对于大多数训练有素的系统,DeepCDP实现了高预测准确度,R2值>0.99和MSE~10-5e2 Å-6.
- 证明了具有系统大小和高并行性的线性缩放,显著降低了计算成本.
- 准确地预测了质子系统中的过量电荷,并追踪了质子位置.
- 展示了模型可转移性,以预测训练有素原子物种的未见系统的电子密度.
结论:
- DeepCDP为电子密度预测提供了传统QM方法的计算效率高,准确的替代方案.
- 该方法可以研究大规模的电荷传输和材料中的化学反应.
- DeepCDP 的可转移性扩大了其在各种化学系统中的适用性.
- 这种方法促进了材料化学中复杂的动态过程的研究.
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