从量子动力学学习表面跳跃的脱凝时间公式
Cancan Shao1, Zhecun Shi1, Jiabo Xu1
1Key Laboratory of Excited-State Materials of Zhejiang Province, Department of Chemistry, Zhejiang University, Hangzhou 310058, China.
The journal of physical chemistry letters
|August 22, 2023
概括
我们开发了一种机器学习方法,为表面跳跃模拟创建更好的脱凝时间公式. 这提高了复杂量子动力学建模的准确性.
科学领域:
- 量子化学 是一个量子化学.
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 表面跳跃模拟被广泛使用,但存在过度连贯性问题,限制了它们的可靠性.
- 准确的量子动力学模拟对于理解化学反应和材料特性至关重要.
研究的目的:
- 开发一种通用的机器学习辅助方法,以确定表面跳跃模拟的最佳脱凝时间公式.
- 通过解决过度连贯性问题来提高表面跳跃的准确性和效率.
主要方法:
- 利用精确的量子动力学作为训练机器学习模型的参考.
- 使用核动能和附带动能差异生成描述符空间,以避免昂贵的力计算.
- 采用多层选和离散优化来得出新的基于能量的脱凝时间公式.
主要成果:
- 开发了新的基于能量的脱凝时间公式,可以显著提高表面跳跃的准确性.
- 在数千个不同的多层系统和六个标准散射模型中验证了新的公式.
- 在高计算效率下实现了近乎精确的量子动力学复制.
结论:
- 拟议的机器学习方法提供了一种系统的方式来改进表面跳跃模拟.
- 新的脱凝时间公式为复杂量子动态的准确建模提供了一个有希望的途径.
- 这项工作有助于在各种科学领域进行更可靠的模拟.
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