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机器学习非adiabatic动力学:消除非adiabatic合的阶段自由与状态-交互状态-平均旋转-限制合集-引用Kohn-Sham方法
Sung Wook Moon1, Soohaeng Yoo Willow2, Tae Hyeon Park2,3
1Department of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, Republic of Korea.
我们引入了一种新方法,无相合项 Δ2,以提高激发状态分子动力学模拟的机器学习潜力. 这种方法提高了圆交叉点附近的精度和稳定性,使得高效的非adiabatic动态建模.
科学领域:
- 计算化学计算化学
- 量子力学就是量子力学.
- 机器学习在化学中的应用
背景情况:
- 激发状态分子动力学 (ESMD) 模拟对于理解光化学过程至关重要.
- 机器学习潜力 (MLP) 提供了电子核动态的高效建模,但在形交叉点 (CI) 附近面临挑战.
- 来自CI奇点和双重值合元件的不连续性阻碍了非adiabatic动态中的MLP的准确性.
研究的目的:
- 开发一种可靠的方法来提高ESMD模拟中的MLP的稳定性和准确性.
- 解决非adiabatic动态中形交叉点和双值合元件所带来的挑战.
- 为了实现更可靠和高效的大规模和长期ESMD模拟.
主要方法:
- 在SI-SA-REKS形式主义中引入了无相合术语, Δ2,它来自于糖尿病的哈密尔顿式.
- 应用 Δ2 术语来改进非adiabatic 动态的 MLP 培训.
- 使用penta-2,4-dieniminium离子 (PSB3) 的兴奋状态分子动力学模拟进行验证.
主要成果:
- 基于Δ2的方法显著提高了MLPs的稳定性和准确性,通过减轻形交叉点的问题.
- 改进的MLP准确地复制了初始激发状态分子动力学模拟.
- 该方法在训练MLP进行复杂的非adiabatic动态方面表现出有效性.
结论:
- 无相合术语 Δ2 为在形交叉点附近的 MLP 中的不连续性提供了明确的解决方案.
- 开发的ML-ESMD方法对于建模非adiabatic动态是高效和准确的.
- 这一进步为大规模和长时间规模激发状态分子动力学模拟中的更广泛应用提供了显著的潜力.
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