由图形处理单元驱动的高性能半经验激发状态分子动力学
Vishikh Athavale1, Maksim Kulichenko1, Sebastian Fernandez-Alberti2
1Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
The journal of physical chemistry letters
|February 19, 2026
概括
这项研究介绍了PYSEQM,这是一个用于激发状态分子动力学 (ESMD) 模拟的GPU加速引擎. 它使用机器学习集成实现了分子系统和光谱计算的高效,长时间模拟.
科学领域:
- 量子化学 是一个量子化学.
- 计算化学计算化学
- 频谱学是一种光谱学.
背景情况:
- 激发状态分子动力学 (ESMD) 对于理解光物理过程至关重要.
- 模拟长轨迹和激发状态的大集合是计算要求很高的.
- 现有的方法经常在复杂系统的效率和可扩展性方面扎.
研究的目的:
- 介绍PYSEQM,这是一个GPU加速引擎,用于高效的激发状态分子动力学.
- 实施和验证一个扩展的拉格朗日激发状态的波恩-奥本海默分子动力学 (XL-ESMD) 方案.
- 展示平台计算光谱属性的能力及其机器学习集成的潜力.
主要方法:
- 在PYSEQM引擎中使用PyTorch.ch开发了一个激发状态的Born-Oppenheimer分子动力学 (BOMD) 模块.
- 实施了一个扩展的拉格朗激发状态BOMD (XL-ESMD) 方案,以提高效率和趋同.
- 利用GPU加速和批量执行用于高吞吐量模拟.
- 在地面和激发状态上传播轨迹,以计算吸收,发射和红外光谱.
主要成果:
- 在单个GPU上实现了长轨迹和大型统计合集的高效模拟.
- 从小分子到900个原子的树枝状分子的平滑缩放.
- XL-ESMD方案以显著降低的计算成本提供了准确的光谱.
- PYSEQM的PyTorch基础可以实现自动区分,GPU分批和ML模型集成.
结论:
- PYSEQM为激发状态分子动力学模拟提供了一个实用和高效的平台.
- 对于成本效益高的激发状态BOMD,XL-ESMD方案是有效的.
- 该平台促进机器学习增强动态和未来数据驱动的非adiabatic建模.
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