物理先前平均函数驱动的高斯过程寻找最小能量反应路径与登图像nudged弹性带:气相,界面和批量相反应的一般方法
Chong Teng1, Yang Wang1, Junwei Lucas Bao1
1Department of Chemistry, Boston College, Chestnut Hill, Massachusetts 02467, United States.
物理信息的高斯过程 (GPs) 通过使用爬图像推动弹性带 (CI-NEB) 方法优化最小能量反应路径 (MEPs) 和过渡状态 (TSs) 来加速计算化学,显著提高效率.
科学领域:
- 计算化学计算化学
- 化学物理 化学物理
- 材料科学 材料科学 材料科学
背景情况:
- 登图像推动弹性带 (CI-NEB) 方法对于识别反应途径和过渡状态至关重要.
- 标准CI-NEB计算可能是计算密集的,需要复杂反应的大量图像.
- 优化挑战来自于广泛的反应坐标跨度和昂贵的电子结构计算.
研究的目的:
- 开发一种更有效的方法来探索最小能量反应路径 (MEP) 和优化过渡状态 (TSs).
- 利用物理启发的基于函数的前平均值的高斯过程 (GPs) 来增强CI-NEB计算.
- 用身体知情的全科医生来证明以代孕为基础的优化效果.
主要方法:
- 利用高斯过程 (GPs) 与物理启发的先前平均函数用于潜在能量表面 (PES) 建模.
- 集成的全科医生与基于代理的优化器进行多维CI-NEB计算.
- 在各种反应类型 (气体,散装,接口/表面) 中进行了系统的基准研究.
主要成果:
- 与传统方法相比,身体知情的全科医生显著提高了CI-NEB优化的效率.
- 该方法在优化过程中展示了反应路径谷的即时学习.
- 从优化中获得的替代PES模型与真正的PES引用相比显示出高准确性.
- 与快速惯性放松引擎 (FIRE) 优化器相比,效率大约提高了10倍.
结论:
- 基于物理的高斯过程为加速MEP探索和计算化学中的TS优化提供了强大而有效的策略.
- 这种方法为各种化学反应系统提供了大量的计算节约和提高了可靠性.
- 将物理先验集成到全科医生中是有效后期平均值学习和增强优化性能的关键.
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