对化学反应路径的隐性神经表示
Kalyan Ramakrishnan1, Lars L Schaaf2,3, Chen Lin1
1University of Oxford, Oxford, United Kingdom.
The Journal of chemical physics
|July 16, 2025
概括
神经网络现在可以连续表示最小能量路径,为像Nudged Elastic Band (NEB) 这样的离散方法提供灵活的替代方案. 这种方法可以更快地估计过渡状态,并有效地处理复杂的反应机制.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 像Nudged Elastic Band (NEB) 这样的传统方法可以离散地近似最小能量路径.
- 这些方法可以与复杂的系统,糟糕的初始猜测或多个竞争反应途径作斗争.
研究的目的:
- 开发使用神经网络的最小能量路径的连续表示.
- 为离散路径搜索算法提供灵活和高效的替代方案.
- 为了证明该方法对具有挑战性的原子系统的适用性.
主要方法:
- 用神经网络对反应路径进行参数化.
- 使用丢失函数训练网络,该函数丢弃了接触式能量梯度.
- 验证2D潜力和复杂的原子系统的方法.
主要成果:
- 神经网络方法为最小能量路径提供了连续的功能.
- 它成功地估计了过渡状态,并在具有挑战性的系统上表现优于NEB.
- 调整采样策略有助于逃避局部最小值.
- 该网络展示了对未见的系统的概括性.
结论:
- 神经网络提供了一种多功能且连续的方法来表示最小能量路径.
- 这种方法增强了对化学反应和材料转换的研究.
- 使用机器学习进行通用反应路径表示的潜力是有希望的.
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