使用图形神经网络加速分子动力学:通过E(q) C-GNNN进行可扩展的方法
Debasis Maji1, Atish Ghosh2, Debaditya Barman1
1Department of Computer & System Sciences, Visva-Bharati, Santiniketan 731235, India.
The journal of physical chemistry letters
|February 22, 2025
概括
图形神经网络 (GNN) 加快了计算密集的初始分子动力学 (AIMD) 模拟. 这种方法显著提高了用于预测二维材料属性的计算速度.
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:
- 初始分子动力学 (AIMD) 模拟对于研究热稳定性和非adiabatic动力学至关重要.
- 在计算上,AIMD模拟是昂贵的,这限制了它们在复杂系统中的应用.
研究的目的:
- 使用图形神经网络 (GNN) 提高AIMD模拟的计算效率.
- 开发一个等价GNN模型来预测二维 (2D) 材料的特性.
主要方法:
- 在AIMD模拟的原子坐标上训练了一个等价GNN模型.
- 应用GNN模型来预测2D g-CN,WTe2和g-CN/WTe2系统的潜在能量,动能,和原子间力.
主要成果:
- 该GNN模型准确地预测了关键参数,波动水平为±3%.
- 在计算速度上取得了显著的改进,数量级的数量级.
- 演示了模型在2D系统中处理不同原子连接的能力.
结论:
- 相当的GNN为广泛的低维材料AIMD模拟提供了计算效率高的替代方案.
- 这种方法适用于均或对称周期性的2D材料.
- 开发的GNN模型加速了对材料属性和动态的研究.
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