FeNNol:一个高效灵活的图书馆,用于构建增强力场的神经网络潜力的神经网络潜力
Thomas Plé1, Olivier Adjoua1, Louis Lagardère1
1Sorbonne Université, LCT, UMR 7616 CNRS, 75005 Paris, France.
The Journal of chemical physics
|July 25, 2024
概括
FeNNol 是一个用于创建混合神经网络潜能 (NNP) 的新库. 它通过将机器学习与基于物理的力场相结合来加快分子模拟的速度,接近传统的力场性能.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 神经网络原子间潜力 (NNP) 提供了精确的分子建模,但在计算上可能很昂贵.
- 机器学习和混合模型的进步扩大了NNP的可能性.
- 将机器学习与基于物理的力场相结合,为高效的模拟提供了一个有希望的途径.
研究的目的:
- 介绍FeNNol,一个用于开发和部署增强力场神经网络潜力的新型库.
- 为构建混合ML-力场模型提供灵活和模块化框架.
- 提高NNP的性能和可访问性,用于分子模拟.
主要方法:
- 开发了FeNNol,这是一个用于构建,训练和运行混合NNP的Python库.
- 集成的最先进的嵌入式与ML参数化的物理相互作用.
- 使用Jax进行自动区分和即时编译以加速NNP评估.
主要成果:
- 在没有复杂的编程的情况下,FeNNol可以灵活构建混合ML力场模型.
- 在GPU上的ANI-2x模型中,通过传统的力场 (例如,AMOEBA) 实现了接近平价的模拟速度.
- 通过 Jax 集成,在 NNP 评估中显著提高了业绩.
结论:
- FeNNol促进了先进混合NNP架构的创建和应用.
- 该图书馆弥合了ML潜力和标准力场之间的性能差距.
- 预计FeNNol将加速各种分子模拟问题的研究.
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