DeePMD-kit v3:用于机器学习潜力的多个后台框架
Jinzhe Zeng1,2,3, Duo Zhang4,5,6, Anyang Peng4
1School of Artificial Intelligence and Data Science, Unversity of Science and Technology of China, Hefei 230026, P. R. China.
Journal of chemical theory and computation
|May 2, 2025
概括
现在DeePMD-kit版本3支持多个机器学习框架,包括TensorFlow,PyTorch,JAX和PaddlePaddle. 这种多后端方法提高了分子动力学模拟和机器学习潜力的互操作性.
科学领域:
- 计算物理 计算物理
- 材料科学 材料科学 材料科学
- 化学 化学 化学
背景情况:
- 机器学习潜力 (MLP) 对分子动力学 (MD) 模拟至关重要.
- 现有的软件包通常依赖于单个机器学习框架 (例如,TensorFlow),限制了互操作性.
- 之前的DeePMD-kit版本由于框架的特殊性而面临整合挑战.
研究的目的:
- 引入DeePMD-kit版本3与多个后台框架.
- 展示MLP的增强多功能性和互操作性.
- 促进与各种机器学习框架和可差异化的力场的集成.
主要方法:
- 为DeePMD-kit.kit开发了一个多后台架构.
- 集成支持TensorFlow,PyTorch,JAX和PaddlePaddle. 这是一个非常好的解决方案.
- 展示了无的后端切换和集成功能.
主要成果:
- DeePMD-kit版本3支持多个ML框架,克服了以前的限制.
- 多后台架构使其与其他MLP软件包轻松集成.
- 证明了可微分分子力场的成功整合.
结论:
- 多后端框架显著提高了DeePMD-kit.kit的灵活性和互操作性.
- 这种进步有助于开发复杂的跨框架科学工作流程.
- 扩大了MLP在物理,化学和材料科学研究中的适用性.
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