在线测试时间的调整,以便更好地将原子间潜能概括到分布之外的数据
Taoyong Cui1,2, Chenyu Tang1, Dongzhan Zhou1
1Shanghai Artificial Intelligence Laboratory, Shanghai, China.
Nature communications
|February 22, 2025
概括
本研究介绍了测试时间适应原子间潜力 (TAIP),这是一个在线框架,用于增强机器学习原子间潜力 (MLIP). TAIP通过适应新数据而提高模拟准确性和稳定性,而不需要额外的培训示例.
科学领域:
- 计算化学是一种计算化学.
- 材料科学 是一种材料科学.
- 机器学习 机器学习
背景情况:
- 机器学习原子间潜力 (MLIP) 提供高精度高效的模拟.
- 训练和测试数据之间的分配转移会降低MLIP的性能,并可能导致模拟崩.
研究的目的:
- 开发一个在线框架,测试时间适应原子间潜力 (TAIP),以改善对未见测试数据的MLIP概括.
- 为了解决MLIP由于分配转移而导致的性能恶化.
主要方法:
- 在TAIP框架内提出了一种双层自我监督的学习方法.
- 杆全球结构和原子局部环境信息用于模型适应.
- 实施在线调整以使模型与测试数据分布保持一致.
主要成果:
- 在不需要额外的数据的情况下,TAIP有效地弥合了培训和测试数据集之间的领域差距.
- 在各种基准测试中证明了增强的测试性能,包括小分子和复杂的周期系统.
- 启用了稳定的分子动力学 (MD) 模拟,而之前的基线模型失败了.
结论:
- 在分子动力学模拟中,TAIP显著提高了MLIP的稳定性和通用性.
- 拟议的在线适应方法对各种化学和材料系统有效.
- 在MLIP应用中,TAIP为分配转移问题提供了可行的解决方案.
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