一组的电势张力电位,复杂度不断增加
Yu Luo1, Jason A Meziere2, German D Samolyuk3
1Department of Mechanical and Materials Engineering, Queen's University, Kingston, Ontario K7L 2N8, Canada.
Journal of chemical theory and computation
|September 12, 2023
概括
本研究介绍了用于原子模拟的混合机器学习力场 (MLFF) 方法. 该方法通过扩大量子力学数据库和增加模型复杂度,系统地提高了准确性,提高了对Zr.等材料的预测.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 物理 物理学 物理
背景情况:
- 机器学习力场 (MLFFs) 为原子模拟提供了高保真性和持续改进.
- 开发精确的MLFF需要大量的量子力学 (QM) 数据和仔细的模型构建.
研究的目的:
- 提出一种混合小细胞方法,将线下和主动学习结合起来,以系统地开发MLFF.
- 构建和评估越来越复杂的MLFF,使用时刻张量潜在形式主义.
- 评估MLFFs对Zr.各种材料属性的预测准确度.
主要方法:
- 实施了混合小细胞方法,整合了线下和主动学习策略.
- 系统地扩展了质量管理数据库,以训练MLFF,逐渐增加模型复杂度.
- 在MLFF构造中采用时刻张量潜在形式主义.
- 量化评估Zr.的结构性,弹性,热性和缺陷性质.
主要成果:
- 模型复杂性与MLFFs的预测准确性有正相关.
- MLFFs准确地预测了训练数据中不存在的隐形配置的属性.
- 高复杂度的MLFF (1513个参数) 与DFT基准有很好的一致性,尽管细微的特征有时会被噪音所掩盖.
结论:
- 混合MLFF方法使得系统的数据库扩展和模型复杂性的增加.
- MLFF显示出对各种材料属性的强有力的预测能力,即使是对样本外数据.
- 虽然高度复杂性提高了准确性,但要捕获细微的物理细节,需要仔细考虑训练数据和潜在噪声.
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