对于数据驱动的原子间潜力的超活性学习
Cas van der Oord1, Matthias Sachs2, Dávid Péter Kovács1
1University of Cambridge, Cambridge, CB2 1PZ UK.
概括
超活性学习 (HAL) 加快了对原子间潜力的训练数据的创建. 这种方法可以快速生成像AlSi10和PEG聚合物这样的材料的精确潜力.
科学领域:
- 计算材料科学 计算材料科学
- 机器学习在化学中的应用
- 原子模拟的原子模拟.
背景情况:
- 原子间电位接近复杂的量子力学计算.
- 产生准确的培训数据是开发这些潜力的瓶.
- 现有的方法需要大量的计算资源来生成数据库.
研究的目的:
- 为加速培训数据库组装引入超活性学习 (HAL).
- 为了证明HAL在创建数据驱动的原子间潜力的效率.
- 为了验证HAL生成的材料性能潜力的准确性.
主要方法:
- 开发了一个HAL框架,将偏差术语与物理样本 (例如分子动力学) 集成在一起.
- 应用HAL生成AlSi10合金和聚乙烯糖醇 (PEG) 聚合物的训练配置.
- 利用生成的数据来训练原子集群扩张 (ACE) 的原子间潜力.
主要成果:
- HAL显著减少了培训数据库组装所需的时间和数据.
- 由HAL生成的ACE潜力准确地预测了AlSi10和PEG的宏观性质 (化温度,密度).
- 潜能从最小的初始配置开始,达到接近实验值的准确性.
结论:
- HAL是快速,自动化培训数据库生成的有效策略.
- 这种方法可以有效地开发精确的数据驱动的原子间潜力.
- 在计算材料科学和聚合物建模中,HAL具有广泛的应用.
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