使用描述器进行粗粒度和预测原子材料模拟
1Aix-Marseille Université, CNRS, CINaM UMR 7325, Campus de Luminy, 13288 Marseille, France.
Physical review letters
|December 22, 2023
概括
描述函数为分析材料模拟中的原子结构提供了一种新方法. 这种方法可以准确地预测和预测财产,提高大规模模拟的效率.
科学领域:
- 计算材料科学科学 计算材料科学
- 材料 信息学 信息学
- 原子学模拟 原子学模拟
背景情况:
- 大规模的原子模拟是资源密集型的数据生成,存储和分析.
- 预测材料的特性和行为,如产量,需要强大的分析工具.
研究的目的:
- 介绍描述函数作为原子结构的一般,度量潜伏空间.
- 为了在大型材料模拟中有效分析和预测性质.
- 开发一种方法来评估对轨迹预测的预测信心.
主要方法:
- 对原子结构的描述函数的开发和应用.
- 使用矢量自回归模型进行轨迹生成,重新采样和预测.
- 在预测信心评估中使用Mahalanobis异常距离.
主要成果:
- 描述函数可以回归各种材料特性,包括脱位密度和应力状态.
- 矢量自回归模型准确地预测材料轨迹,并允许平滑物质分布.
- 来自Mahalanobis距离的预测信心有效评估粗粒度模型.
结论:
- 描述函数为分析大规模模拟中的原子结构提供了一个强大的框架.
- 拟议的预测方法提供了可靠的预测,具有可量化的信心.
- 材料产量与描述元组的内在维度减少有关.
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