机器学习预测了二维合体的玻璃过渡,除了中等范围的晶体秩序之外
Eun Cheol Kim1, Dong Jae Chun1, Chung Bin Park1
1Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea.
Physical review. E
|November 18, 2023
概括
机器学习模型仅使用粒子位置成功预测体粒子玻璃过渡. 这表明,在二维合体中,普遍的结构特征存在于中等范围的晶体秩序之外.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 材料科学是一种材料科学.
- 计算物理学的计算物理.
背景情况:
- 了解合体系统中的玻璃过渡对于材料科学至关重要.
- 二维 (2D) 合体提供了一个模型系统来研究相位过渡.
- 在玻璃过渡中中等范围晶体秩序 (MRCO) 的作用尚未完全理解.
研究的目的:
- 为了研究2D合体中玻璃过渡过程中结构秩序的存在.
- 为了确定机器学习模型是否可以仅使用粒子位置来预测玻璃过渡.
- 探索合体系统中的通用结构特征.
主要方法:
- 构建的机器学习 (ML) 模型仅使用环状颗粒的位置.
- 在两种类型的二维合物上训练并测试了ML模型:具有MRCO的多分散合物 (PC) 和没有MRCO的二元合物 (BC).
- 评估了ML模型对玻璃过渡的预测性能.
主要成果:
- ML模型成功预测了二维合体的玻璃过渡.
- 在没有MRCO的BC上训练的模型可以预测具有MRCO的PC的玻璃过渡.
- 这表明,超出MRCO的结构特征对于预测玻璃过渡很重要.
结论:
- 对于ML模型来说,仅颗粒的位置就足以预测合玻璃过渡.
- 普遍的结构特征,而不仅仅是MRCO,在这些二维合体系统中控制玻璃过渡.
- 机器学习提供了一种强大的工具,可以揭示软物质相变的基本原理.
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