使用机器学习预测酸盐和酸盐玻璃的对相关函数.
Kumar Ayush1, Pooja Sahu2, Sk Musharaf Ali2
1Department of Chemical Engineering and Center for Atomistic Modeling and Materials Design, Indian Institute of Technology Madras, Chennai, TN 600036, India. tpatra@iitm.ac.in.
Physical chemistry chemical physics : PCCP
|December 15, 2023
概括
一个新的机器学习模型从玻璃的成分准确地预测了玻璃的原子结构. 这种方法加速了新型玻璃材料的发现和设计,这些材料具有量身定制的特性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 玻璃科学 玻璃科学
背景情况:
- 眼镜表现出基于组成的可调节的热物理性质.
- 在玻璃杯中建立普遍的组成-属性关系是很困难的,因为组成空间很大.
研究的目的:
- 开发一种机器学习 (ML) 的元模型,用于预测眼镜中的组成-原子结构关系.
- 创建一个自动化管道来预测玻璃原子的空间分布.
主要方法:
- 集成的无监督深度学习 (卷积神经网络自编码器) 与回归算法 (随机森林).
- 利用分子动力学模拟来生成酸盐和玻璃的原子结构.
- 开发了一个潜在空间表示,用于预测对相关函数.
主要成果:
- ML模型准确地预测了各种玻璃组合的原子对相关函数.
- 自动化管道成功模拟了玻璃中的原子的空间分布.
- 在未知玻璃成分上验证了模型的准确性.
结论:
- 开发的ML框架为了解玻璃原子结构提供了一种通用和准确的方法.
- 这种方法可以显著加速新眼镜的设计和发现.
- 该方法提供了对玻璃材料的组成-结构-性质关系的基本见解.
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