通过自然语言处理和深度学习来改进耐腐蚀合金设计
Kasturi Narasimha Sasidhar1, Nima Hamidi Siboni1,2, Jaber Rezaei Mianroodi1,2
1Max-Planck-Institut für Eisenforschung GmbH, Max-Planck Straße-1, 40237 Düsseldorf, Germany.
Science advances
|August 11, 2023
概括
我们开发了先进的机器学习方法,使用自然语言处理和深度学习来预测合金孔潜力. 这种方法通过整合文本数据和使用元素属性而不是构成,显著提高了准确性.
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
- 化学工程是化学工程的重要组成部分.
背景情况:
- 用于材料设计的机器学习模型往往受到它们无法处理来自实验描述的文本数据的限制.
- 手动从文本中提取数据可以降低信息密度,限制模型的准确性.
研究的目的:
- 为了增强机器学习能力,耐腐蚀合金设计.
- 开发用于将文本数据纳入深度学习模型的自动化方法.
- 确定影响合金孔潜力的关键元素描述符.
主要方法:
- 实现了自然语言处理 (NLP) 方法,将文本数据自动转换为适合深度神经网络的格式.
- 开发了一个深度学习模型,利用转换的输入特征空间.
- 取代传统的合金成分输入以基于元素物理和化学性质的数值描述器.
主要成果:
- 在测量潜在预测准确度方面取得了显著的改进,超过了最先进的方法.
- 成功识别了影响位潜力的关键元素描述器,包括配置,原子包装效率,局部电子负性差异和原子半径差异.
结论:
- 自动化NLP与深度学习的整合为增强材料设计提供了一个强大的策略.
- 元素的物理/化学特性是合金孔潜力的关键预测因素,为设计优质耐腐蚀合金提供了新的见解.
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