Machine Learning Decomposition Onset Temperature of Lubricant Additives

Zhang Yun; Xu Xiaojie

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Machine Learning Decomposition Onset Temperature of Lubricant Additives

机译：润滑剂添加剂的机器学习分解起始温度

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The thermal stability of lubricant additives is a fundamental parameter in practical applications, which is determined by the molecular structure. The ability to predict thermal properties, particularly lubricant additives' decomposition onset temperature, is of ultimate importance. We develop the Gaussian process regression model to present the relationship between molecular descriptors and onset temperature of decomposition of lubricant additives. This model is highly stable and accurate, which is promising as a fast, robust, and low-cost tool for estimating various types of lubricant additives' decomposition onset temperature.

机译：润滑油添加剂的热稳定性是实际应用中的一个基本参数，它由分子结构决定。预测热性能的能力，尤其是润滑剂添加剂的分解起始温度，至关重要。我们开发了高斯过程回归模型来描述分子描述符与润滑添加剂分解起始温度之间的关系。该模型具有较高的稳定性和准确性，是一种快速、可靠、低成本的润滑油添加剂分解起始温度估算工具。

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来源
《Journal of Materials Engineering and Performance》 |2020年第10期|共12页
作者
Zhang Yun; Xu Xiaojie;
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作者单位

North Carolina State Univ Raleigh NC 27695 USA;

North Carolina State Univ Raleigh NC 27695 USA;

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原文格式 PDF
正文语种 eng
中图分类金属学与金属工艺;工程材料学;
关键词
decomposition; Gaussian process; lubricant additive; machine learning; temperature;

机译：分解高斯过程;润滑剂添加剂;机器学习;温度;

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机译：高温液体润滑剂添加剂。总结报告

Machine Learning Decomposition Onset Temperature of Lubricant Additives

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