![]() In the same year, Tsubouchi et al., used the quantitative structure property relationship (QSPR) method (having the same basic principle as the QSAR method) to study the high-temperature traction coefficient and low-temperature fluidity of traction fluids. ![]() Edgar et al., introduced a specific QSAR method for traction fluid design in 2004. There are usually three selection methods, namely filter, wrapper, and embedded methods.Īs tribologists, we must also consider whether there is a certain dependence law between the structure and tribological properties of materials. The filtering principle is to remove noise, redundancy, and irrelevant descriptors without losing information. Therefore, only a small number of descriptors are generally used in the QSAR model. The use of too many descriptors leads to training difficulties, overfitting, poor model interpretation, and other problems in the modeling process. At present, there are thousands of structural descriptors and features used in the QSAR method. Common machine learning methods include multiple linear regression (MLR), principal component analysis (PCA), partial least squares (PLS) regression analysis, support vector machine (SVM), and artificial neural network (ANN) methods. This function relation is obtained by learning existing data in a typical machine learning task. After determining a set of structural descriptors for a given activity, the task of QSAR research is to find the functional relationship between the descriptors and the activity. These indices represent more subtle structural changes. Over the years, the QSAR indices (descriptors) representing the structure of compounds have been expanded to be directly computationally available. The quantitative structure ability relationship (QSAR) model was established and has been used until the present. expressed the chemical structure in a quantified form, established the correlation between the quantified structural index and the properties of compounds through regression, and thus established a prediction model. It has long been recognized that the properties of a substance depend on the molecular structure of the substance. ![]() Despite these limitations, it is hoped that the described QSTR method will become a useful tool and serve as a reference for tribology research groups in the future. Additionally, the research topics of the quantitative tribo-ability relationship of lubricants covered in this review are mainly mentioned to introduce various modeling methods, and there may be many similar works that are not covered in this review. ![]() It is noted that the study of lubricants involves many related issues, such that there may be omissions in this review. Specifically, it highlights a series of recent studies conducted by Chinese scholars and future prospects related to these works. This review presents the new concept of the quantitative structure tribo-ability relationship (QSTR) derived from the basic principles of quantitative structure activity relationship (QSAR) theory and reviews the latest advances in research on basic problems of the QSTR of lubricants. ![]() In recent years, lubricant research has developed from empirical to theoretical, from descriptive to rational, from qualitative to quantitative, and from macroscopic to microscopic studies. ![]()
0 Comments
Leave a Reply. |