Quantitative Structure–Property Relationships (QSPR) and Machine Learning (ML) Models for Materials Science
摘要
Quantitative Structure–Property Relationships (QSPR) serve as a pivotal tool in the field of Materials Science, providing insights into the correlation between the structural characteristics of materials and their resulting properties. This chapter aims to comprehensively explore the theoretical foundations, methodologies, applications, and recent advancements in QSPR, with a focus on its significance in the design and development of novel materials. Through an in-depth analysis of various experimental data, this study elucidates the intricate relationships between material structure and properties, highlighting the potential for predictive modelling and rational design strategies. By integrating computational approaches, statistical analysisStatistical analysis, and experimental validationExperimental validation, this chapter contributes to the advancement of QSPR methodologies, paving the way for the accelerated discovery and optimization of materials for diverse applications.