Progress and Applications of Deep Learning in Vibrational Spectroscopy

Vibrational spectroscopy is a cornerstone in molecular analysis, offering detailed insights into chemical compositions and dynamics. Recent years have witnessed a paradigm shift with the integration of deep learning, which excels in automatically extracting intricate patterns from raw spectral data, bypassing traditional preprocessing steps. This synergy has significantly enhanced the precision and speed of applications ranging from material science to biomedical diagnostics. This book comprehensively explores the transformative impact of deep learning on vibrational spectroscopy modeling, emphasizing its superiority over traditional machine learning approaches. This book also provides an overview of the latest research and applications in vibrational spectroscopy over the past three years and offers insights into future directions for spectroscopic modeling in the face of big data challenges.

Autorentext

Congli Mei is a full professor in Zhejiang University of Water Resources and Electric Power and holds a PhD in Control Science and Engineering from Zhejiang University. His area of research includes the study of soft sensor technology. He also lectures and supervises master degree students; and has to his credit several publications and awards.