Deep learning in vibrational spectroscopy: Benefits, limitations, and recent progress

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 review comprehensively explores the transformative impact of deep learning on vibrational spectroscopy modeling, emphasizing its superiority over traditional machine learning approaches. However, the interplay between vibrational spectroscopy and deep learning still presents significant challenges, including the demand for massive labeled datasets, the risk of overfitting, particularly with limited samples, and the inherently black-box nature of deep learning models. To address these challenges, this review highlights recent breakthroughs that leverage the unique synergy between the two fields. For instance, transfer learning enables knowledge transfer across spectral domains, mitigating the need for extensive labeled data. Generative adversarial networks synthetically expand datasets by capturing the complex patterns inherent in vibrational spectra. Physics-informed neural networks integrate spectroscopic principles directly into model architectures, bridging the gap between physical insights and data-driven approaches. Additionally, enhancing model interpretability through techniques like attention mechanisms and saliency mapping is critical for trustworthy deployment, especially in high-stakes applications where domain-specific insights from vibrational spectroscopy can guide and validate model predictions. This review not only encapsulates these advancements but also distills best practices for model development, emphasizing experimental design tailored to spectral data, hyperparameter tuning for robustness, and validation protocols that ensure reliability in cheminformatics. This review also provides an overview of the latest research and applications in vibrational spectroscopy over the past 2 years and offers insights into future directions for spectroscopic modeling in the face of big data challenges.