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New Techniques in Solid-State NMR
After the discovery of nuclear magnetic resonance, [1, 2] the new spectroscopy was used for the study of 1 H nuclei in liquids, but then the signal from copper in the receiver coil itself, the first observation of NMR in the solid state, was found.“Wide-line NMR”, named thus because of the line-broadening effects of dipolar interaction and chemical shift anisotropy, was not far behind, and soon led to significant advances through the analysis of spectral lineshapes. Inthis way Richards and Smith [3] demonstrated the presence of H3O+ cations in solid hydrates of strong acids, while Andrew and Eades [4] investigated the details of molecular motion in three solid benzenes. Even now, 50 years later, it is difficult to think of a technique which would provide a more convincing demonstration of the reality of these effects.
Deep learning architecture and application
As one of the fastest-growing topics in machine learning, deep learning algorithms have achieved unprecedented success in recent years. Novel paradigms (such as contrastive learning and few-shot learning) in deep learning and rising neural network architectures (e.g., transformer and masked autoencoder) are dramatically changing the field of data-driven algorithms. More importantly, deep learning models are redefining the next generation of industrial applications spanning image recognition, speech processing, language translation, healthcare, and other sciences. For example, recent advances in deep representation learning are allowing us to learn about protein 3D structures, which sheds new light on fundamental medicine and biology along with potentially bringing in billions of dollars (e.g., in the pharmaceutical market).
