Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …

In recent years, extensive research in the field of Deep Learning (DL) has led to the
development of a wide array of machine learning algorithms dedicated to solving complex …

Abstract Structure determination of amorphous materials remains challenging, owing to the
disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder …

The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and
unambiguous identification and characterization of peaks is a difficult, but critically important …

The execution and analysis of complex experiments are challenged by the vast
dimensionality of the underlying parameter spaces. Although an increase in data-acquisition …

The reliability of organic molecular crystal structure prediction has improved tremendously in
recent years. Crystal structure predictions for small, mostly rigid molecules are quickly …

Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major
impact on academic research and industry. Nowadays, DL provides an unprecedented way …

A primary goal of metabolomics studies is to fully characterize the small-molecule
composition of complex biological and environmental samples. However, despite advances …

The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and
characterize their parameters, often referred to as deconvolution, is a crucial step in the …

A new tool, ML-J-DP4, provides an efficient and accurate method for determining the most
likely structure of complex molecules within minutes using standard computational …