![]() An automatic labeling platform is constructed that annotated over 22,000 groups of organic matter molecules and their NMR spectra. ![]() This study proposes a solution to convert non-uniform two-dimensional (2D) graph into a uniform one-dimensional (1D) matrix, which makes 2D graph data available for machine learning models. Additionally, this method simplifies the operations from repetitive trial and error. Our method has the advantages of high-throughput prediction, high accuracy, and time savings compared with the existing methods. ![]() Unfortunately, these methods are complicated, time-consuming, and labor-intensive. Fruitful researches studying kerogen at the molecular level have been conducted. The adsorption and hydrocarbon generation capacity of kerogen is directly related to its types, molecular components, and structures. Kerogen is the primary hydrocarbon source of shale oil/gas, and nearly half of the hydrocarbons in shale are adsorbed in kerogen. This study aims to develop a new method that combines machine learning with nuclear magnetic resonance (NMR) spectra to predict the kerogen components and types.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |