Construction of macromolecular model of coal based on deep learning algorithm

The construction of macromolecular models for the amorphous structure of coal can help reveal its physicochemical properties from a microscopic perspective and provide insight into its reaction mechanisms, leading to the development of cleaner coal technologies. However, this process requires careful consideration of characterization information. Researchers often need to intervene manually, which makes the task time-consuming. In this study, we proposed a multi-modal deep learning technique, namely ClipIRMol (contrastive language-image pre-training for infrared-molecule), for predicting coal molecular fragments based on the reverse molecular design method. On this basis, a structure evolution algorithm was developed to transform these fragments into a complex molecular structure model. Our approach takes elemental analysis, IR spectrum, and ¹³C NMR data as inputs. It is capable of constructing highly accurate molecular models of any different types of coal with atom count ranging from tens to thousands in just a few minutes. These spectra were simulated by quantum chemical calculations to show alignment with their experimental data. The introduced 3D molecular models grounded in topological structures overcome the limitation of traditional nearly-planar structures. This offers a new direction for macromolecular modeling of amorphous organic macromolecules.