Mechanism Analysis and Property Prediction of Extended Surfactants Based on the Respectively Optimized Force Field

Abstract

Extended surfactants represent a novel class of anionic–nonionic surfactants with exceptional performance and unique application value in chemically enhanced oil recovery. Although molecular dynamics (MD) simulations can efficiently screen these surfactants, the current research is limited. Here, it is proven for the first time that existing generic force fields (GAFF and CHARMM) cannot accurately describe extended surfactants, and traditional approaches are insufficient for obtaining precise charge parameters. The concept of the respectively optimized force field (ROFF) with the purports of specialization and accuracy is proposed to construct high-accuracy models for MD simulations, and a new approach is developed to simulate the interface model. By combining the newly specialized alkane model, ROFF-based surfactant models, and the innovative simulation protocol, high accuracy and reliability can be obtained in predicting hydration free energies, minimum of area per molecule, and critical micelle concentration of extended surfactants. Key properties of the newly designed extended surfactants in conventional oil–water interfaces and oil reservoir environments are comprehensively predicted by using advanced analytical and characterization methods. Furthermore, the more rigorous mechanism underlying the special amphiphilicity of the extended surfactant is revealed, potentially offering significant improvements over previous empirical perspectives.

Predicting Natural Rubber Crystallinity by a Novel Machine Learning Algorithm Based on Molecular Dynamics Simulation Data

“…Thus, it is crucial to meticulously investigate the impacts caused by elements other than rubber, particularly phospholipids and proteins, on the behavior of NR’s SIC in order to attain a more comprehensive understanding. Impressively, existing studies have acknowledged the substantial potential of molecular dynamics (MD) simulation as a robust approach for unraveling the complexities of materials with intricate structures. − MD simulation has been widely acknowledged as an effective approach for studying the crystallization behavior of polymers, as demonstrated by numerous studies. It offers a powerful tool to investigate the molecular-level mechanisms and dynamics involved in the process of polymer crystallization. − However, it has been observed that in the fields of chemistry and materials science, the process of model training typically involves a limited subset of high-quality data points, which usually range from a few hundred to even fewer .…”