Molecular dynamics simulations are performed to investigate the behavior of water molecules near gold monolayer protected clusters (MPCs) with two different types of surfactant, HS(CH(2))(5)(OCH(2)CH(2))(2)COOH (type1) and HS(CH(2))(11)COOH (type2). The effects of the different moieties of the two ligands on the local structure of the water molecules are quantified by means of the reduced density profiles of oxygen and hydrogen atoms, and the hydrogen bond statistics. The adsorption characteristics of water molecules are evaluated by means of their residence time near the MPCs. The results show that the hydrophilic oligo (ethylene glycol) segment increases the number of water molecules, which penetrate the protective layer of MPC. As a result, the inter-water hydrogen bond network in the protective layer of type1 MPC is stronger than that in the protective layer of the type2 MPC. It is shown that the presence of interfacial hydrogen bonds increases the adsorption of water molecules near the MPCs and therefore constrains the motion of MPCs. As a result, the residence time of the water molecules adjacent to the type1 MPC is longer than that of the molecules adjacent to the type2 MPC.

Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recovery after the oral surgery procedures. We provided a deep learning network (DLN) with calibrated simulation data that came from different simulation conditions with experimental verification. The DLN achieves nearly exact result of simulated bone healing history around implants. The correlation of the predicted essential physical properties of surrounding bones (e.g. strain and fluid velocity) and performance indexes of implants (e.g. bone area and bone-implant contact) were greater than 0.980 and 0.947, respectively. The testing AUC values for the classification of each tissue phenotype were ranging from 0.90 to 0.99. The DLN reduced hours of simulation time to seconds. Moreover, our DLN is explainable via Deep Taylor decomposition, suggesting that the transverse fluid velocity, upper and lower parts of dental implants are the keys that influence bone healing and the distribution of tissue phenotypes the most. Many examples of commercial dental implants with designs which follow these design strategies can be found. This work demonstrates that DLN with proper network design is capable to replace complex, time-dependent, multi-physical models/theories, as well as to reveal the underlying features without prior professional knowledge.

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