AI活用によるトライボ分子シミュレーション技術の革新

抄録

In this article, a newly developed universal neural network potential (NNP), enabling accelerated molecular simulations, and its application to tribological phenomena were reported. The NNP is based on a graph neural network theory and an original dataset including a huge number of the first-principles calculation results. Two application examples using this method were introduced. One was exploring geometry for a fundamental molecular adsorption of long-chain fatty acid, which is difficult to deal with the conventional first-principles calculations. This new technique successfully observed that coverage of fatty acids on metal surface largely influence on the adsorption structures, i.e., orientation angle and intermolecular interaction of long-chain alkyl group. The second application was a molecular dynamics simulation for understanding influence of base oil structures on adsorption behavior of an oiliness additive. The adsorption time of an oiliness additive in three types of base oil (normal paraffin, isoparaffin, and naphthene) was measured. It was found that an oiliness additive in isoparaffin phase showed the earliest adsorption time among the tested base oils. This study unveiled the adsorption mechanisms of the oiliness additive in the base oils: an oiliness additive can smoothly pass inside the isoparaffin-derived oil film with sparse structure and easily reaches metal surface. We concluded that a rapid tribo-material discovery, as well as analysis on molecular mechanism of tribological phenomena, is expected to be achieved by adapting universal NNP to molecular simulations.