Several two-dimensional (2D) materials such as graphene, molybdenum disulfide, or boron nitride are emerging as alternatives for lubrication additives to control friction and wear at the interface. On the other hand, the initiative to accelerate materials discovery through data-driven computational methods has identified numerous novel topologies and families of 2D materials that can potentially be designed as low-friction additives. Hence, generating a structure–property (friction) correlations for 2D material-based additives that present a large variation in atomic composition is the next big challenge. Herein, we present a machine learning (ML) method using the Bayesian modeling and transfer learning approach to predict the maximum energy barrier (MEB) of the potential surface energy (correlated to intrinsic friction) of ten different 2D materials that were previously unexplored for their tribological properties. The descriptors (or properties) required to train the ML model with high accuracy are identified by taking into account the established physical models for dissipation in 2D materials. As a result, a difference of less than 8% in MEB values as predicted via the ML model presented here and the PES profiles generated using molecular dynamics simulations, for a select few 2D materials, was obtained. The model also enabled the identification of material properties that present the highest sensitivity to the corrugated potential, hence enabling the development of design routes for the synthesis of 2D materials with optimal tribological properties.

中文翻译：

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使用机器学习方法预测二维材料的纳米级摩擦

几种二维（2D）材料（例如石墨烯，二硫化钼或氮化硼）正在作为润滑添加剂的替代品出现，以控制界面处的摩擦和磨损。另一方面，通过数据驱动的计算方法来加速材料发现的倡议发现了许多新颖的方法。可以设计为低摩擦添加剂的2D材料的拓扑和族。因此，为基于2D材料的添加剂产生结构-特性（摩擦）相关性会带来下一个很大的原子组成变化是下一个重大挑战。本文中，我们提出了一种使用贝叶斯建模和传递学习方法的机器学习（ML）方法，以预测先前尚未为其开发的十种不同2D材料的潜在表面能（与固有摩擦力相关）的最大能垒（MEB）。摩擦学特性。通过考虑已建立的二维材料耗散物理模型，可以确定训练ML模型所需的描述符（或属性）。结果是，通过此处介绍的ML模型预测的MEB值和使用分子动力学模拟生成的PES曲线对于选定的几种2D材料而言，相差不到8％。该模型还能够识别对瓦楞电位具有最高敏感性的材料特性，从而能够开发出具有最佳摩擦学特性的二维材料合成设计路线。