Quach Co D, Gilmer Justin B, Pert Daniel, Mason-Hogans Akanke, Iacovella Christopher R, Cummings Peter T, McCabe Clare
Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235, USA.
Interdiscplinary Materials Science, Vanderbilt University, Nashville, Tennessee 37235, USA.
J Chem Phys. 2022 Apr 21;156(15):154902. doi: 10.1063/5.0080838.
Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion.
单层膜作为一种润滑层，有望减少纳米级分离的机械设备的摩擦和磨损。这些薄膜具有广阔的设计空间，具有许多可调节的特性，这些特性会影响其摩擦学效果。例如，端基化学、薄膜组成和主链化学都可能导致具有显著不同摩擦学特性的薄膜。然而，如果没有组合方法以及可自动化、可重复和可扩展的工作流程来筛选有前景的候选薄膜，这个设计空间将很难探索。利用分子模拟设计框架（MoSDeF），进行了一项组合筛选研究，以探索9747种独特的单层膜（总共116964次模拟），并使用随机森林回归器这一集成学习技术构建机器学习（ML）模型，以探索端基化学的作用及其对摩擦学效果的影响。发现最有前景的薄膜含有氰基和乙烯等小端基。随后，该ML模型被应用于筛选从ChEMBL小分子库中识别出的端基候选物。与单独模拟相比，大约193131种独特的薄膜候选物被筛选出来，分析速度加快了大约五个数量级。因此，该ML模型能够用作预测工具，极大地加快对有前景的候选薄膜进行初步筛选，以便进行未来的模拟研究，这表明计算筛选与ML相结合可以大大提高组合方法中的通量，以生成计算机模拟数据，然后以可控、自洽的方式训练ML模型。
