Shahbazi Fatemeh, Esfahani Mohammad Nasr, Keshmiri Amir, Jabbari Masoud
Warwick Manufacturing Group (WMG), University of Warwick, Coventry, CV4 7AL, UK.
School of Engineering, University of Manchester, Manchester, M13 9PL, UK.
Sci Rep. 2024 Sep 3;14(1):20437. doi: 10.1038/s41598-024-71007-z.
Molecular dynamics (MD) simulations can reduce our need for experimental tests and provide detailed insight into the chemical reactions and binding kinetics. There are two challenges while dealing with MD simulations: one is the time and length scale limitations, and the latter is efficiently processing the massive amount of data resulting from the MD simulations and generating the proper reaction rates. In this work, we evaluated the use of regression machine learning (ML) methods to solve these two challenges by developing a framework for ethanol adsorption on an Aluminium (Al) slab. This framework comprises three main stages: first, an all-atom molecular dynamics model; second, ML regression models; and third, validation and testing. In stage one, the adsorption of ethanol molecules on the Al surface for various temperatures, velocities and concentrations is simulated using the large-scale atomic/molecular massively parallel simulator (LAMMPS) and ReaxFF. The outcome of stage one is utilised for training, testing, and validating the predictive models in stages two and three. We developed and evaluated 28 different ML models for predicting the number of adsorbed molecules over time, including linear regression, support vector machine (SVM), decision trees, ensemble, Gaussian process regression (GPR), neural network (NN) and Bayesian hyper-parameter optimisation models. Based on the results, the Bayesian-based GPR showed the highest accuracy and the lowest training time. The developed model can predict the number of adsorbed molecules for new cases within seconds, while MD simulations take a few weeks. This adsorption rate can then be used in macroscale simulations to tackle the time and length scale limitations. The proposed numerical framework has the potential to be generalised and, therefore, contribute to future low-cost binding reaction estimations, providing a valuable tool for industry and experimentalists.
分子动力学（MD）模拟可以减少我们对实验测试的需求，并深入洞察化学反应和结合动力学。在处理MD模拟时存在两个挑战：一是时间和长度尺度的限制，另一个是有效处理MD模拟产生的大量数据并生成适当的反应速率。在这项工作中，我们通过开发一个乙醇在铝（Al）平板上吸附的框架，评估了使用回归机器学习（ML）方法来解决这两个挑战。该框架包括三个主要阶段：第一，全原子分子动力学模型；第二，ML回归模型；第三，验证和测试。在第一阶段，使用大规模原子/分子大规模并行模拟器（LAMMPS）和反应力场（ReaxFF）模拟乙醇分子在不同温度、速度和浓度下在Al表面的吸附。第一阶段的结果用于训练、测试和验证第二和第三阶段的预测模型。我们开发并评估了28种不同的ML模型，用于预测随时间吸附的分子数量，包括线性回归、支持向量机（SVM）、决策树、集成模型、高斯过程回归（GPR）、神经网络（NN）和贝叶斯超参数优化模型。基于结果，基于贝叶斯的GPR显示出最高的准确性和最短的训练时间。所开发的模型可以在几秒钟内预测新情况下吸附的分子数量，而MD模拟则需要几周时间。然后，这个吸附速率可用于宏观模拟，以解决时间和长度尺度的限制。所提出的数值框架有可能被推广，因此有助于未来低成本的结合反应估计，为工业界和实验人员提供一个有价值的工具。
