Using molecular simulation for adsorbent screening is computationally expensive and thus prohibitive to materials discovery. Machine learning (ML) algorithms trained on fundamental material properties can potentially provide quick and accurate methods for screening purposes. Prior efforts have focused on structural descriptors for use with ML. In this work, the use of chemical descriptors, in addition to structural descriptors, was introduced for adsorption analysis. Evaluation of structural and chemical descriptors coupled with various ML algorithms, including decision tree, Poisson regression, support vector machine and random forest, were carried out to predict methane uptake on hypothetical metal organic frameworks. To highlight their predictive capabilities, ML models were trained on 8% of a data set consisting of 130,398 MOFs and then tested on the remaining 92% to predict methane adsorption capacities. When structural and chemical descriptors were jointly used as ML input, the random forest model with 10-fold cross validation proved to be superior to the other ML approaches, with an R² of 0.98 and a mean absolute percent error of about 7%. The training and prediction using the random forest algorithm for adsorption capacity estimation of all 130,398 MOFs took approximately 2 h on a single personal computer, several orders of magnitude faster than actual molecular simulations on high-performance computing clusters.

中文翻译：

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使用组合的结构和化学描述符进行机器学习，以预测金属有机骨架（MOF）的甲烷吸附性能

使用分子模拟进行吸附剂筛选在计算上是昂贵的，因此阻碍了材料的发现。经过基本材料性能训练的机器学习（ML）算法可以潜在地提供用于筛选目的的快速准确的方法。先前的工作集中在与ML一起使用的结构描述符上。在这项工作中，除了结构描述符外，还介绍了使用化学描述符进行吸附分析的方法。结合决策树，泊松回归，支持向量机和随机森林等各种ML算法对结构和化学描述符进行评估，以预测假设金属有机框架上的甲烷吸收。为了突出其预测能力，我们对ML模型进行了训练，该模型使用了8％的数据集，其中包括130个数据集，398个MOF，然后对剩余的92％进行测试以预测甲烷的吸附能力。当将结构和化学描述符共同用作ML输入时，具有10倍交叉验证的随机森林模型被证明优于其他ML方法，并且具有R ²为0.98，平均绝对百分比误差约为7％。使用随机森林算法对所有130,398个MOF的吸附容量进行估计和预测，在一台个人计算机上花费了大约2小时，比高性能计算集群上的实际分子模拟快了几个数量级。