Due to their exceptional host-guest properties, Metal-Organic Frameworks (MOFs) are promising materials for storage of various gases with environmental and technological interest. Molecular modeling and simulations are invaluable tools, extensively used over the last two decades for the study of various properties of MOFs. In particular, Monte Carlo simulation techniques have been employed for the study of the gas uptake capacity of several MOFs at a wide range of different thermodynamic conditions. Despite the accurate predictions of molecular simulations, the accurate characterization and the high-throughput screening of the enormous number of MOFs that can be potentially synthesized by combining various structural building blocks is beyond present computer capabilities. In this work, we propose and demonstrate the use of an alternative approach, namely one based on an Automated Machine Learning (AutoML) architecture that is capable of training machine learning and statistical predictive models for MOFs’ chemical properties and estimate their predictive performance with confidence intervals. The architecture tries numerous combinations of different machine learning (ML) algorithms, tunes their hyper-parameters, and conservatively estimates performance of the final model. We demonstrate that it correctly estimates performance even with few samples (<100) and that it provides improved predictions over trying a single standard method, like Random Forests. The AutoML pipeline democratizes ML to non-expert material-science practitioners that may not know which algorithms to use on a given problem, how to tune them, and how to correctly estimate their predictive performance, dramatically improving productivity and avoiding common analysis pitfalls. A demonstration on the prediction of the carbon dioxide and methane uptake at various thermodynamic conditions is used as a showcase sharable at https://app.jadbio.com/share/86477fd7-d467-464d-ac41-fcbb0475444b.

由于其非凡的宾客特性，金属有机框架（MOF）是有前途的材料，可用于存储具有环境和技术意义的各种气体。分子建模和模拟是无价的工具，在过去的二十年中广泛用于研究MOF的各种特性。特别是，蒙特卡罗模拟技术已用于研究多种MOF在各种不同的热力学条件下的气体吸收能力。尽管对分子模拟进行了准确的预测，但通过组合各种结构构件可以潜在地合成的大量MOF的准确表征和高通量筛选仍超出了当前的计算机功能。在这项工作中，我们提出并演示了另一种方法的使用，即一种基于自动机器学习（AutoML）架构的软件，它能够针对MOF的化学性质训练机器学习和统计预测模型，并以置信区间估算其预测性能。该体系结构尝试了多种不同机器学习（ML）算法的组合，调整了它们的超参数，并保守地估计了最终模型的性能。我们证明了即使使用很少的样本（<100），它也可以正确地估计性能，并且与尝试使用单个标准方法（如随机森林）相比，它可以提供更好的预测。AutoML管道将ML民主化为非专业的材料科学从业者，他们可能不知道在给定问题上使用哪种算法，如何调整它们以及如何正确估计其预测性能，大大提高了生产率，避免了常见的分析陷阱。在https://app.jadbio.com/share/86477fd7-d467-464d-ac41-fcbb0475444b上可以共享关于在各种热力学条件下预测二氧化碳和甲烷吸收量的演示。