Department of Chemistry , University of Crete , Voutes Campus , GR-70013 Heraklion , Crete , Greece.
Advanced Technology Division, Toyota Motor Europe NV/SA , Technical Center , Hoge Wei 33B , 1930 Zaventem , Belgium.
J Am Chem Soc. 2020 Feb 26;142(8):3814-3822. doi: 10.1021/jacs.9b11084. Epub 2020 Feb 12.
Application of machine learning (ML) methods for the determination of the gas adsorption capacities of nanomaterials, such as metal-organic frameworks (MOF), has been extensively investigated over the past few years as a computationally efficient alternative to time-consuming and computationally demanding molecular simulations. Depending on the thermodynamic conditions and the adsorbed gas, ML has been found to provide very accurate results. In this work, we go one step further and we introduce chemical intuition in our descriptors by using the "type" of the atoms in the structure, instead of the previously used building blocks, to account for the chemical character of the MOF. ML predictions for the methane and carbon dioxide adsorption capacities of several tens of thousands of hypothetical MOFs are evaluated at various thermodynamic conditions using the random forest algorithm. For all cases examined, the use of atom types instead of building blocks leads to significantly more accurate predictions, while the number of MOFs needed for the training of the ML algorithm in order to achieve a specified accuracy can be reduced by an order of magnitude. More importantly, since practically there are an unlimited number of building blocks that materials can be made of but a limited number of atom types, the proposed approach is more general and can be considered as universal. The universality and transferability was proved by predicting the adsorption properties of a completely different family of materials after the training of the ML algorithm in MOFs.
机器学习 (ML) 方法在过去几年中被广泛应用于纳米材料（如金属有机骨架 (MOF)）的气体吸附容量的测定，作为一种计算效率更高的替代方法，可以替代耗时且计算量大的分子模拟。根据热力学条件和吸附气体的不同，ML 被发现可以提供非常准确的结果。在这项工作中，我们更进一步，通过使用结构中的“原子类型”而不是之前使用的构建块来引入化学直觉，以说明 MOF 的化学性质。使用随机森林算法在各种热力学条件下评估了数十万种假设 MOF 的甲烷和二氧化碳吸附容量的 ML 预测。对于所有检查的情况，使用原子类型而不是构建块可以导致更准确的预测，并且为了达到指定的精度，训练 ML 算法所需的 MOF 的数量可以减少一个数量级。更重要的是，由于实际上可以用无限数量的构建块来制造材料，但只有有限数量的原子类型，因此所提出的方法更通用，可以被认为是通用的。通过在 MOF 中训练 ML 算法后预测完全不同材料家族的吸附性能，证明了这种方法的通用性和可转移性。
