MachineCat · Machine Learning for Catalytic Carbon Dioxide Activation

Machine Learning for Catalytic Carbon Dioxide Activation

Computational chemistry offers the possibility to predict chemical phenomena based entirely on computer simulations. As such, it is not only an invaluable tool for understanding chemical properties and the outcomes of reactions, but also provides guidance towards the design of new compounds and catalysts. Unfortunately, traditional approaches are subject to a balancing act between predictive accuracy and efficiency. Highly accurate methods are limited small molecules, due to their prohibitive computational cost, while more efficient approaches employ approximations rendering them less reliable. Machine learning (ML) has emerged as a powerful tool to overcome these limitations. ML models are able to “learn” to behave like highly accurate computational chemistry methods, providing predictions at only fraction of the original computational cost, but with the same accuracy. Hence, they can be used to simulate molecular systems at levels of precision beyond the reach of conventional approaches. However, due to their young age and inherent black box nature, little is understood of the inner workings of these ML models. This makes their predictions hard to rationalize and complicates the systematic improvement of current ML approaches, as it is not clear how to incorporate additional physical knowledge. The main objective of Machine Learning for Catalytic Carbon Dioxide Activation (MachineCat) was to deepen our understanding of ML in computational chemistry and use this knowledge to push the boundaries of existing approaches. To this end, MachineCat studied chemical problems which prove challenging to current ML methods, focusing on the organocatalytic conversion of carbon dioxide mediated by a modified chitosan catalyst. This reaction is highly relevant for sustainable chemistry, as it offers cheap access to value-added chemicals, potentially replacing fossil fuels as primary carbon source. Yet, little detail is known on how the reaction proceeds and one objectives of MachineCat was to use ML approaches to elucidate the reaction mechanism. As a final objective, MachineCat aimed to explore the potential of ML methods for the rational design of new compounds and improved catalytic systems. The conclusions found in MachineCat demonstrate the utility of modern ML architectures beyond providing efficient and accurate models of complex chemical systems. By incorporating physical relations into the structure of these ML models, their predictions and internal states can be readily understood in the context of fundamental chemical concepts, such as atomic charges and orbitals. Moreover, physical laws can be integrated in such a way, that the rich formalism of quantum mechanics can be applied directly to these ML models. This offers access a vast range of chemical properties and even the molecular wavefunction itself. Such models provide a direct relationship between chemical structure, composition and properties, which can be leveraged to design compounds with desirable qualities. Finally, by incorporating ideas from the field of ML, it is even possible to construct models which can directly generate the structure and composition of novel compounds.

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