Using collective knowledge to assign oxidation states of metal cations in metal-organic frameworks

Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can fail to describe oxidation states in extended crystalline systems such as metal-organic frameworks. Here we propose the use of a machine-learning model, trained on assignments by chemists encoded in the chemical names in the Cambridge Structural Database, to automatically assign oxidation states to the metal ions in metal-organic frameworks. In our approach, only the immediate local environment around a metal centre is considered. We show that the strategy is robust to experimental uncertainties such as incorrect protonation, unbound solvents or changes in bond length. This method gives good accuracy and we show that it can be used to detect incorrect assignments in the Cambridge Structural Database, illustrating how collective knowledge can be captured by machine learning and converted into a useful tool.

Advancing computational and data-driven methods for the design and discovery of nanoporous materials

Seyedmohamad Moosavi

Metal-organic framework (MOFs) and related nanoporous materials have emerged as promising candidates for a variety of applications, such as gas separation and storage, catalysis, sensing, etc. Their building block structure allows us to generate a huge number of distinct materials only by changing the metal nodes and organic linkers. This, in principle, allows the design and discovery of materials that perform optimally for a given application. However, the conventional process of material development, from discovery to synthesis and performance evaluation, is too slow and expensive for exploring this enormous pool of materials. Complimentary methods are therefore needed to accelerate this process. The aim of this thesis is to investigate and expand the computational and data-driven methods for the development of nanoporous materials for gas separation and storage. The prevailing use of these methods is for high-throughput screening of the materials for a target application. However, the capability and success of such a screening approach depend on fast, reliable, and accurate prediction of material properties, as well as on the effective exploration of the chemical space. Therefore, in this thesis, we develop material descriptors for the chemistry and pore geometry of MOFs, which allow us to use machine learning to rapidly evaluate their adsorption properties with high accuracy. We next introduce a methodology to quantify the diversity of material databases to assess how well the chemical space is explored when a given material database is screened. We illustrate the importance of this diversity analysis by showing how the lack of diversity in MOF databases hinders material discovery, leads to chemical insights that are not generalisable and makes machine learning models not transferable. The promising materials discovered in a screening study are only of interest if they can be synthesised and are sufficiently stable to withstand the operating conditions of the corresponding application. Therefore, we investigate the applicability and capability of computational and data-driven methods to address some of the challenges in the material synthesis and the mechanical stability of MOFs. Material synthesis still mainly rests on the chemical intuition of synthetic chemists. Here, we introduce a method using machine learning and a genetic algorithm to capture this chemical intuition for MOF synthesis. We demonstrate how this simple approach can be powerful for guiding the synthesis of new materials. Lastly, we study how the mechanical stability of MOFs, which is fundamentally important for most of their practical applications, is affected by its underlying structure, i.e., the framework bonding topology and ligand structure. We show how this understanding can be used to develop strategies to design MOFs with enhanced mechanical stability.

EPFL2020

Accelerated Discovery of Metal-Organic Frameworks (MOFs) For Energy Related Applications

Arunraj Chidambaram

Metal-Organic Frameworks (MOFs) are an emerging class of materials that consist of metal ions which are linked with organic ligands via coordination bonds to form extended networks. MOF structures consists predominantly of open voids which makes them amongst the most porous materials synthesized to date. The aim of this thesis is to contribute to the development of a platform for a systematic study of the influence of synthetic conditions on MOFs and thereby, assess how this can affect the surface area of MOFs. To achieve this aim, a platform that utilises high-throughput robotic synthesis guided by genetic algorithm and machine learning was devised. This led to a rational synthesis of a MOF with the highest surface area reported to date. MOFs are considered as promising adsorbents for carbon capture from flue gasses emitted from fossil fuel fired power plants. Whilst there are many MOFs that are optimised for separation of CO2 from N2, their separation capability is detrimentally affected when they are subjected to realistic flue gas conditions that contains H2O vapour. This is due to the competition between CO2 and H2O for the same adsorption sites. To address this, through close collaboration with computational scientists, we discovered a new class of materials for CO2/H2O separations.

EPFL2020

CO2 chemical transformations using metal-organic frameworks

Serhii Shyshkanov

During the last decades, intensive efforts have been applied to tackle the enormous emissions of CO2 into atmosphere. These efforts include not only developing efficient carbon capture techniques, but also the introduction of new ways to transform the captured CO2 into the valuable products. In this context, numerous catalytic systems have been developed for the reduction of CO2. Typically, homogeneous systems provide better selectivity, due to more precise control of the chemical properties of the catalytic moiety, and require milder reaction conditions, compared to the currently employed heterogeneous catalysts. However, the majority of industrial chemical processes deploy heterogeneous catalytic systems, as their comparative stability and facile separation of the products from the catalyst facilitates scale-up and continuous production. Therefore, the crucial vector of successful transfer of the newest achievements in chemical catalysis lies in merging the advantages provided by homogeneous catalysts with the benefits of heterogeneous systems. Metal-organic frameworks (MOFs) are crystalline materials formed by the self-assembly of metal ions or clusters with organic ligands and possess unique merits, such as extremely high surface area, uniform and tunable porous structure, wide opportunities for rational design. Amalgamation of the newly developed concepts, such as FLP chemistry, showing promising results in activation of CO2, as well as recent advances in conventional catalysis with MOFs should allow the advantages of MOFs as materials with directed assembly to converge with the versatile needs of a catalytic material an improve the potential for application of them both in industry and chemical synthesis. This thesis describes the implementation of the FLP concept into MOFs. A proof of a concept, demonstrating feasibility and potential applicability of this approach for CO2 fixation is followed by the development and evaluation of a recyclable and water tolerant heterogeneous MOF catalyst for FLP-mediated reduction of CO2. Additionally, MOF-based catalysts, employing incorporated functionalities and supported metal nanoparticles, were accessed for CO2 conversion.

EPFL2020

Advancing computational and data-driven methods for the design and discovery of nanoporous materials

Seyedmohamad Moosavi

EPFL2020

CO2 chemical transformations using metal-organic frameworks

Serhii Shyshkanov

EPFL2020