Geometric landscapes for material discovery within energy–structure–function maps

Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal–organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy–structure–function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1000s to 10 000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications.

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

Luminescent, porous, metal-organic crystals: Design, synthesis, and applications

Fatmah Ebrahim

The work presented in this thesis is focused on the design and discovery of new luminescent, porous, crystalline materials for targeted applications. In particular, we focus on luminescent Metal-Organic Frameworks (MOFs) and Organic Molecules of Intrinsic Microporosity (OMIMs), both belonging to emerging classes of synthetic materials that are particularly promising due to their porosity, modularity, and the many dynamic photophysical processes that can be exploited within them. While there is great potential for these types of materials, their relatively recent discovery, less than two decades prior to the writing of this thesis, means that they are still in early stages of development and so very few examples exist that demonstrate the potential for real-world applicability. Challenges persist in achieving targeted design, high stability, or processing for integration into devices. The aim of this thesis is consider new ways of strategically designing MOFs and OMIMs that may be carried a step closer to real-world application. With problems like excess energy consumption and the depletion of clean air and water supplies on the rise in todayâs world, applications that can have a positive impact on the environment are of particular interest. We present a luminescent MOF that is capable of detecting trace amounts of fluoride contaminations in drinking water down to the parts per billion (ppb) range. The interactions of this MOF with fluoride in aqueous solutions are simultaneously electrostatic and specific in nature because of the carefully designed structure of its active site. This allows the material to be easily regenerated and used over 10 cycles, setting it apart from most existing molecular and polymeric fluoride sensors. We combined our MOF with a portable prototype sampling device that was designed and built in-house to measure fluoride concentrations in natural groundwater samples taken from three different countries, with the results showing excellent agreement with ion chromatography analysis. The strategy that we use to obtain this reversible yet selective interaction can be applied to the design of new MOFs that work on a similar principle. In addition, we present a new OMIM that exhibits broad spectrum, tuneable white light emission. The structural components and pore environment of the OMIM contribute to it having a high quantum yield in addition to tuneability that can be controlled by guest molecules. Using this material, we obtain the highest thus-far reported value of photoluminescence quantum yield for a single-species white-light emitter, with a nearly pure-white emission colour. This has promising implications for the fabrication of new, energy-efficient Organic Light-Emitting Diode (OLED) devices. In addition, introducing structural changes to the base ligand of this OMIM will allow us to obtain a greater range of colour tuneability as well as new features of guest-host chemistry.

EPFL2021

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

Seyedmohamad Moosavi

EPFL2020

Luminescent, porous, metal-organic crystals: Design, synthesis, and applications

Fatmah Ebrahim

EPFL2021