Materials design has evolved from experimental trial-and-error, to model-based theoretical science and physics-based computational methods, and have now entered the paradigm of data-driven methods. During the past decades physics-based methods such as density functional theory (DFT) has been the driving force of computational materials design, and have been used to produce large sets of materials data by academic groups across the world. Since different databases use different technologies and data formats, the data production has resulted in scattered heterogeneous materials database designs. Data driven design of materials relies on data and methods operating on the data such as machine learning, neural networks, and analysis tools. To enable the full potential of the emerging data operational methods, especially the learning-based methods which commonly improves in correlation to the amount of training data, we need to make the data collectively accessible.

Database integration and interoperability are used to make the data collectively and viably accessible, where ontologies are key to, e.g., make data storages machine interpretable and unification of heterogenous data descriptions. This thesis presents an ontology for units of measures specifically targeting challenges of handling units across databases of computational and experimental data. The ontology is created using definition files part of the property definitions in release 1.2, from the community-driven OPTIMADE standard for a common application programming interface (API) for materials databases. The resulting ontology allows addressing data integration challenges encountered in that effort, which were unsolvable with available tools. These challenges include reference to specific and generalized units that have changed over time, using relevant symbols for different scientific domains, specification of relationships between units, and representation of units not part of System of Units (SI) without defining them in SI units or using SI system conventions.

Developments of structure-to-property machine learning and artificial intelligence (AI) mod-els part of data-driven methods has sped-up traditional physics-based simulation methods. Despite the speed increase of structure-to-property predictions, all possible materials is a massive space which is intractable to screen exhaustively, which leads to the idea of in-verse materials design using property-to-structure prediction. Generative AI models are promising candidates for property-to-structure prediction. Most existing crystal structure generative AI models does not account for symmetry, resulting in generation of high concentration of low-symmetry crystal structures. This thesis presents a generative AI model, capable of generating novel and stable symmetry-based descriptions of crystal structures, which inherently accounts for crystal structure symmetry.