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Towards efficient novel materials discovery : Acceleration of high-throughput calculations and semantic management of big data using ontologies

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Lenz-Himmer,  Maja-Olivia
NOMAD, Fritz Haber Institute, Max Planck Society;

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Lenz-Himmer, M.-O. (2022). Towards efficient novel materials discovery: Acceleration of high-throughput calculations and semantic management of big data using ontologies. PhD Thesis, Humboldt-Universität, Berlin.


Cite as: https://hdl.handle.net/21.11116/0000-000A-A95E-F
Abstract
The discovery of novel materials with specific functional properties is one of the highest goals in materials science. Screening the structural and chemical space for potential new material candidates is often facilitated by high-throughput methods. Fast and still precise computations are a main tool for such screenings and often start with a geometry relaxation to find the nearest low-energy configuration relative to the input structure. In part I of this work, a new constrained geometry relaxation is presented which maintains the perfect symmetry of a crystal, saves time and resources as well as enables relaxations of meta-stable phases and systems with local symmetries or distortions. Apart from improving such computations for a quicker screening of the materials space, better usage of existing data is another pillar that can accelerate novel materials discovery. While many different databases exists that make computational results accessible, their usability depends largely on how the data is presented. We here investigate how semantic technologies and graph representations can improve data annotation. A number of different ontologies and knowledge graphs are developed enabling the semantic representation of crystal structures, materials properties as well experimental results in the field of heterogeneous catalysis. We discuss the breakdown of the knowledge-graph approach when knowledge is created using artificial intelligence and propose an intermediate information layer. The underlying ontologies can provide background knowledge for possible autonomous intelligent agents in the future. We conclude that making materials science data understandable to machines is still a long way to go and the usefulness of semantic technologies in the domain of materials science is at the moment very limited.