Towards an Ontology for Data-driven Discovery of New Materials

摘要: Materials scientists and nano-technologists are struggling with the challenge of managing large volumes multivariate, multidimensional mixed-media data sets being generated from experimental, characterisation, testing post-processing steps associated their search for new materials. In addition, they need to access publicly available databases containing: crystallographic structure data; thermodynamic phase stability ionic conduction data. demanding integration tools enable them across these disparate correlate experimental public databases, in order identify fertile areas searching. Systematic analysis required generate targeted programs that reduce duplication costly compound preparation, characterisation. This paper presents MatOnto – an extensible ontology, based on DOLCE upper aims represent structured knowledge about materials, properties processing involved composition engineering. The primary aim is provide a common, model exchange, re-use materials science experimentation.