Multiscale Cross-Domain Thermochemical Knowledge-Graph.
Journal of chemical information and modeling
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Mosbach, S., Menon, A., Farazi, F., Krdzavac, N., Zhou, X., Akroyd, J., & Kraft, M. (2020). Multiscale Cross-Domain Thermochemical Knowledge-Graph.. Journal of chemical information and modeling, 60 (12), 6155-6166. https://doi.org/10.1021/acs.jcim.0c01145
In this paper, we develop a set of software agents which improve a knowledge-graph containing thermodynamic data of chemical species by means of quantum chemical calculations and error-canceling balanced reactions. The knowledge-graph represents species-associated information by making use of the principles of linked data, as employed in the Semantic Web, where concepts correspond to vertices and relationships between the concepts correspond to edges of the graph. We implement this representation by means of ontologies, which formalize the definition of concepts and their relationships, as a critical step to achieve interoperability between heterogeneous data formats and software. The agents, which conduct quantum chemical calculations and derive the estimates of standard enthalpies of formation, update the knowledge-graph with newly obtained results, improving data values, and adding nodes and connections between them. A key distinguishing feature of our approach is that it extends an existing, general-purpose knowledge-graph, called J-Park Simulator (http://theworldavatar.com), and its ecosystem of autonomous agents, thus enabling seamless cross-domain applications in wider contexts. To this end, we demonstrate how quantum calculations can directly affect the atmospheric dispersion of pollutants in an industrial emission use-case.
Ecosystem, Thermodynamics, Software
EPSRC (via Newcastle University) (EP/R029369/1)
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External DOI: https://doi.org/10.1021/acs.jcim.0c01145
This record's URL: https://www.repository.cam.ac.uk/handle/1810/323288
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