• Preprint 257

Technical Report 257, c4e-Preprint Series, Cambridge

A multi-scale cross-domain thermochemical knowledge-graph

Reference: Technical Report 257, c4e-Preprint Series, Cambridge, 2020

Associated Themes:
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  • Autonomous agents for quantum chemistry and enthalpy of formation calculations are developed.
  • Enthalpies of formation are derived using error-cancelling balanced reactions.
  • The agents are integrated, exploiting knowledge-graph-enabled interoperability.
  • A multi-scale, cross-domain industrial pollution use-case is considered.
  • Quantum calculations are directly seen to affect atmospheric pollutant dispersion.

Graphical abstract 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 chemistry calculations and error-cancelling 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 formalise the definition of concepts and their relationships, as a critical step to achieve interoperability between heterogeneous data formats as well as software. The agents, which conduct quantum chemistry calculations and derive 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 (theworldavatar.com), and its eco-system 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 emissions use-case.

Material from this preprint has been published in Journal of Chemical Information and Modeling.


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