Linking reaction mechanisms and quantum chemistry: An ontological approach

OntoPESScan: An Ontology for Potential Energy Surface Scans

In this work, a new OntoPESScan ontology is developed for the semantic representation of one-dimensional potential energy surface (PES) scans, a central concept in computational chemistry. This ontology is developed in line with knowledge graph principles and The World Avatar (TWA) project. OntoPESScan is linked to other ontologies for chemistry in TWA, including OntoSpecies, which helps uniquely identify species along the PES and access their properties, and OntoCompChem, which allows the association of potential energy surfaces with quantum chemical calculations and the concepts used to derive them. A force-field fitting agent is also developed that makes use of the information in the OntoPESScan ontology to fit force fields to reactive surfaces of interest on the fly by making use of the empirical valence bond methodology. This agent is demonstrated to successfully parametrize two cases, namely, a PES scan on ethanol and a PES scan on a localized π-radical PAH hypothesized to play a role in soot formation during combustion. OntoPESScan is an extension to the capabilities of TWA and, in conjunction with potential further ontological support for molecular dynamics and reactions, will further progress toward an open, continuous, and self-growing knowledge graph for chemistry.

Knowledge Engineering in Chemistry: From Expert Systems to Agents of Creation

Passing knowledge from human to human is a natural process that has continued since the beginning of humankind. Over the past few decades, we have witnessed that knowledge is no longer passed only between humans but also from humans to machines. The latter form of knowledge transfer represents a cornerstone in artificial intelligence (AI) and lays the foundation for knowledge engineering (KE). In order to pass knowledge to machines, humans need to structure, formalize, and make knowledge machine-readable. Subsequently, humans also need to develop software that emulates their decision-making process. In order to engineer chemical knowledge, chemists are often required to challenge their understanding of chemistry and thinking processes, which may help improve the structure of chemical knowledge.Knowledge engineering in chemistry dates from the development of expert systems that emulated the thinking process of analytical and organic chemists. Since then, many different expert systems employing rather limited knowledge bases have been developed, solving problems in retrosynthesis, analytical chemistry, chemical risk assessment, etc. However, toward the end of the 20th century, the AI winters slowed down the development of expert systems for chemistry. At the same time, the increasing complexity of chemical research, alongside the limitations of the available computing tools, made it difficult for many chemistry expert systems to keep pace.In the past two decades, the semantic web, the popularization of object-oriented programming, and the increase in computational power have revitalized knowledge engineering. Knowledge formalization through ontologies has become commonplace, triggering the subsequent development of knowledge graphs and cognitive software agents. These tools enable the possibility of interoperability, enabling the representation of more complex systems, inference capabilities, and the synthesis of new knowledge.This Account introduces the history, the core principles of KE, and its applications within the broad realm of chemical research and engineering. In this regard, we first discuss how chemical knowledge is formalized and how a chemist's cognition can be emulated with the help of reasoning algorithms. Following this, we discuss various applications of knowledge graph and agent technology used to solve problems in chemistry related to molecular engineering, chemical mechanisms, multiscale modeling, automation of calculations and experiments, and chemist-machine interactions. These developments are discussed in the context of a universal and dynamic knowledge ecosystem, referred to as The World Avatar (TWA).

Automated Rational Design of Metal-Organic Polyhedra

Metal-organic polyhedra (MOPs) are hybrid organic-inorganic nanomolecules, whose rational design depends on harmonious consideration of chemical complementarity and spatial compatibility between two or more types of chemical building units (CBUs). In this work, we apply knowledge engineering technology to automate the derivation of MOP formulations based on existing knowledge. For this purpose we have (i) curated relevant MOP and CBU data; (ii) developed an assembly model concept that embeds rules in the MOP construction; (iii) developed an OntoMOPs ontology that defines MOPs and their key properties; (iv) input agents that populate The World Avatar (TWA) knowledge graph; and (v) input agents that, using information from TWA, derive a list of new constructible MOPs. Our result provides rapid and automated instantiation of MOPs in TWA and unveils the immediate chemical space of known MOPs, thus shedding light on new MOP targets for future investigations.

Chemical reaction network knowledge graphs: the OntoRXN ontology

The organization and management of large amounts of data has become a major point in almost all areas of human knowledge. In this context, semantic approaches propose a structure for the target data, defining ontologies that state the types of entities on a certain field and how these entities are interrelated. In this work, we introduce OntoRXN, a novel ontology describing the reaction networks constructed from computational chemistry calculations. Under our paradigm, these networks are handled as undirected graphs, without assuming any traversal direction. From there, we propose a core class structure including reaction steps, network stages, chemical species, and the lower-level entities for the individual computational calculations. These individual calculations are founded on the OntoCompChem ontology and on the ioChem-BD database, where information is parsed and stored in CML format. OntoRXN is introduced through several examples in which knowledge graphs based on the ontology are generated for different chemical systems available on ioChem-BD. Finally, the resulting knowledge graphs are explored through SPARQL queries, illustrating the power of the semantic approach to standardize the analysis of intricate datasets and to simplify the development of complex workflows.

From Platform to Knowledge Graph: Evolution of Laboratory Automation

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.

eScience Infrastructures in Physical Chemistry

As the volume of data associated with scientific research has exploded over recent years, the use of digital infrastructures to support this research and the data underpinning it has increased significantly. Physical chemists have been making use of eScience infrastructures since their conception, but in the last five years their usage has increased even more. While these infrastructures have not greatly affected the chemistry itself, they have in some cases had a significant impact on how the research is undertaken. The combination of the human effort of collaboration to create open source software tools and semantic resources, the increased availability of hardware for the laboratories, and the range of data management tools available has made the life of a physical chemist significantly easier. This review considers the different aspects of eScience infrastructures and explores how they have improved the way in which we can conduct physical chemistry research. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Question Answering System for Chemistry

This paper describes the implementation and evaluation of a proof-of-concept Question Answering (QA) system for accessing chemical data from knowledge graphs (KGs) which offer data from chemical kinetics to the chemical and physical properties of species. We trained the question classification and named the entity recognition models that specialize in interpreting chemistry questions. The system has a novel design which applies a topic model to identify the question-to-ontology affiliation to handle ontologies with different structures. The topic model also helps the system to provide answers with a higher quality. Moreover, a new method that automatically generates training questions from ontologies is also implemented. The question set generated for training contains 432,989 questions under 11 types. Such a training set has been proven to be effective for training both the question classification model and the named entity recognition model. We evaluated the system using other KGQA systems as baselines. The system outperforms the chosen KGQA system answering chemistry-related questions. The QA system is also compared to the Google search engine and the WolframAlpha engine. It shows that the QA system can answer certain types of questions better than the search engines.

Automated Calibration of a Poly(oxymethylene) Dimethyl Ether Oxidation Mechanism Using the Knowledge Graph Technology

In this paper, we develop a knowledge graph-based framework for the automated calibration of combustion reaction mechanisms and demonstrate its effectiveness on a case study of poly(oxymethylene)dimethyl ether (PODE_n, where n = 3) oxidation. We develop an ontological representation for combustion experiments, OntoChemExp, that allows for the semantic enrichment of experiments within the J-Park simulator (JPS, theworldavatar.com), an existing cross-domain knowledge graph. OntoChemExp is fully capable of supporting experimental results in the Process Informatics Model (PrIMe) database. Following this, a set of software agents are developed to perform experimental result retrieval, sensitivity analysis, and calibration tasks. The sensitivity analysis agent is used for both generic sensitivity analyses and reaction selection for subsequent calibration. The calibration process is performed as a sampling task, followed by an optimization task. The agents are designed for use with generic models but are demonstrated with ignition delay time and laminar flame speed simulations. We find that calibration times are reduced, while accuracy is increased compared to manual calibration, achieving a 79% decrease in the objective function value, as defined in this study. Further, we demonstrate how this workflow is implemented as an extension of the JPS.

Multiscale Cross-Domain Thermochemical Knowledge-Graph

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.