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

Abstract

Metrics & MoreArticle Recommendations CONSPECTUS: 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).

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

“…A promising solution lies in Graph Attention Networks (GATs), which assign weights to node embeddings based on neighborhood information, thereby highlighting influential graph features. 108 While graph representations seem well-suited for multiscale mixtures, it is important to acknowledge that chemistry also involves 'paper tools' and symbolic representations 109,110 weaving the "torn web" 111 of chemical knowledge 112 afresh. Language models, which could be expected to complement traditional handbooks, still struggle with converting from string representations to molecule names, 113 indicating the need for further training across diverse chemical representations.…”

Semantic agent framework for automated flood assessment using dynamic knowledge graphs

“…The World Avatar (TWA) project intends to create an all-encompassing model of our world, with a current emphasis on automation and decarbonization in chemistry (Farazi et al, 2020;Bai et al, 2022;Kondinski et al, 2023), process and energy industry (Devanand et al, 2020;Atherton et al, 2021), and smart cities and city planning (Chadzynski et al, 2022(Chadzynski et al, , 2023a(Chadzynski et al, , 2023b. TWA aims to provide a technology agnostic and scalable architecture based on open standards and protocols to create a collaborative knowledgemodel based system to foster interoperability along three themes (Akroyd et al, 2021): (1) providing cross-domain insights into the current state of physical assets in the real world, (2) controlling real-world entities, and (3) facilitating complex what-if scenario analyses.…”

From Platform to Knowledge Graph: Distributed Self-Driving Laboratories

“…As suggested in [30,34,35], we follow the steps from specifying target deliverables to conceptualising relevant concepts and finally implementing codes for queries. Aimed at capturing data and material flow in distributed SDLs, the relevant concepts range from the reaction experiment to the hardware employed to conduct it.…”

“…As the knowledge graph expands, this characteristic allows for capturing data provenance from experimental processes as knowledge statements, effectively acting as a living copy of the real world. By facilitating immediate dissemination of data between SDLs at its creation, the dynamic knowledge graph presents a promising holistic solution to the challenges aforementioned [30,35] and supports the realisation of "AI Scientists" [34,36].…”