1
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Bai J, Mosbach S, Taylor CJ, Karan D, Lee KF, Rihm SD, Akroyd J, Lapkin AA, Kraft M. A dynamic knowledge graph approach to distributed self-driving laboratories. Nat Commun 2024; 15:462. [PMID: 38263405 PMCID: PMC10805810 DOI: 10.1038/s41467-023-44599-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist's research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.
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Affiliation(s)
- Jiaru Bai
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
| | - Sebastian Mosbach
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Connor J Taylor
- Astex Pharmaceuticals, 436 Cambridge Science Park Milton Road, Cambridge, CB4 0QA, UK
- Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- Faculty of Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Dogancan Karan
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Kok Foong Lee
- CMCL Innovations, Sheraton House, Cambridge, CB3 0AX, UK
| | - Simon D Rihm
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Jethro Akroyd
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Alexei A Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
- Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Markus Kraft
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore.
- School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459, Singapore, Singapore.
- The Alan Turing Institute, London, NW1 2DB, UK.
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2
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Du W, Wang X, Zhu Q, Jing X, Liu X. CPBA-CLIM: An entity-relation extraction model for ontology-based knowledge graph construction in hazardous chemical incident management. Sci Prog 2024; 107:368504241235510. [PMID: 38490167 PMCID: PMC10943738 DOI: 10.1177/00368504241235510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
In recent years, hazardous chemical incidents have occurred frequently, resulting in significant human casualties, property damage, and environmental pollution due to human or natural factors. Accurately mining the lessons learned from accumulating incident reports and constructing the knowledge graph for hazardous chemical incident management can assist managers in identifying patterns and analyzing common attributes, thereby preventing the recurrence of similar incidents. This article addresses the challenges of dispersed textual information, specialized vocabulary, and data formats in hazardous chemical incidents. We propose a novel entity-relation extraction model called CPBA-CLIM (content-position-based attention-cross-label intersect matching) to provide an accurate data foundation for constructing the hazardous chemical incident knowledge graph. The content-position-based attention module, based on content-position attention, incorporates contextual semantic information into the combined encoding of bidirectional encoder representations from the transformer's content and position to obtain dynamic word vectors that align with the thematic context of the text. Additionally, the cross-label intersect matching strategy evaluates the rationality of entity-relation interactions in sets containing potential overlaps, reducing the impact of entity-relation overlap on triplet extraction accuracy. Comparative experimental results on public datasets demonstrate the model's outstanding performance in overlapping triplets. Qualitative experiments on a self-constructed dataset integrate our model with ontology construction techniques, successfully establishing a knowledge graph for managing hazardous chemical incidents. This research effectively enhances the degree of automation and efficiency in knowledge graph construction, thus offering support and decision-making foundations for hazardous chemical safety management.
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Affiliation(s)
- Wanru Du
- China Aerospace Academy of Systems Science and Engineering, Beijing, China
- Aerospace Hongka Intelligent Technology (Beijing) Co., Ltd., Beijing, China
| | - Xiaoyin Wang
- Aerospace Hongka Intelligent Technology (Beijing) Co., Ltd., Beijing, China
| | - Quan Zhu
- China Aerospace Academy of Systems Science and Engineering, Beijing, China
- Aerospace Hongka Intelligent Technology (Beijing) Co., Ltd., Beijing, China
| | - Xiaochuan Jing
- Aerospace Hongka Intelligent Technology (Beijing) Co., Ltd., Beijing, China
| | - Xuan Liu
- China Aerospace Academy of Systems Science and Engineering, Beijing, China
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3
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Schrier J, Norquist AJ, Buonassisi T, Brgoch J. In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science. J Am Chem Soc 2023; 145:21699-21716. [PMID: 37754929 DOI: 10.1021/jacs.3c04783] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials.
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Affiliation(s)
- Joshua Schrier
- Department of Chemistry, Fordham University, The Bronx, New York 10458, United States
| | - Alexander J Norquist
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States
| | - Tonio Buonassisi
- Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Jakoah Brgoch
- Department of Chemistry and Texas Center for Superconductivity, University of Houston, Houston, Texas 77204, United States
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4
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Simone F, Ansaldi SM, Agnello P, Patriarca R. Industrial safety management in the digital era: Constructing a knowledge graph from near misses. COMPUT IND 2023. [DOI: 10.1016/j.compind.2022.103849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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5
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Menon A, Pascazio L, Nurkowski D, Farazi F, Mosbach S, Akroyd J, Kraft M. OntoPESScan: An Ontology for Potential Energy Surface Scans. ACS OMEGA 2023; 8:2462-2475. [PMID: 36687109 PMCID: PMC9850739 DOI: 10.1021/acsomega.2c06948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
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.
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Affiliation(s)
- Angiras Menon
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Laura Pascazio
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create
Way, CREATE Tower, #05-05, Singapore 138602
| | - Daniel Nurkowski
- CMCL
Innovations, Sheraton House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Feroz Farazi
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Sebastian Mosbach
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create
Way, CREATE Tower, #05-05, Singapore 138602
| | - Jethro Akroyd
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create
Way, CREATE Tower, #05-05, Singapore 138602
| | - Markus Kraft
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create
Way, CREATE Tower, #05-05, Singapore 138602
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459
- The
Alan Turing Institute, London NW1 2BD, United
Kingdom
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6
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Lim PK, Julca I, Mutwil M. Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data. Comput Struct Biotechnol J 2023; 21:1639-1650. [PMID: 36874159 PMCID: PMC9976193 DOI: 10.1016/j.csbj.2023.01.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The immense structural diversity of products and intermediates of plant specialized metabolism (specialized metabolites) makes them rich sources of therapeutic medicine, nutrients, and other useful materials. With the rapid accumulation of reactome data that can be accessible on biological and chemical databases, along with recent advances in machine learning, this review sets out to outline how supervised machine learning can be used to design new compounds and pathways by exploiting the wealth of said data. We will first examine the various sources from which reactome data can be obtained, followed by explaining the different machine learning encoding methods for reactome data. We then discuss current supervised machine learning developments that can be employed in various aspects to help redesign plant specialized metabolism.
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Affiliation(s)
- Peng Ken Lim
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Irene Julca
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
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7
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Kondinski A, Bai J, Mosbach S, Akroyd J, Kraft M. Knowledge Engineering in Chemistry: From Expert Systems to Agents of Creation. Acc Chem Res 2022; 56:128-139. [PMID: 36516456 PMCID: PMC9850921 DOI: 10.1021/acs.accounts.2c00617] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
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).
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Affiliation(s)
- Aleksandar Kondinski
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Jiaru Bai
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Sebastian Mosbach
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.,CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, 138602 Singapore
| | - Jethro Akroyd
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.,CMCL
Innovations, Sheraton
House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Markus Kraft
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.,CARES,
Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, 138602 Singapore,School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459 Singapore,E-mail:
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8
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Liu S, Liu Y, Yang C, Deng L. Relative Entropy of Distance Distribution Based Similarity Measure of Nodes in Weighted Graph Data. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1154. [PMID: 36010818 PMCID: PMC9407273 DOI: 10.3390/e24081154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/10/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Many similarity measure algorithms of nodes in weighted graph data have been proposed by employing the degree of nodes in recent years. Despite these algorithms obtaining great results, there may be still some limitations. For instance, the strength of nodes is ignored. Aiming at this issue, the relative entropy of the distance distribution based similarity measure of nodes is proposed in this paper. At first, the structural weights of nodes are given by integrating their degree and strength. Next, the distance between any two nodes is calculated with the help of their structural weights and the Euclidean distance formula to further obtain the distance distribution of each node. After that, the probability distribution of nodes is constructed by normalizing their distance distributions. Thus, the relative entropy can be applied to measure the difference between the probability distributions of the top d important nodes and all nodes in graph data. Finally, the similarity of two nodes can be measured in terms of this above-mentioned difference calculated by relative entropy. Experimental results demonstrate that the algorithm proposed by considering the strength of node in the relative entropy has great advantages in the most similar node mining and link prediction.
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9
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Lim S, Lee S, Piao Y, Choi M, Bang D, Gu J, Kim S. On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach. Comput Struct Biotechnol J 2022; 20:4288-4304. [PMID: 36051875 PMCID: PMC9399946 DOI: 10.1016/j.csbj.2022.07.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 11/22/2022] Open
Abstract
A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compounds databases. We first compile what kind of tasks are trying to be accomplished by machine learning methods. Then, we survey deep learning technologies to show their modeling power and current applications for accomplishing drug related tasks. Next, we survey deep learning techniques to address the insufficiency issue of annotated data for more effective navigation of chemical space. Chemical compound information alone may not be powerful enough for drug related tasks, thus we survey what kind of information, such as assay and gene expression data, can be used to improve the prediction power of deep learning models. Finally, we conclude this survey with four important newly developed technologies that are yet to be fully incorporated into computational analysis of chemical information.
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Affiliation(s)
- Sangsoo Lim
- Bioinformatics Institute, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Sangseon Lee
- Institute of Computer Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Yinhua Piao
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - MinGyu Choi
- Department of Chemistry, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- MOGAM Institute for Biomedical Research, Yong-in 16924, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
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10
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Design of Knowledge Graph Retrieval System for Legal and Regulatory Framework of Multilevel Latent Semantic Indexing. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6781043. [PMID: 35909848 PMCID: PMC9325598 DOI: 10.1155/2022/6781043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/08/2022] [Accepted: 06/16/2022] [Indexed: 11/18/2022]
Abstract
Latent semantic analysis (LSA) is a natural language statistical model, which is considered as a method to acquire, generalize, and represent knowledge. Compared with other retrieval models based on concept dictionaries or concept networks, the retrieval model based on LSA has the advantages of strong computability and less human participation. LSA establishes a latent semantic space through truncated singular value decomposition. Words and documents in the latent semantic space are projected onto the dimension representing the latent concept, and then the semantic relationship between words can be extracted to present the semantic structure in natural language. This paper designs the system architecture of the public prosecutorial knowledge graph. Combining the graph data storage technology and the characteristics of the public domain ontology, a knowledge graph storage method is designed. By building a prototype system, the functions of knowledge management, knowledge query, and knowledge push are realized. A named entity recognition method based on bidirectional long-short-term memory (bi-LSTM) combined with conditional random field (CRF) is proposed. Bi-LSTM-CRF performs named entity recognition based on character-level features. CRF can use the transition matrix to further obtain the relationship between each position label, so that bi-LSTM-CRF not only retains the context information but also considers the influence between the current position and the previous position. The experimental results show that the LSTM-entity-context method proposed in this paper improves the representation ability of text semantics compared with other algorithms. However, this method only introduces relevant entity information to supplement the semantic representation of the text. The order in the case is often ignored, especially when it comes to the time series of the case characteristics, and the “order problem” may eventually affect the final prediction result. The knowledge graph of legal documents of theft cases based on ontology can be updated and maintained in real time. The knowledge graph can conceptualize, share, and perpetuate knowledge related to procuratorial organs and can also reasonably utilize and mine many useful experiences and knowledge to assist in decision-making.
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11
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Kondinski A, Menon A, Nurkowski D, Farazi F, Mosbach S, Akroyd J, Kraft M. Automated Rational Design of Metal-Organic Polyhedra. J Am Chem Soc 2022; 144:11713-11728. [PMID: 35731954 PMCID: PMC9264355 DOI: 10.1021/jacs.2c03402] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
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.
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Affiliation(s)
- Aleksandar Kondinski
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Angiras Menon
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Daniel Nurkowski
- CMCL
Innovations, Sheraton House, Castle Park, Cambridge CB3 0AX, U.K.
| | - Feroz Farazi
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Sebastian Mosbach
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Jethro Akroyd
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
| | - Markus Kraft
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
- CMCL
Innovations, Sheraton House, Castle Park, Cambridge CB3 0AX, U.K.
- CARES, Cambridge Centre for Advanced Research and Education
in Singapore, 1 Create
Way, CREATE Tower, #05-05, Singapore 138602
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459
- The
Alan Turing Institute, 2QR, John Dodson House, 96 Euston Road, London NW1 2DB, U.K.
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12
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Kondinski A, Rasmussen M, Mangelsen S, Pienack N, Simjanoski V, Näther C, Stares DL, Schalley CA, Bensch W. Composition-driven archetype dynamics in polyoxovanadates. Chem Sci 2022; 13:6397-6412. [PMID: 35733899 PMCID: PMC9159092 DOI: 10.1039/d2sc01004f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 04/29/2022] [Indexed: 12/13/2022] Open
Abstract
Molecular metal oxides often adopt common structural frameworks (i.e. archetypes), many of them boasting impressive structural robustness and stability. However, the ability to adapt and to undergo transformations between different structural archetypes is a desirable material design feature offering applicability in different environments. Using systems thinking approach that integrates synthetic, analytical and computational techniques, we explore the transformations governing the chemistry of polyoxovanadates (POVs) constructed of arsenate and vanadate building units. The water-soluble salt of the low nuclearity polyanion [V6As8O26]4− can be effectively used for the synthesis of the larger spherical (i.e. kegginoidal) mixed-valent [V12As8O40]4− precipitate, while the novel [V10As12O40]8− POVs having tubular cyclic structures are another, well soluble product. Surprisingly, in contrast to the common observation that high-nuclearity polyoxometalate (POM) clusters are fragmented to form smaller moieties in solution, the low nuclearity [V6As8O26]4− anion is in situ transformed into the higher nuclearity cluster anions. The obtained products support a conceptually new model that is outlined in this article and that describes a continuous evolution between spherical and cyclic POV assemblies. This new model represents a milestone on the way to rational and designable POV self-assemblies. Systems-based elucidation of the polyoxovanadate speciation reveals that heterogroup substitution can transform spherical kegginoids into tubular architectures in a programmable manner.![]()
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Affiliation(s)
- Aleksandar Kondinski
- Department of Chemical Engineering and Biotechnology, University of Cambridge Philippa Fawcett Drive S CB3 0AS UK
| | - Maren Rasmussen
- Institut für Anorganische Chemie, Christian-Albrechts-Universität zu Kiel 24118 Kiel Germany
| | - Sebastian Mangelsen
- Institut für Anorganische Chemie, Christian-Albrechts-Universität zu Kiel 24118 Kiel Germany
| | - Nicole Pienack
- Institut für Anorganische Chemie, Christian-Albrechts-Universität zu Kiel 24118 Kiel Germany
| | - Viktor Simjanoski
- Primer affiliate of University of Chicago Master Program Chicago IL USA
| | - Christian Näther
- Institut für Anorganische Chemie, Christian-Albrechts-Universität zu Kiel 24118 Kiel Germany
| | - Daniel L Stares
- Institut für Chemie und Biochemie der Freien Universität Berlin Arnimallee 20 14195 Berlin Germany
| | - Christoph A Schalley
- Institut für Chemie und Biochemie der Freien Universität Berlin Arnimallee 20 14195 Berlin Germany
| | - Wolfgang Bensch
- Institut für Anorganische Chemie, Christian-Albrechts-Universität zu Kiel 24118 Kiel Germany
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13
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Zhou X, Kraft M. Blockchain Technology in the Chemical Industry. Annu Rev Chem Biomol Eng 2022; 13:347-371. [PMID: 35363506 DOI: 10.1146/annurev-chembioeng-092120-022935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This article presents a review of the application of blockchain and blockchain-based smart contracts in the chemical and related industries. We introduce the basic concepts of blockchain and smart contracts and explain how some of their features are enabled. We review several typical or novel applications of blockchain and smart contract technologies and their enabling concepts and underlying technologies. We classify the selected literature into five categories and discuss their motivations and technical designs. We recognize that the trend of decentralization creates a need to use blockchain and smart contracts to implement trust and distributed control mechanisms. We also speculate on future applications of blockchain and smart contracts. We believe that, in the future, blockchains with different consensus mechanisms will be studied and applied to achieve more efficient and practical decentralized systems. Also, blockchain-based smart contracts will be more widely applied to enhance autonomous distributed controls in decentralized systems. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Xiaochi Zhou
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom;
| | - Markus Kraft
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom; .,Cambridge Centre for Advanced Research and Education in Singapore (CARES), Singapore.,School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore
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14
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Bai J, Cao L, Mosbach S, Akroyd J, Lapkin AA, Kraft M. From Platform to Knowledge Graph: Evolution of Laboratory Automation. JACS AU 2022; 2:292-309. [PMID: 35252980 PMCID: PMC8889618 DOI: 10.1021/jacsau.1c00438] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Indexed: 05/19/2023]
Abstract
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.
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Affiliation(s)
- Jiaru Bai
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Liwei Cao
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Sebastian Mosbach
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge
Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
| | - Jethro Akroyd
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge
Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
| | - Alexei A. Lapkin
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge
Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
| | - Markus Kraft
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge
Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459 Singapore
- The
Alan Turing Institute, London NW1 2DB, United Kingdom
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15
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Devanand A, Karmakar G, Krdzavac N, Farazi F, Lim MQ, Foo Eddy Y, Karimi IA, Kraft M. ElChemo: A cross-domain interoperability between chemical and electrical systems in a plant. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107556] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Kanza S, Willoughby C, Bird CL, Frey JG. eScience Infrastructures in Physical Chemistry. Annu Rev Phys Chem 2021; 73:97-116. [PMID: 34882434 DOI: 10.1146/annurev-physchem-082120-041521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
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.
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Affiliation(s)
- Samantha Kanza
- School of Chemistry, University of Southampton, Southampton, United Kingdom; , , ,
| | - Cerys Willoughby
- School of Chemistry, University of Southampton, Southampton, United Kingdom; , , ,
| | - Colin Leonard Bird
- School of Chemistry, University of Southampton, Southampton, United Kingdom; , , ,
| | - Jeremy Graham Frey
- School of Chemistry, University of Southampton, Southampton, United Kingdom; , , ,
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17
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Weber JM, Guo Z, Zhang C, Schweidtmann AM, Lapkin AA. Chemical data intelligence for sustainable chemistry. Chem Soc Rev 2021; 50:12013-12036. [PMID: 34520507 DOI: 10.1039/d1cs00477h] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This study highlights new opportunities for optimal reaction route selection from large chemical databases brought about by the rapid digitalisation of chemical data. The chemical industry requires a transformation towards more sustainable practices, eliminating its dependencies on fossil fuels and limiting its impact on the environment. However, identifying more sustainable process alternatives is, at present, a cumbersome, manual, iterative process, based on chemical intuition and modelling. We give a perspective on methods for automated discovery and assessment of competitive sustainable reaction routes based on renewable or waste feedstocks. Three key areas of transition are outlined and reviewed based on their state-of-the-art as well as bottlenecks: (i) data, (ii) evaluation metrics, and (iii) decision-making. We elucidate their synergies and interfaces since only together these areas can bring about the most benefit. The field of chemical data intelligence offers the opportunity to identify the inherently more sustainable reaction pathways and to identify opportunities for a circular chemical economy. Our review shows that at present the field of data brings about most bottlenecks, such as data completion and data linkage, but also offers the principal opportunity for advancement.
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Affiliation(s)
- Jana M Weber
- Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, UK. .,Chemical Data Intelligence (CDI) Pte Ltd, Robinson Road, #02-00, 068898, Singapore
| | - Zhen Guo
- Chemical Data Intelligence (CDI) Pte Ltd, Robinson Road, #02-00, 068898, Singapore.,Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, 138602, Singapore
| | - Chonghuan Zhang
- Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, UK.
| | - Artur M Schweidtmann
- Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
| | - Alexei A Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, UK. .,Chemical Data Intelligence (CDI) Pte Ltd, Robinson Road, #02-00, 068898, Singapore.,Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, 138602, Singapore
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18
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Dong J, Zhao M, Liu Y, Su Y, Zeng X. Deep learning in retrosynthesis planning: datasets, models and tools. Brief Bioinform 2021; 23:6375056. [PMID: 34571535 DOI: 10.1093/bib/bbab391] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/16/2021] [Accepted: 08/30/2021] [Indexed: 12/29/2022] Open
Abstract
In recent years, synthesizing drugs powered by artificial intelligence has brought great convenience to society. Since retrosynthetic analysis occupies an essential position in synthetic chemistry, it has received broad attention from researchers. In this review, we comprehensively summarize the development process of retrosynthesis in the context of deep learning. This review covers all aspects of retrosynthesis, including datasets, models and tools. Specifically, we report representative models from academia, in addition to a detailed description of the available and stable platforms in the industry. We also discuss the disadvantages of the existing models and provide potential future trends, so that more abecedarians will quickly understand and participate in the family of retrosynthesis planning.
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Affiliation(s)
- Jingxin Dong
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Hunan, China
| | - Mingyi Zhao
- Department of Pediatrics, Third Xiangya Hospital, Central South University, 400013, Hunan, China
| | - Yuansheng Liu
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Hunan, China
| | - Yansen Su
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, 230601, Hefei, China
| | - Xiangxiang Zeng
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Hunan, China
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19
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Zhou X, Nurkowski D, Mosbach S, Akroyd J, Kraft M. Question Answering System for Chemistry. J Chem Inf Model 2021; 61:3868-3880. [PMID: 34338504 DOI: 10.1021/acs.jcim.1c00275] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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.
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Affiliation(s)
- Xiaochi Zhou
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K
| | - Daniel Nurkowski
- CMCL Innovations, Sheraton House, Castle Park, Castle Street, Cambridge CB3 0AX, U.K
| | - Sebastian Mosbach
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K
| | - Jethro Akroyd
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K
| | - Markus Kraft
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.,School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459.,CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, Singapore 138602
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20
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Zheng X, Wang B, Zhao Y, Mao S, Tang Y. A knowledge graph method for hazardous chemical management: Ontology design and entity identification. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.10.095] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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21
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Mosbach S, Menon A, Farazi F, Krdzavac N, Zhou X, Akroyd J, Kraft M. Multiscale Cross-Domain Thermochemical Knowledge-Graph. J Chem Inf Model 2020; 60:6155-6166. [PMID: 33242243 DOI: 10.1021/acs.jcim.0c01145] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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 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.
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Affiliation(s)
- Sebastian Mosbach
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom.,Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, Singapore, 138602
| | - Angiras Menon
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Feroz Farazi
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Nenad Krdzavac
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, Singapore, 138602
| | - Xiaochi Zhou
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Jethro Akroyd
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom.,Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, Singapore, 138602
| | - Markus Kraft
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom.,Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, Singapore, 138602.,School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459
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22
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Kohse-Höinghaus K. Combustion in the future: The importance of chemistry. PROCEEDINGS OF THE COMBUSTION INSTITUTE. INTERNATIONAL SYMPOSIUM ON COMBUSTION 2020; 38:S1540-7489(20)30501-0. [PMID: 33013234 PMCID: PMC7518234 DOI: 10.1016/j.proci.2020.06.375] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 05/18/2020] [Accepted: 06/28/2020] [Indexed: 06/11/2023]
Abstract
Combustion involves chemical reactions that are often highly exothermic. Combustion systems utilize the energy of chemical compounds released during this reactive process for transportation, to generate electric power, or to provide heat for various applications. Chemistry and combustion are interlinked in several ways. The outcome of a combustion process in terms of its energy and material balance, regarding the delivery of useful work as well as the generation of harmful emissions, depends sensitively on the molecular nature of the respective fuel. The design of efficient, low-emission combustion processes in compliance with air quality and climate goals suggests a closer inspection of the molecular properties and reactions of conventional, bio-derived, and synthetic fuels. Information about flammability, reaction intensity, and potentially hazardous combustion by-products is important also for safety considerations. Moreover, some of the compounds that serve as fuels can assume important roles in chemical energy storage and conversion. Combustion processes can furthermore be used to synthesize materials with attractive properties. A systematic understanding of the combustion behavior thus demands chemical knowledge. Desirable information includes properties of the thermodynamic states before and after the combustion reactions and relevant details about the dynamic processes that occur during the reactive transformations from the fuel and oxidizer to the products under the given boundary conditions. Combustion systems can be described, tailored, and improved by taking chemical knowledge into account. Combining theory, experiment, model development, simulation, and a systematic analysis of uncertainties enables qualitative or even quantitative predictions for many combustion situations of practical relevance. This article can highlight only a few of the numerous investigations on chemical processes for combustion and combustion-related science and applications, with a main focus on gas-phase reaction systems. It attempts to provide a snapshot of recent progress and a guide to exciting opportunities that drive such research beyond fossil combustion.
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Key Words
- 2M2B, 2-methyl-2-butene
- AFM, atomic force microscopy
- ALS, Advanced Light Source
- APCI, atmospheric pressure chemical ionization
- ARAS, atomic resonance absorption spectroscopy
- ATcT, Active Thermochemical Tables
- BC, black carbon
- BEV, battery electric vehicle
- BTL, biomass-to-liquid
- Biofuels
- CA, crank angle
- CCS, carbon capture and storage
- CEAS, cavity-enhanced absorption spectroscopy
- CFD, computational fluid dynamics
- CI, compression ignition
- CRDS, cavity ring-down spectroscopy
- CTL, coal-to-liquid
- Combustion
- Combustion chemistry
- Combustion diagnostics
- Combustion kinetics
- Combustion modeling
- Combustion synthesis
- DBE, di-n-butyl ether
- DCN, derived cetane number
- DEE, diethyl ether
- DFT, density functional theory
- DFWM, degenerate four-wave mixing
- DMC, dimethyl carbonate
- DME, dimethyl ether
- DMM, dimethoxy methane
- DRIFTS, diffuse reflectance infrared Fourier transform spectroscopy
- EGR, exhaust gas recirculation
- EI, electron ionization
- Emissions
- Energy
- Energy conversion
- FC, fuel cell
- FCEV, fuel cell electric vehicle
- FRET, fluorescence resonance energy transfer
- FT, Fischer-Tropsch
- FTIR, Fourier-transform infrared
- Fuels
- GC, gas chromatography
- GHG, greenhouse gas
- GTL, gas-to-liquid
- GW, global warming
- HAB, height above the burner
- HACA, hydrogen abstraction acetylene addition
- HCCI, homogeneous charge compression ignition
- HFO, heavy fuel oil
- HRTEM, high-resolution transmission electron microscopy
- IC, internal combustion
- ICEV, internal combustion engine vehicle
- IE, ionization energy
- IPCC, Intergovernmental Panel on Climate Change
- IR, infrared
- JSR, jet-stirred reactor
- KDE, kernel density estimation
- KHP, ketohydroperoxide
- LCA, lifecycle analysis
- LH2, liquid hydrogen
- LIF, laser-induced fluorescence
- LIGS, laser-induced grating spectroscopy
- LII, laser-induced incandescence
- LNG, liquefied natural gas
- LOHC, liquid organic hydrogen carrier
- LT, low-temperature
- LTC, low-temperature combustion
- MBMS, molecular-beam MS
- MDO, marine diesel oil
- MS, mass spectrometry
- MTO, methanol-to-olefins
- MVK, methyl vinyl ketone
- NOx, nitrogen oxides
- NTC, negative temperature coefficient
- OME, oxymethylene ether
- OTMS, Orbitrap MS
- PACT, predictive automated computational thermochemistry
- PAH, polycyclic aromatic hydrocarbon
- PDF, probability density function
- PEM, polymer electrolyte membrane
- PEPICO, photoelectron photoion coincidence
- PES, photoelectron spectrum/spectra
- PFR, plug-flow reactor
- PI, photoionization
- PIE, photoionization efficiency
- PIV, particle imaging velocimetry
- PLIF, planar laser-induced fluorescence
- PM, particulate matter
- PM10 PM2,5, sampled fractions with sizes up to ∼10 and ∼2,5 µm
- PRF, primary reference fuel
- QCL, quantum cascade laser
- RCCI, reactivity-controlled compression ignition
- RCM, rapid compression machine
- REMPI, resonance-enhanced multi-photon ionization
- RMG, reaction mechanism generator
- RON, research octane number
- Reaction mechanisms
- SI, spark ignition
- SIMS, secondary ion mass spectrometry
- SNG, synthetic natural gas
- SNR, signal-to-noise ratio
- SOA, secondary organic aerosol
- SOEC, solid-oxide electrolysis cell
- SOFC, solid-oxide fuel cell
- SOx, sulfur oxides
- STM, scanning tunneling microscopy
- SVO, straight vegetable oil
- Synthetic fuels
- TDLAS, tunable diode laser absorption spectroscopy
- TOF-MS, time-of-flight MS
- TPES, threshold photoelectron spectrum/spectra
- TPRF, toluene primary reference fuel
- TSI, threshold sooting index
- TiRe-LII, time-resolved LII
- UFP, ultrafine particle
- VOC, volatile organic compound
- VUV, vacuum ultraviolet
- WLTP, Worldwide Harmonized Light Vehicle Test Procedure
- XAS, X-ray absorption spectroscopy
- YSI, yield sooting index
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23
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Affiliation(s)
- Markus Kraft
- CARES Cambridge Centre for Advanced Research and Education in Singapore 1 Create Way, CREATE Tower, #05-05 138602 Singapore Singapore
- University of Cambridge Department of Chemical Engineering and Biotechnology Philippa Fawcett Drive CB3 0AS Cambridge United Kingdom
- Nanyang Technological University School of Chemical and Biomedical Engineering 62 Nanyang Drive 637459 Singapore Singapore
| | - Andreas Eibeck
- CARES Cambridge Centre for Advanced Research and Education in Singapore 1 Create Way, CREATE Tower, #05-05 138602 Singapore Singapore
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