The<i>CSD</i>and knowledge databases: from answers to questions

Шевченко, А. П.; Eremin, Roman A.; Блатов, В. А.

doi:10.1039/d0ce00265h

Cited by 20 publications

(24 citation statements)

References 58 publications

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“…Third, we found the approach presented by Shanmugalingam, Chandrasekara [29] very interesting, where the authors of the paper created a ticket classification tool, in which the determined class information are transmitted to a solution bot in order to generate a STS that automatically answers customer requests. As already said, there is a lot of research going on pertaining chatbots and question-answersystems are also a ML field that is researched in Jiang, Su [47] and Shevchenko, Eremin [48]. Surprisingly, most papers analyzed during this Literature Review do not use the class information received from their incident management tools to automate ticket answering.…”

Section: Discussionmentioning

confidence: 99%

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Fuchs¹,

Drieschner²,

Wittges³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field.

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Section: Discussionmentioning

confidence: 99%

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Fuchs¹,

Drieschner²,

Wittges³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…With its more than one million entries (102 508 of which were recognized as MOFs until May 2020), 9 the CSD is the oldest and largest data set of organic and metal–organic crystals. 30 …”

Section: Databases Of Nanoporous Materialsmentioning

confidence: 99%

“…In the MOF community, it is standard practice to publish new structures in the Cambridge Structural Database (CSD) and to report the reference code assigned to the entry in the article. With its more than one million entries (102 508 of which were recognized as MOFs until May 2020), the CSD is the oldest and largest data set of organic and metal–organic crystals …”

Section: Databases Of Nanoporous Materialsmentioning

confidence: 99%

“…With its more than one million entries (102 508 of which were recognized as MOFs until May 2020), 9 the CSD is the oldest and largest data set of organic and metal−organic crystals. 30 Unfortunately, many of the reported structures in the CSD are not suitable for a computational screening study out of the box. From X-ray diffraction data, it is difficult to locate hydrogen.…”

Section: ■ Introductionmentioning

confidence: 99%

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Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution

2020

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Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials’ performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications.

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“…The approaches of the second group can be called crystallochemical because they rest upon the experimental crystallographic information and the chemical composition of the structures under consideration. In this case, the design follows some rules, which are derived from the chemical properties of the structural building units (atoms, molecules, clusters, and complex groups) or from a big-data analysis of the experimental information by machine learning methods or correlation analysis . The crystallochemical approaches were mainly used for the design of coordination compounds and metal–organic frameworks, where the connectivity of the structural components is well-determined by their chemical properties.…”

mentioning

confidence: 99%

From Simple to Complex: Design of Inorganic Crystal Structures with a Topologically Extended Zintl–Klemm Concept

Medrish

Eremin

Блатов

2020

J. Phys. Chem. Lett.

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We present a novel approach to the generation of new crystalline phases, which is based on a combination of the topological description of crystal structures as a periodic net and the extended Zintl–Klemm concept, which establishes the structural relations between chemically and structurally simpler and more complex inorganic compounds. With this approach, we have explored the structural similarities between all known binary sulfides, selenides, and the corresponding simple sulfates and selenates and have theoretically revealed seven new high-pressure phases in the last two groups of compounds. Using density functional theory methods, we have studied the thermodynamic and mechanical stability of the new phases, have adjusted the transition pathways in the sulfate and selenate systems, and have revealed new structural correlations of the baric polymorphism in these systems. The advantages of the topological approach compared to conventional methods of modeling crystal structures are discussed and illustrated.

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TheCSDand knowledge databases: from answers to questions

Abstract: We develop tools for extracting new information on crystal structures from crystallographic databases and show how to use these tools in the design of coordination compounds.

Cited by 20 publications

References 58 publications

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution

From Simple to Complex: Design of Inorganic Crystal Structures with a Topologically Extended Zintl–Klemm Concept

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