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Raoyu Qiu; Zequn Lin; Zican Yang; Liang Gao – Journal of Chemical Education, 2024
Machine learning (ML) is extensively applied in chemistry, particularly in vibrational spectroscopy. However, few teaching examples effectively demonstrate the capabilities of ML in classifying polymeric materials, exhibiting subtle spectral differences that elude visual discrimination. This study presents a teaching example specifically tailored…
Descriptors: Artificial Intelligence, Classification, Undergraduate Study, Chemistry
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Shin-Yu Kim; Inseong Jeon; Seong-Joo Kang – Journal of Chemical Education, 2024
Artificial intelligence (AI) and data science (DS) are receiving a lot of attention in various fields. In the educational field, the need for education utilizing AI and DS is also being emerged. In this context, we have created an AI/DS integrating program that generates a compound classification/regression model using characteristics of compounds…
Descriptors: Chemistry, Science Instruction, Laboratory Experiments, Artificial Intelligence
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Alanah Grant St. James; Luke Hand; Thomas Mills; Liwen Song; Annabel S. J. Brunt; Patrick E. Bergstrom Mann; Andrew F. Worrall; Malcolm I. Stewart; Claire Vallance – Journal of Chemical Education, 2023
Applications of machine learning in chemistry are many and varied, from prediction of structure-property relationships, to modeling of potential energy surfaces for large scale atomistic simulations. We describe a generalized approach for the application of machine learning to the classification of spectra which can be used as the basis for a wide…
Descriptors: Artificial Intelligence, Chemistry, Science Instruction, Classification
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Lafuente, Deborah; Cohen, Brenda; Fiorini, Guillermo; Garci´a, Agusti´n Alejo; Bringas, Mauro; Morzan, Ezequiel; Onna, Diego – Journal of Chemical Education, 2021
Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks…
Descriptors: Undergraduate Students, Chemistry, Electronic Learning, Artificial Intelligence
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Noyes, Keenan; McKay, Robert L.; Neumann, Matthew; Haudek, Kevin C.; Cooper, Melanie M. – Journal of Chemical Education, 2020
Computer-assisted analysis of students' written responses to questions is becoming a possibility due to developments in technology. This could make such constructed response questions more feasible for use in large classrooms where multiple choice assessments are often considered a more practical option. In this study, we use a previously…
Descriptors: Automation, Artificial Intelligence, Computer Uses in Education, Classification
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Thrall, Elizabeth S.; Lee, Seung Eun; Schrier, Joshua; Zhao, Yijun – Journal of Chemical Education, 2021
Techniques from the branch of artificial intelligence known as machine learning (ML) have been applied to a wide range of problems in chemistry. Nonetheless, there are very few examples of pedagogical activities to introduce ML to chemistry students in the chemistry education literature. Here we report a computational activity that introduces…
Descriptors: Undergraduate Students, Artificial Intelligence, Man Machine Systems, Science Education
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Baosen Zhang; Ariana Frkonja-Kuczin; Zhong-Hui Duan; Aliaksei Boika – Journal of Chemical Education, 2023
Computer vision (CV) is a subfield of artificial intelligence (AI) that trains computers to understand our visual world based on digital images. There are many successful applications of CV including face and hand gesture detection, weather recording, smart farming, and self-driving cars. Recent advances in computer vision with machine learning…
Descriptors: Classification, Laboratory Equipment, Visual Aids, Optics
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Lemes, Mauricio R.; Dal Pino, Arnaldo – Journal of Chemical Education, 2011
Although several chemical elements were not known by end of the 19th century, Mendeleev came up with an astonishing achievement, the periodic table of elements. He was not only able to predict the existence of (then) new elements, but also to provide accurate estimates of their chemical and physical properties. This is a profound example of the…
Descriptors: Chemistry, Classification, Artificial Intelligence, Networks