Machine learning for fluid property correlations: Classroom examples with MATLAB
File(s)
Accepted version
Supporting information
Author(s)
Joss, lisa
Muller, Erich
Type
Journal Article
Abstract
Recent advances in computer hardware and algorithms are spawning an explosive growth in the use of computer-based systems aimed at analyzing and ultimately correlating large amounts of experimental and synthetic data. As these machine learning tools become more widespread, it is becoming imperative that scientists and researchers become familiar with them, both in terms of understanding the tools and the current limitations of artificial intelligence, and more importantly being able to critically separate the hype from the real potential. This article presents a classroom exercise aimed at first-year science and engineering college students, where a task is set to produce a correlation to predict the normal boiling point of organic compounds from an unabridged data set of >6000 compounds. The exercise, which is fully documented in terms of the problem statement and the solution, guides the students to initially perform a linear correlation of the boiling point data with a plausible relevant variable (the molecular weight) and to further refine it using multivariate linear fitting employing a second descriptor (the acentric factor). Finally, the data are processed through an artificial neural network to eventually provide an engineering-quality correlation. The problem statements, data files for the development of the exercise, and solutions are provided within a MATLAB environment but are general in nature.
Date Issued
2019-04-09
Date Acceptance
2018-12-19
Citation
Journal of Chemical Education, 2019, 96 (4), pp.697-703
ISSN
0021-9584
Publisher
American Chemical Society
Start Page
697
End Page
703
Journal / Book Title
Journal of Chemical Education
Volume
96
Issue
4
Copyright Statement
© 2019 American Chemical Society and Division of Chemical Education, Inc.
Subjects
Science & Technology
Social Sciences
Physical Sciences
Chemistry, Multidisciplinary
Education, Scientific Disciplines
Chemistry
Education & Educational Research
First-Year Undergraduate/General
Second-Year Undergraduate
Chemical Engineering
Computer-Based Learning
Mathematics/Symbolic Mathematics
Molecular Properties/Structure
Physical Properties
NEURAL-NETWORK
PREDICTION
03 Chemical Sciences
13 Education
Education
Publication Status
Published
Date Publish Online
2019-01-08