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The future of chemistry will increasingly involve more AI and specifically standard machine learning methods. Undergraduate chemistry majors need to gain some understanding of what machine learning is and how it is used in chemistry. This Foundations Course serves as an introductory-level, hands-on guide for the next generation of chemists, integrating foundational machine learning and AI concepts directly into the general chemistry curriculum. Through a suite of custom-built, interactive applications and carefully designed experiments, this text demystifies the "black box" of modern AI.
Students will move beyond theory to actively build, train, and critique models, all while developing a robust portfolio of Python code and ethical reflections. From the fundamentals of data pre-processing and model evaluation to the advanced applications of neural networks and computer vision, this curriculum prepares students to be responsible, effective, and AI-augmented scientists, ready to tackle the challenges of a data-driven world.
Note this page is under construction as are the book and software suit. Check back after Sept 1 for the first complete version.
Installation Software
Installation Software
ChemLeanChem1_Installer.exe
ChemLeanChem1_Installer.exe
Mac Installer (Coming soon)
Linux Installer (Flatpack) (Coming soon)
DATA and MODELS
DATA and MODELS
TextBook
TextBook
D. J. Ulness, P.S. Mork, J.R. Mach Foundations of Machine Learning for Chemistry.
Free PDF file Note this is just a draft (The August 18 draft). Final version by Sept 1.
Free e-book (coming soon)
Softcover (available from Lulu.com for $25)
Course Webpage
Course Webpage
For Concordia students we offer both synchronous and aysnchronous options. For those of you across the world, the course materials are free for you to use. Note that no acreditation is give by Concordia without officially enrolling at the College. Click here to go to the Course Webpage.
You can find video lectures on the course page as well as YouTube by clicking here. (Videos not yet ready)
Resources
Resources
More to come. This will include outside links to youtube videos, etc.
About the Authors
About the Authors
Professor of physical chemistry at Concordia College in Moorhead, Minnesota, where he has taught since 1998. His research interests include nonlinear optical spectroscopy, mathematical physics, and the dynamics of liquids. He is passionate about helping students develop both a curiosity about nature and the confidence to apply their knowledge in the evolving field of chemistry.
Associate Professor of organic chemistry of 33 years at Concordia College in Moorhead, Minnesota. She is a passionate advocate for her students. Students report they leave her classes feeling like a scientist. She authored an honors-level high school chemistry textbook. She loves incorporating new technology in her classes and is committed to equiping chemistry majors for the AI-driven future.
Associate Professor of Biochemistry and Chair of the Chemistry Department at Concordia College in Moorhead, Minnesota, where she has taught since 2001. An enthusiastic early adopter of educational technology, she has a strong sense for how to guide students in using these tools to enhance their learning. She is passionate about fostering independent learning and mentoring students through course-based undergraduate research experiences in biochemistry. She has an interest in molecular modeling and protein structure prediction, using computational tools to explore and advance biochemical research.
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