Theory

Though a full understanding of the theory behind our research is not essential for day-to-day work, knowing the math behind the techniques we use is important for your exams, helps when things go wrong, and is essential for understanding results. Here are some resources to understand each subject.

Note that PDF’s of some of these textbooks (and others) can be found in books within group_resources folder on Box.

DFT

Density Functional Theory: A Practical Introduction by Sholl (Ambar’s PhD advisor!) and Steckel. Chapters 1 and 3 are a good introduction. Chapters 5 and 6 are important as you get further into research.

Machine Learning

Learning from data - a short course Abu-Mostafa et al, 2012. PDF here

Deep Learning for Molecules & Materials by Andrew D White (ebook). A really nice ebook on ML for chemistry/materials science applications with lots of relevant examples/tutorials throughout.

For deeper learning, the classes STA 208, 142A/B or ECS289G are recommended.