I’m pleased to announce that our book chapter, Chapter 22. Data-driven acceleration of coupled-cluster and perturbation theory methods by Grier M. Jones, P. D. Varuna S. Pathirage, Konstantinos D. Vogiatzis in Quantum Chemistry in the Age of Machine Learning, published by Elsevier, is finally in press! In our book chapter, we discuss the data-driven quantum chemistry (DDQC) schemes developed within our group, including case studies of the data-driven coupled-cluster singles and doubles (DDCCSD) and data-driven complete active space second-order perturbation theory (DDCASPT2) methods. Writing the book chapter provided me with excellent professional development opportunities, including creating a website and GitHub repository (linked below) for our DDQC methods. Even if you do not have access to the book chapter, feel free to checkout the code examples developed by Varuna (DDCCSD) and me (DDCASPT2)!
Links:
- Book: Quantum Chemistry in the Age of Machine Learning
- Book companion: https://www.elsevier.com/books-and-journals/book-companion/9780323900492?SQ_VARIATION_1295026=0#anchor-22
- Website with code examples: https://chemracer.github.io/DDQC_Demo/
- GitHub repository: https://github.com/ChemRacer/DDQC_Demo
Grier Jones 