My master’s thesis explored how machine learning can optimize Gaussian basis sets for ab initio density-functional theory (DFT) calculations, with particular attention to compact basis families and accuracy trade-offs.
- Introduced a projection-based loss that aligns an optimisable Gaussian basis to a larger reference set, enabling joint optimisation of exponents and contractions with gradient-based methods via automatic differentiation.
- Evaluated the learned bases across diverse small molecules, showing consistent energy-error reductions for minimal and split-valence sets, especially STO-nG, while gains over modern polarised/augmented references remained modest.
The study highlights both the promise and the current limitations of ML-driven basis-set optimisation, motivating richer datasets and hybrid strategies.