Inverse design of quantum materials with generative AI

In this master project, you will develop and apply physics-informed data, representations, and methods for inverse design models based on generative AI, enabling the property-conditioned generation of quantum systems. Development will focus on probabilistic variational autoencoders and diffusion models. Applications will focus on the inverse design and data-driven discovery of quantum materials (e.g., single-molecule quantum magnets).

Prerequisite knowledge:

FYS3110 – Quantum Mechanics

KJM3650 – Computations for quantum chemistry

Recommended courses to take during first semesters of master programme:

FYS-STK4155 – Applied Data Analysis and Machine Learning