Title:	Diffusion models for PDEs
DNr:	Berzelius-2025-316
Project Type:	LiU Berzelius
Principal Investigator:	Zheng Zhao <zheng.zhao@liu.se>
Affiliation:	Linköpings universitet
Duration:	2025-09-30 – 2026-04-01
Classification:	10201
Homepage:	https://wise-materials.org/project/accelerating-chemical-vapour-deposition-discovery-and-development-with-generative-machine-learning/
Keywords:

Abstract

The project aims to use diffusion models for solving PDEs and apply to chemical vapour deposition discovery and development. Thin layers, or films, of different materials play a vital role in everyday life. They keep food fresh, make cutting tools more durable, and form the foundation of all electronic devices. One of the main ways to create these films is chemical vapour deposition (CVD), which uses reactive gases carrying the atoms needed for the film. Despite CVD’s technological importance, developing new processes still relies heavily on trial and error and in-house expertise, because current modelling methods are slow and resource-intensive. In this project, we aim to combine CVD modelling with generative machine learning to achieve a deeper, more reliable understanding of CVD and to speed up the sustainable discovery of new processes.