Title:	Rational and data-driven computational research on modern molecular materials
DNr:	NAISS 2023/6-299
Project Type:	NAISS Medium Storage
Principal Investigator:	Hans Ågren <hagren@kth.se>
Affiliation:	Uppsala universitet
Duration:	2024-01-01 – 2025-01-01
Classification:	10407 10603 30105
Homepage:	https://www.katalog.uu.se/profile/?id=N18-41
Keywords:

Abstract

This comprehensive research initiative encompasses a spectrum of computational projects across diverse scientific domains. One pivotal project focuses on advancing the design and fabrication of highly-luminant blue light-emitting diodes (LEDs) for OLEDs, addressing current limitations in efficiency and color purity but also complexities of electroluminescence in radical-based OLEDs. In the realm of nanotechnology, the computational nanoplasmonics project explores applications spanning solar concentrators, therapy, imaging, and sensing. Employing the ExDIM model, the team aims to establish a QM/MM/ExDIM multiscale implementation. This is complemented by the development and optimization of parallelized sparse matrix algebra for enhanced computational efficiency. Shifting focus to environmental concerns, a pioneering project investigates the gas-liquid interface of water, especially its implications for cloud formation and climate. Utilizing liquid-jet X-ray photoelectron spectroscopy and multi-scale molecular simulations, the team seeks to elucidate fundamental mechanisms, bridging the gap between chemical substances and cloud formation. Catalyst development takes center stage in another initiative, where the team works on electrocatalysts for nitrate reduction, addressing global water pollution concerns. The project employs density functional theory to understand the catalytic performance of metal boron organic polymers. In the realm of materials science, the investigation into two-dimensional materials explores their electronic and optical properties. From chalcogenide-based semiconductors to perovskite-based quantum dots, this project utilizes various quantum-chemical codes to understand light-matter interactions. Machine learning plays a pivotal role in a project dedicated to the high-throughput theoretical exfoliation of 2D materials. The medical diagnostics arena sees active research in the development of positron emission tomography (PET) tracers for neurodegenerative diseases. This multidisciplinary effort combines in-silico modeling, chemical synthesis, and experimental validation to identify PET tracers targeting brain proteinopathies. In collaboration with experimentalists we build up, step by step, know-how and understanding which makes us better suited to tackle complicated systems and processes in biology, chemistry and in the life and materials sciences. We aim to interpret modelling results in terms of chemical structure, properties and dynamics, where we deal with real problems by using models that join the accuracy of quantum mechanics and the applicability of classical physics.