Rational and Data-driven Computational Research on Modern Materials
Abstract
The NAISS Plan 2026 outlines several key projects aimed at advancing computational research on modern materials through cutting-edge simulations and experimental collaborations.
The plan includes a methodological development project focusing on computational nanoplasmonics, where our discrete interaction model for plasmonics of ultrafine particles has shown great performance in a number of applications with potential to aid the design of new solar concentrators, photodetectors, and nanophotonic devices. Further development of this model will focus on applications of our recently developed quantum mechanics plasmonic mechanics interface which allows the study of single molecular properties triggered by ultra strong enhanced plamonic fields.
Another highlight is the modeling and defect engineering of 2D MXene materials. MXenes, with their unique combination of properties like high conductivity and mechanical strength, are poised for various applications, including electromagnetic shielding, energy storage and photodetectors. This project uses high-throughput computational methods to analyze defect-related properties and to enhance the performance of MXenes. By integrating machine learning and Monte Carlo simulations, the project will aim to identify new materials with tailored defect properties for practical use in coatings, electronics, and photonics. Perovskite materials are the objects for a following-up project with the aim to improve efficiency of perovskite-based solar cells and quantum light emitting diodes. A multiscale computational framework is being devoped for this purpose that integrates DFT, ML and advanced MD simulation techniques.
Another notable project deals with efficient charge separation in catalytic systems, particularly through the use of piezoelectric materials. These materials generate polarization fields when subjected to stress, improving the separation of photogenerated carriers. This project has earlier focused on layered perovskites and their capability for enhancing photocatalytic processes in environmental applications like pollutant decomposition and clean energy production. The use of VASP and COMSOL codes will enable precise calculations of band structures, piezoelectric effects, and material properties under stress.
In the area of bioimaging, the development of long-persistent afterglow materials is another focal point. These materials, which exhibit room-temperature phosphorescence, are being investigated also for their potential in sensors and security printing. In the biomedical scene we further develop our project on in-silico modelling of positron emission tomography (PET) tracers for diagnosing neurodegenerative diseases like Alzheimer's and Parkinson's. The focus is on PET tracers that can effectively target pathological proteins, including tau and α-synuclein fibrils. The project uses molecular dynamics simulations, machine learning models, and structure-based simulations to investigate the binding modes of tracers and optimize their design for early diagnosis.
Finally, we introduce a new area into our NAISS project toolbox – namely attosecond physics which deals with electron motion at the attosecond timescale. This is motivated partly by Sweden’s Nobel Prize in Physics last year for attosecond lasing, but also by the recently granted Kunt and Alic Wallenberg consortium contribution for a team of researchers from several Swedish universities which aim to push the frontiers of attosecond physics. Our Uppsala group has the responsibility for theory and computational development within this consortium.
