Modeling of Functional Materials
Title: Modeling of Functional Materials
DNr: NAISS 2024/5-19
Project Type: NAISS Medium Compute
Principal Investigator: Paul Erhart <>
Affiliation: Chalmers tekniska högskola
Duration: 2024-02-01 – 2025-02-01
Classification: 10304


1. Metallic nanostructures Metallic nanostructures are of interest for, e.g., sensing, catalysis, and energy conversion. We therefore study the thermodynamic, optical, and electronic properties of these materials using density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations, machine learning (ML) models, atomic scale simulations as well as, to a lesser extent, electrodynamic simulations. 1.1. Light-matter interaction and strong coupling. The ability to manipulate chemistry and physical properties through light-matter coupling via optical cavities is a rapidly expanding research area. We will focus on the manipulation of chemical reactions through strong coupling (Marie-Skłodowska-Curie grant) and the generation of so-called hot carriers through cavity coupling (KAW grant). 1.2. Hydrogen sensing. The ability to rapidly and reliably detect hydrogen leaks is fundamental for H2 technologies, which are an important part of the sustainable energy economy. We will continue the development of large AI/ML models to not only accelerate H2 sensing but dramatically improve tolerance to poisoning agents and extend their functionality to other sensing modalities (Vinnova grant). In addition, we will build ML models for atomic scale systems using the neuroevolution potential (NEP) framework and use these models to study phase separation, surface segregation as well as hydrogen sorption and desorption (AoA Nano; VR). 2. Dynamics of strongly anharmonic materials Understanding and manipulating the dynamic and thermal transport properties of materials is of interest in the context of, e.g., electronic and optoelectronic devices, thermal management, thermoelectric energy generation and materials for quantum computing. In this area, we use DFT calculations, ML models for atomistic systems as well as atomic scale simulations. To deal with the large number of degrees of freedom and the chemical complexity of these systems we are continuously developing our methods and software packages. 2.1 Halide perovskites Halide perovskites are interesting for advanced optoelectronic applications, such as solar cells and lighting. The possibility to improve stability and performance of these materials through mixing, defect, and surface/interface engineering is currently receiving enormous attention. In the coming year, we will therefore focus on constructing and sampling models for many-component systems with up to 8 different elements (VR grants). This is possible thanks to the dramatic progress that we have been able to achieve thanks to the construction of ML models via the NEP framework. 2.2 Molecular materials Molecular materials including liquid chromophores and molecular solar storage-thermal materials exhibit a rich dynamic behavior due to the large variety of different bonding types and configurations, which are strongly coupled to their optoelectronic properties. In the coming year, we will continue our studies of the dynamic properties of these systems, in particular in the context of neutron spectroscopy (SwedNess graduate school; WASP-WISE), as well as the coupling between optical response and dynamics in molecular solar-thermal storage materials (WISE grant). To this end, we will combine DFT (and beyond) calculations as well as scalar and tensorial NEP models capitalizing on our recently developed TNEP approach.