Open ARL and Master Thesis Positions
Hybrid perovskite: Structural prototypes and DFT
Research Group Theory of Magnetic Materials (TMM)
2025/04/01
Advanced Research Lab, Master Thesis
Hybrid perovskites have attracted considerable interest recently for their exceptional optoelectronic properties and potential applications in photovoltaics, light-emitting devices, and photodetectors, making them highly promising for next-generation technologies. In this project, AiiDA workflow manager and VASP will be used to conduct high-throughput calculations to identify stable and high-performance two-dimensional (2D) perovskites.
This research focuses on the generation of 2D perovskite structures across different structural prototypes, including Ruddlesden-Popper (RP) and Dion-Jacobson (DJ) phases. The primary objective of this project is to investigate physical properties, such as shift current, in screened perovskite candidates. Consequently, this project necessitates a strong motivation to work with various tools, including AiiDA, VASP, Wannier90, and Python.
Please write to Prof. Hongbin Zhang (Email: hzhang@tmm.tu-darmstadt.de) if you are interested.
Supervisor: Prof. Dr. Hongbin Zhang
Implementation of Multi-Fidelity Bayesian Optimization with Consideration of Costs
Research Group Theory of Magnetic Materials (TMM)
2025/04/01
Advanced Research Lab, Master Thesis
Optimizing the performance and parameters of complex systems is a challenging task in modern engineering and scientific research, especially when data acquisition is costly or of variable quality. Multi-fidelity Bayesian optimization (MFBO) is capable of integrating data with different fidelity (i.e., varying accuracy and cost) to improve the efficiency and reduce the total cost of the optimization process.
The aim of this study is to implement a multi-fidelity Bayesian optimization framework that takes into account the cost of data to improve the optimization performance under resource constrained conditions.
Supervisor: Prof. Dr. Hongbin Zhang
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Generative diffusion model for crystal structures
Research Group Theory of Magnetic Materials (TMM)
2025/04/01
Advanced Research Lab, Master Thesis
In this project, we are going to focus on establishing a methodology to leverage advanced machine learning techniques, particularly deep generative models, to predict novel inorganic material compositions. The model will be trained on a large dataset of computational inorganic materials, learning the underlying distribution and optimizing the relevant physical properties in the properly constructed latent space. Once trained, the model can propose new, valid inorganic compositions that are likely to exhibit desirable properties.
Expertise will be gained in the generative diffusion model as specified in https://arxiv.org/abs/2312.03687, and coding with Python valuable for both future PhD studies and industrial positions.
Supervisor: Prof. Dr. Hongbin Zhang
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High throughput Design of Fe2P-type Magneto-caloric Materials
Research Group Theory of Magnetic Materials (TMM)
2021/03/30
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High throughput Screening of Magnetic Ground States
Research Group Theory of Magnetic Materials (TMM)
2021/03/30
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Topological Phenomena in Kagome Magnets
Research Group Theory of Magnetic Materials (TMM)
2021/03/30
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Designing permanent magnets by interstitial and substitutional doping
Research Group Theory of Magnetic Materials (TMM)
2021/02/02
Advanced Research Lab, Master Thesis
In this project massive density functional theory calculations should be carried out to evaluate the thermal conductivities for both 2D and 3D insulators with large band gaps. Particular focus should hereby be on those cases with tunable structural phase transitions.
If time allows there will also be explorative calculations to get the interfacial thermal resistance.
Supervisor: Prof. Dr. Hongbin Zhang
High throughput design of 2D functional van der Waals Materials
Research Group Theory of Magnetic Materials (TMM)
2020/09/02
High throughput screening for 3D and 2D spin-gapless semiconductors
Research Group Theory of Magnetic Materials (TMM)
2020/07/02
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