Positions for Research Assistants or Associates
Last changes 2.04.2025
We currently have an open PhD position within the Framework of SusMatEner is a Marie Curie Doctoral Network funded by the HORIZON EUROPE programme (Deadline: 30.04.2025):
PhD9 – On-the-fly machine learning for electrochemical conversion and storage.
More information is available here.
Open ARL/ Master Thesis Positions
We are looking for students to do a research project (advanced research lab or master thesis) within our group in the following areas listed below.
If you are interested, please contact Prof. Dr. Hongbin Zhang to discuss possibilities.
IMPORTANT for all positions: we use python, so please get yourself prepared for it, at least spiritually.
Automatic Rietveld refinement
Inverse design of meta-materials
DFT: Tailoring 2D materials via intercalations
Reinforcement learning for HEA
DFT: HEA for spintronic applications
DFT: photo-driven thermal switches
Kalman filter for ARPES
DFT: Spin reorientations in (Nd,Pr)2(Fe, Co, Cu)14B
DFT+ML: Multi-fidelity Curie temperature of Heusler alloys (DFT + exp)
DFT: transparent conductors, gapped metals
NOMAD solar cells
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.
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. We will first assess the impact of each level of data on the optimization objective by constructing a hierarchical model of fidelity containing different data costs. Then, a suitable surrogate model is selected to integrate these data with different fidelity levels, and a Bayesian optimization algorithm is developed for global optimization.
Candidates are expected to have a good foundation in mathematics, be familiar with the Python programming language, and have at least a basic understanding of machine learning and materials science.
Understanding the local coordination environment around atomic species is crucial for unraveling the mechanisms of operation in various functional materials. Extended X-ray Absorption Fine Structure (EXAFS) has emerged as a powerful tool in this domain, providing exceptional elemental specificity and spatiotemporal resolution. By focusing on the interference patterns generated by photoelectron waves, EXAFS enables researchers to delve deep into the atomic and electronic structure of materials.
Recent advancements in machine learning have opened up new possibilities for mapping between EXAFS spectra and pair distribution functions (PDF), offering more precise insights into atomic structures. The combination of machine learning and EXAFS is especially promising for complex systems with multimodal bond distributions, such as catalysts, ionic liquids, and nanomaterials. The use of DFT-based FEFF codes, a widely used software for calculating EXAFS spectra, provides a robust framework for these studies.
This project will primarily focus on testing and benchmarking the EXAFS calculation workflow using FEFF, while also exploring the forward mapping and the inverse design of local structures based on EXAFS data.
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.
