Energy Materials
Energy materials are the key ingredients in devices for storing energy or converting it into other forms. Examples of these devices are solar cells and batteries. The goal of our research is to understand how energy materials work and exploit that knowledge for predicting new compounds. We are fascinated to understand how physical effects on the microscopic level, active at tiny scales, impact the macroscopic appearances of technological materials. We use and develop an array of theoretical and computational methods to calculate the properties of energy materials on supercomputers. We also intensively collaborate with several experimental groups that fabricate and characterize these material in their labs. A focus of our most recent efforts in this area are the atomic dynamics occurring at finite temperatures.
A snapshot of our recent work in this context:
- Lone-pair dynamics in halide perovskites
Nature Comm. 15, 4184 (2024) - Critical role of anharmonic lattice dynamics in nitride semiconductors
Adv. Energy Mater. 14, 2303059 (2024) - Control functional properties via cation disorder in nitride semiconductors
Adv. Energy Mater. 14, 2402540 (2024) - Anharmonic Fluctuations Govern the Band Gap of Halide Perovskites
Phys. Rev. Mater. 7, L092401 (2023) - Anharmonic Lattice Dynamics in Sodium Ion Conductors
J. Phys. Chem. Lett. 13, 5938 (2022) - Transversal Halide Motion Intensifies Band-To-Band Transitions in Halide Perovskites
Adv. Sci. 9, 2200706 (2022) - A Single Atom Change Turns Insulating Saturated Wires into Molecular Conductors
Nat. Commun. 12, 3432 (2021) - Anharmonic Host Lattice Dynamics Enable Fast Ion Conduction in Superionic AgI
Phys. Rev. Mater. 4, 115402 (2020) - Broad Tunability of Carrier Effective Masses in Two-Dimensional Halide Perovskites
ACS Energy Lett. 5, 3609 (2020)
New Theoretical and Computational Methods
Next to theory and experiment, computational sciences arguably have become the third pillar in the natural sciences. In computational materials physics, we aim to predict the properties of technologically relevant compounds with minimal empirical input. Our group develops new methods in this context, with a particular focus on the finite-temperature properties of energy materials. We believe that some of the most relevant physical properties of these systems cannot be rationalized from consideration of their low-temperature characteristics alone. Therefore, we propose new frameworks that take into account the full extent of the pertinent atomic dynamics in materials when predicting their properties. Examples for where we found these atomic fluctuations to be crucial are the charge transport in semiconductors or the ionic conductivity of solid-state ion conductors. Together with our theory collaborators, we are unifying molecular dynamics, low-cost electronic-structure theory, quantum dynamics and machine learning into new methods that can bring us closer to the physical reality of materials.
A snapshot of our recent work in this context:
- Overdamped phonons in perovskites
Phys. Rev. Lett. 134, 016403 (2025) - ML-accelerated predictions of defects via Raman fingerprints
J. Am. Chem. Soc. 146, 26863 (2024) - ML-based analysis of structural dynamics in solid-state electrolytes
J. Mater. Chem. A 12, 11344 (2024) - Temperature-transferable model for large-scale electronic-structure calculations of semiconductors
J. Chem. Phys. 160, 134102(2024) - Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra
J. Phys. Chem. C 128, 6464(2024) - Correlated Anharmonicity and Dynamic Disorder Control Carrier Transport in Halide Perovskites
Phys. Rev. Mater. 7, L081601 (2023) - Accurate Non-Adiabatic Couplings from Optimally-Tuned Range-Separated Hybrid Functionals
J. Chem. Phys. 157, 101104 (2022) - The Significance of Polarons and Dynamic Disorder in Halide Perovskites
ACS Energy Lett. 6, 2162 (2021) - Accurate Molecular Geometries in Complex Excited-State Potential Energy Surfaces from Time-Dependent Density Functional Theory
J. Chem. Theory Comput. 17, 357 (2021) - Assessing the accuracy of screened range-separated hybrids for bulk properties of semiconductors
Phys. Rev. Materials 5, 034602 (2021)
Theory of Functional Energy Materials (TheoFEM) 