arXiv subjects

Frederik Vonhoff

Publications and source records attributed to Frederik Vonhoff.

4 recordsLinked to original sources

arXiv2026

Hamiltonian learning reveals optoelectronic mechanisms across thermodynamic state space in soft semiconductors

Predicting optoelectronic response across thermodynamic state space requires coupling finite-temperature nuclear dynamics to electronic structure at scales where direct first-principles calculations are impractical. Machine-learning force fields and Hamiltonian-learning models provide scalable predictions, but integrating them into reliable and interpretable workflows remains challenging. Here, we introduce FLOW-OTTER, a modular, model-agnostic framework that automates molecular dynamics, Hamiltonian prediction, observable extraction, reliability assessment, and Hamiltonian-level interpretation. We demonstrate FLOW-OTTER in halide perovskites, soft semiconductors whose anharmonic fluctuations strongly modulate electronic response. Using FLOW-OTTER, we show that independently learned nuclear and electronic models remain predictive when composed end-to-end, reproducing experimental temperature- and pressure-dependent band-gap trends using models trained only on first-principles targets at zero pressure. By resolving the nonlinear evolution of Pb-$s$/Br-$p$ antibonding at the valence-band maximum, it identifies the microscopic origin of the asymmetric pressure response. FLOW-OTTER thus establishes Hamiltonian learning as a general route from thermodynamic trajectories to experimentally grounded optoelectronic mechanisms.

cond-mat.mtrl-sci↗

arXiv2026

Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction

Predicting optoelectronic properties of large-scale atomistic systems under realistic conditions is crucial for rational materials design, yet computationally prohibitive with first-principles simulations. Recent neural network models have shown promise in overcoming these challenges, but typically require large datasets and lack physical interpretability. Physics-inspired approximate models offer greater data efficiency and intuitive understanding, but often sacrifice accuracy and transferability. Here we present HAMSTER, a physics-informed machine learning framework for predicting the quantum-mechanical Hamiltonian of complex chemical systems. Starting from an approximate model encoding essential physical effects, HAMSTER captures the critical influence of dynamic environments on Hamiltonians using only few explicit first-principles calculations. We demonstrate our approach on halide perovskites, achieving accurate prediction of optoelectronic properties across temperature and compositional variations, and scalability to systems containing tens of thousands of atoms. This work highlights the power of physics-informed Hamiltonian learning for accurate and interpretable optoelectronic property prediction in large, complex systems.

cond-mat.mtrl-sci↗

arXiv2025

Analysis of real-space transport channels for electrons and holes in halide perovskites

Predicting and explaining charge carrier transport in halide perovskites is a formidable challenge because of the unusual vibrational and electron-phonon coupling properties of these materials. This study explores charge carrier transport in two prototypical halide perovskite materials, MAPbBr$_3$ and MAPbI$_3$, using a dynamic disorder model. Focusing on the role of real-space transport channels, we analyze temporal orbital occupations to assess the impact of material-specific on-site energy levels and spin-orbit coupling (SOC) strengths. Our findings reveal that both on-site energies and SOC magnitude significantly influence the orbital occupation dynamics, thereby affecting charge dispersal and carrier mobility. In particular, energy gaps across on-site levels and the halide SOC strength govern the filling of transport channels over time. This leads us to identify the $ppπ$ channel as a critical bottleneck for charge transport and to provide insights into the differences between electron and hole transport across the two materials.

cond-mat.mtrl-sci↗

arXiv2022

Microscopic origin of the effective spin-spin interaction in a semiconductor quantum dot ensemble

We present a microscopic model for a singly charged quantum dot (QD) ensemble to reveal the origin of the long-range effective interaction between the electron spins in the QDs. Wilson's numerical renormalization group (NRG) is used to calculate the magnitude and the spatial dependency of the effective spin-spin interaction mediated by the growth induced wetting layer. Surprisingly, we found an antiferromagnetic Heisenberg coupling for very short inter-QD distances that is caused by the significant particle-hole asymmetry of the wetting layer band at very low filling. Using the NRG results obtained from realistic parameters as input for a semiclassical simulation for a large QD ensemble, we demonstrate that the experimentally reported phase shifts in the coherent spin dynamics between single and two color laser pumping can be reproduced by our model, solving a longstanding mystery of the microscopic origin of the inter QD electron spin-spin interaction.

cond-mat.mes-hall↗