Artificial intelligence and machine learning (AI/ML) are transforming experimental condensed matter physics and material science, enhancing the discovery rate. Our work focuses on colloidal metal-halide perovskite quantum dots, which are versatile nanomaterials with strong potential as LEDs, quantum light sources, and other optoelectronic devices. Owing to their broad compositional tunability...
Conventional methods for monitoring insect populations and diversity often encounter limitations in spatial and temporal resolution, are labor-intensive, and carry inherent biases from light or bait traps. To overcome these challenges, our research group employs entomological Lidar. This technique, unlike traditional time-of-flight Lidar, uses a specialized Scheimpflug configuration that...
Knowledge of the shape and duration of ultrashort laser pulses plays an important role, e.g., in the optimization of high-harmonic generation (HHG), pump-probe spectroscopy, generation of Terahertz radiation [1], and can be an ideal diagnostic tool for lasers systems, be they conventional or novel. Among the current laser pulse characterization methods, the dispersion-scan (d-scan) technique...
Vision transformers (ViTs) have emerged as powerful tools in computer vision. However, ViTs can be resource-intensive because of their reliance on the self-attention mechanism that involves $\mathcal{O}(n^2)$ complexity, where $n$ is the sequence length. These challenges become even more pronounced in quantum computing, where handling large-scale models is constrained by limited qubit...
Many complex oxides undergo a temperature-driven insulator-to-metal transition (IMT), usually around room temperature. In some materials, an IMT can also be induced by applying a current, opening the possibility to use these materials as controllable switches in electronic devices. $\textrm{Ca}_2\textrm{RuO}_4$ is such a case: The resistivity can be changed by orders of magnitude when a...
I present a machine learning framework to investigate the catalytic activity of monolayer binary alloys toward the oxygen reduction reaction (ORR). Leveraging a dataset comprising thousands of density functional theory (DFT) calculations of *OH adsorption energies on AgPt/Pt(111), AuCu/Cu(111), AuPt/Pt(111), and AuPd/Pd(111) monolayer alloy surfaces, I engineered 25 structural, energetic, and...
Perovskite materials are central to various technologies, particularly in photovoltaic applications. However, computational studies of perovskites using ab initio methods are limited by computational cost, especially when simulating large systems or long time scales. Molecular dynamics (MD) simulations offer a viable alternative, yet classical interatomic potentials often fall short in...
This work presents a systematic investigation into how particle shape and softness—specifically in bacterial samples—affect sorting in deterministic lateral displacement (DLD) microfluidic devices. While previous studies have qualitatively observed the impact of non-spherical shapes, we provide a quantitative, two-level experimental analysis using shape-defined particles and high-speed...
Angle-resolved photoemission spectroscopy (ARPES) is a key experimental technique to determine the electronic structure of quantum materials. Recently, two new variations of ARPES have been introduced. MicroAREPES adds spatial resolution, so that inhomogeneous samples or operating devices can be studied. Time-resolved ARPES adds time resolution, opening a window on dynamic processes, such as...
MeV gamma-ray astronomy holds the key to studying some of the Universe’s most energetic and dynamic phenomena, including kilonovae, supernovae, and gamma-ray bursts. Yet, this region of the spectrum remains underexplored (the so-called “MeV Gap”) due to low photon interaction probability, high background levels, and complex signal responses in detectors.
At DTU Space, we address this...
This paper reports a novel microfluidic approach for characterizing and sorting breast cancer cell subpopulations based on size and mechanical properties, using deterministic lateral displacement (DLD). The metastatic potential of cancer cells is closely linked to their mechanical characteristics, which evolve with tumor progression and reflect cellular heterogeneity. Small and large tumor...
