arXiv subjects

Hazem Daoud

Publications and source records attributed to Hazem Daoud.

6 recordsLinked to original sources

arXiv2025

SCULPT: An Interactive Machine Learning Platform for Analyzing Multi-Particle Coincidence Data from Cold Target Recoil Ion Momentum Spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements Uniform Manifold Approximation and Projection (UMAP) for non-linear dimensionality reduction to reveal correlations in highly dimensional data. We also discuss potential extensions to deep autoencoders for feature learning, and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric's robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT's capabilities, we analyze photo double ionization data measured using the COLTRIMS method for 3-body dissociation of the D2O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software's modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

physics.atm-clus↗

arXiv2025

Femtosecond photo-induced displacive phase transition in Sb$_{2}$Te (group 2) phase-change material

Two classes of Phase Change Materials (PCMs) have emerged as the best candidates for applications requiring the fast reading and writing of data: GeTe-Sb$_{2}$Te$_{3}$ pseudobinary alloys (group 1) and doped Sb-Te compounds near the eutectic composition Sb$_{70}$Te$_{30}$ (group 2). Both material classes undergo reversible switching between a low-resistance opaque crystalline phase and a high-resistance but less absorbing amorphous phase through heating, electrical, or optical pulses, achieving (sub-)nanosecond switching speeds. While group 1 compounds are employed in current generation devices and relatively well studied, model systems in group 2 compounds have been found to crystallize more rapidly and thus offer the perspective of improved devices. Despite their superior crystallization speed (SET process), to this point there have been no ultrafast experimental studies on crystallized PCMs of group 2 for the RESET process. Here we perform ultrafast electron diffraction and femtosecond resolved sum frequency non-linear spectroscopy on Peierls distorted Sb$_{2}$Te crystallized thin films (PCM of group 2) following femtosecond optical pulse irradiation. We observe a pump-induced structural change on two distinct timescales: responses with characteristic timescales of $\approx$ 300 fs and 2~ps. We quantified the experimental result by a coherent displacement and the Debye-Waller effect. In particular, the $\approx$ 300 fs UED signal results from the ultrafast release of the Peierls distortion through non-thermal coherent Sb displacement, while the 2~ps response reflects electron-lattice equilibrium. These results reveal the ultrafast non-thermal structural dynamics of Sb$_{2}$Te and suggest energy-efficient switching of group 2 PCMs should be possible on femtosecond time scales.

cond-mat.mtrl-sci↗

arXiv2023

Synthesis technique and electron beam damage study of nanometer-thin single-crystalline Thymine

Samples suitable for electron diffraction studies must satisfy certain characteristics such as having a thickness in the range of 10 - 100 nm. We report, to our knowledge, the first successful synthesis technique of nanometer-thin sheets of single-crystalline thymine suitable for electron diffraction and spectroscopy studies. This development provides a well defined system to explore issues related to UV photochemistry of DNA and high intrinsic stability essential to maintaining integrity of genetic information. The crystals are grown using the evaporation technique and the nanometer-thin sheets are obtained via microtoming. The sample is characterized via x-ray diffraction (XRD) and is subsequently studied using electron diffraction via a transmission electron microscope (TEM). Thymine is found to be more radiation resistant than similar molecular moieties (e.g., carbamazepine) by a factor of 5. This raises interesting questions about the role of the fast relaxation processes of electron scattering-induced excited states, extending the concept of radiation hardening beyond photoexcited states. The high stability of thymine in particular opens the door for further studies of these ultrafast relaxation processes giving rise to the high stability of DNA to UV radiation.

physics.chem-ph↗

arXiv2022

Novel applications of Generative Adversarial Networks (GANs) in the analysis of ultrafast electron diffraction (UED) images

Inferring transient molecular structural dynamics from diffraction data is an ambiguous task that often requires different approximation methods. In this paper we present an attempt to tackle this problem using machine learning. While most recent applications of machine learning for the analysis of diffraction images apply only a single neural network to an experimental dataset and train it on the task of prediction, our approach utilizes an additional generator network trained on both synthetic data and experimental data. Our network converts experimental data into idealized diffraction patterns from which information is extracted via a convolutional neural network (CNN) trained on synthetic data only. We validate this approach on ultrafast electron diffraction (UED) data of bismuth samples undergoing thermalization upon excitation via 800 nm laser pulses. The network was able to predict transient temperatures with a deviation of less than 6% from analytically estimated values. Notably, this performance was achieved on a dataset of 408 images only. We believe employing this network in experimental settings where high volumes of visual data are collected, such as beam lines, could provide insights into the structural dynamics of different samples.

physics.chem-ph↗

arXiv2021

Utilizing relativistic time dilation for time-resolved studies

Time-resolved studies have so far relied on rapidly triggering a photo-induced dynamic in chemical or biological ions or molecules and subsequently probing them with a beam of fast moving photons or electrons that crosses the studied samples in a short period of time. Hence, the time resolution of the signal is mainly set by the pulse duration of the pump and probe pulses. In this paper we propose a different approach to this problem that has the potential to consistently achieve orders of magnitude higher time resolutions than what is possible with laser technology or electron beam compression methods. Our proposed approach relies on accelerating the sample to a high speed to achieve relativistic time dilation. Probing the time-dilated sample would open up previously inaccessible time resolution domains.

physics.chem-ph↗

arXiv2018

Exploring vibrational ladder climbing in vibronic coupling models: Toward experimental observation of a geometric phase signature of a conical intersection

Conical intersections (CIs) have been widely studied using spectroscopic techniques. However, CIs have mainly been identified by rapid internal conversion transitions that take place after the photoexcitation. Such identifications cannot distinguish various types of intersections as well as to separate the actual intersection from an avoided crossing. In this paper, we investigate how ultrafast IR laser pulses can be utilized to stimulate nuclear dynamics revealing geometric phase features associated with CIs. We consider two low-dimensional nonadiabatic models to obtain optimal two- and three-pulse laser sequences for stimulating nuclear dynamics necessary for the CI identification. Our results provide insights on designing non-linear spectroscopic schemes for subsequent probes of the nuclear wavepackets by ultrafast electron diffraction techniques to unambiguously detect CIs in molecules.

physics.chem-ph↗