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We simulated Coulomb explosion dynamics due to fast ionization induced by high-intensity x-rays in six proteins that share similar atomic content and shape. We followed and projected the trajectory of the fragments onto a virtual detector, providing a unique explosion footprint. After collecting 500 explosion footprints for each protein, we utilized principal component analysis and 𝑡-distributed stochastic neighbor embedding to classify these. Results show that the classification algorithms were able to separate proteins on the basis of explosion footprints from structurally similar proteins into distinct groups. The explosion footprints, therefore, provide a unique identifier for each protein. We envision that method could be used concurrently with single-particle coherent imaging experiments to provide additional information on shape, mass, or conformation.

We describe a method to compute photon–matter interaction and atomic dynamics with x-ray lasers using a hybrid code based on classical molecular dynamics and collisional-radiative calculations. The forces between the atoms are dynamically determined based on changes to their electronic occupations and the formation of a free electron cloud created from the irradiation of photons in the x-ray spectrum. The rapid transition from neutral solid matter to dense plasma phase allows the use of screened potentials, reducing the number of non-bonded interactions. In combination with parallelization through domain decomposition, the hybrid code handles large-scale molecular dynamics and ionization. This method is applicable for large enough samples (solids, liquids, proteins, viruses, atomic clusters, and crystals) that, when exposed to an x-ray laser pulse, turn into a plasma in the first few femtoseconds of the interaction. We present four examples demonstrating the applicability of the method. We investigate the non-thermal heating and scattering of bulk water and damage-induced dynamics of a protein crystal using an x-ray pump–probe scheme. In both cases, we compare to the experimental data. For single particle imaging, we simulate the ultrafast dynamics of a methane cluster exposed to a femtosecond x-ray laser. In the context of coherent diffractive imaging, we study the fragmentation as given by an x-ray pump–probe setup to understand the evolution of radiation damage in the time range of hundreds of femtoseconds.

Radiation therapy uses ionizing radiation to break chemical bonds in cancer cells, thereby causing DNA damage and leading to cell death. The therapeutic effectiveness can be further increased by making the tumor cells more sensitive to radiation. Here, we investigate the role of the initial halogen atom core hole on the photofragmentation dynamics of 2-bromo-5-iodo-4-nitroimidazole, a potential bifunctional radiosensitizer. Bromine and iodine atoms were included in the molecule to increase the photoionization cross-section of the radiosensitizer at higher photon energies. The fragmentation dynamics of the molecule was studied experimentally in the gas phase using photoelectron-photoion-photoion coincidence spectroscopy and computationally using Born-Oppenheimer molecular dynamics. We observed significant changes between shallow core (I 4d, Br 3d) and deep core (I 3d) ionization in fragment formation and their kinetic energies. Despite the fact, that the ions ejected after deep core ionization have higher kinetic energies, we show that in a cellular environment, the ion spread is not much larger, keeping the damage well-localized. A study on photodissociation dynamics of 2-bromo-5-iodo-nitroimidazole - a model radiosensitizer - using coincidence spectroscopy and computational methods.

- Single particle imaging using X-ray lasers is a technique aiming to capture atomic resolution structures of biomolecules in their native state. Knowing the particle's orientation during exposure is crucial for method enhancement. It has been shown that the trajectories of sulfur atoms in a Coulomb exploding lysozyme are reproducible, providing orientation information. This study explores if sulfur atom depth influences explosion trajectory. Employing a hybrid collisional-radiative/molecular dynamics model, we analyze the X-ray laser-induced dynamics of a single sulfur ion at varying depths in water. Our findings indicate that the ion spread-depth relationship depends on pulse parameters. At a photon energy of 2 keV, high-charge states are obtained, resulting in an increase of the spread with depth. However, at 8 keV photon energy, where lower charge states are obtained, the spread is essentially independent with depth. Finally, lower ion mass results in less reproducible trajectories, opening a promising route for determining protein orientation through the introduction of heavy atoms.

Nonadiabatic holonomic quantum computation is a method used to implement high-speed quantum gates with non-Abelian geometric phases associated with paths in state space. Due to their noise tolerance, these phases can be used to construct error resilient quantum gates. We extend the holonomic dark path qubit scheme in [M.-Z. Ai et al., Fundam. Res. 2, 661 (2022)] to qudits. Specifically, we demonstrate one- and two-qudit universality by using the dark path technique. Explicit qutrit (d = 3) gates are demonstrated and the scaling of the number of loops with the dimension d is addressed. This scaling is linear and we show how any diagonal qudit gate can be implemented efficiently in any dimension.

We have developed a hybrid Monte Carlo and classical molecular dynamics code to follow the ultrafast atomic dynamics in biological macromolecules induced by a femtosecond X-ray laser. Our model for fragmentation shows good qualitative agreement with ab-initio simulations of small molecules, while being computationally faster.  We applied the code for macromolecules and simulated the Coulomb explosion dynamics due to the fast ionization in six proteins with different physical properties. The trajectories of the ions are followed and projected onto a detector, where the particular pattern depends on the protein, providing a unique footprint. We utilize algorithms such as principal component analysis  and t-distributed stochastic neighbor embedding to classify the fragmentation pattern. The results show that the classification algorithms are able to separate the explosion patterns into distinct groups. We envision that this method could be used to provide additional class information, like particle mass or shape, in structural determination experiments using X-ray lasers.