@article{HoeflingDietrich2024, author = {H{\"o}fling, Felix and Dietrich, Siegfried}, title = {Structure of liquid-vapor interfaces: perspectives from liquid state theory, large-scale simulations, and potential grazing-incidence X-ray diffraction}, volume = {160}, journal = {The Journal of Chemical Physics}, arxiv = {http://arxiv.org/abs/2311.06425}, doi = {10.1063/5.0186955}, pages = {104107}, year = {2024}, abstract = {Grazing-incidence X-ray diffraction (GIXRD) is a scattering technique which allows one to characterize the structure of fluid interfaces down to the molecular scale, including the measurement of the surface tension and of the interface roughness. However, the corresponding standard data analysis at non-zero wave numbers has been criticized as to be inconclusive because the scattering intensity is polluted by the unavoidable scattering from the bulk. Here we overcome this ambiguity by proposing a physically consistent model of the bulk contribution which is based on a minimal set of assumptions of experimental relevance. To this end, we derive an explicit integral expression for the background scattering, which can be determined numerically from the static structure factors of the coexisting bulk phases as independent input. Concerning the interpretation of GIXRD data inferred from computer simulations, we account also for the finite sizes of the bulk phases, which are unavoidable in simulations. The corresponding leading-order correction beyond the dominant contribution to the scattered intensity is revealed by asymptotic analysis, which is characterized by the competition between the linear system size and the X-ray penetration depth in the case of simulations. Specifically, we have calculated the expected GIXRD intensity for scattering at the planar liquid--vapor interface of Lennard-Jones fluids with truncated pair interactions via extensive, high-precision simulations. The reported data cover interfacial and bulk properties of fluid states along the whole liquid--vapor coexistence line. A sensitivity analysis demonstrates the robustness of our findings concerning the detailed definition of the mean interface position. We conclude that previous claims of an enhanced surface tension at mesoscopic scales are amenable to unambiguous tests via scattering experiments.}, language = {en} } @inproceedings{SkoblinHoeflingChristgau2023, author = {Skoblin, Viktor and H{\"o}fling, Felix and Christgau, Steffen}, title = {Gaining Cross-Platform Parallelism for HAL's Molecular Dynamics Package using SYCL}, volume = {36}, booktitle = {29. PARS-Workshop 2023}, issn = {0177-0454}, arxiv = {http://arxiv.org/abs/2406.04210}, year = {2023}, abstract = {Molecular dynamics simulations are one of the methods in scientific computing that benefit from GPU acceleration. For those devices, SYCL is a promising API for writing portable codes. In this paper, we present the case study of HAL's MD package that has been successfully migrated from CUDA to SYCL. We describe the different strategies that we followed in the process of porting the code. Following these strategies, we achieved code portability across major GPU vendors. Depending on the actual kernels, both significant performance improvements and regressions are observed. As a side effect of the migration process, we obtained impressing speedups also for execution on CPUs.}, language = {en} } @article{RodriguesPelaVonaLubecketal.2024, author = {Rodrigues Pela, Ronaldo and Vona, Cecilia and Lubeck, Sven and Alex, Ben and Gonzalez Oliva, Ignacio and Draxl, Claudia}, title = {Critical assessment of G0W0 calculations for 2D materials: the example of monolayer MoS2}, volume = {10}, journal = {npj Comput. Mater.}, arxiv = {http://arxiv.org/abs/https://arxiv.org/abs/2310.04198}, doi = {10.1038/s41524-024-01253-2}, pages = {77}, year = {2024}, language = {en} } @article{KrishnadasSinhaKirchneretal.2024, author = {Krishnadas, Anirudh and Sinha, Nidhi and Kirchner, Tom and Antony, Bobby}, title = {Calculation of electron-impact ionization of various benzene derivatives}, volume = {99}, journal = {IOP Publications- Physica Scripta}, number = {9}, doi = {10.1088/1402-4896/ad640a}, pages = {095403}, year = {2024}, abstract = {Calculations of the electron-impact ionization cross-section of pyrene, anthracene, benzoyl chloride, benzophenone, and phthalonitrile are reported over a wide energy range. A comparison of theoretical models, viz. spherical complex optical potential (SCOP), pixel counting method (PCM), and the binary encounter Bethe (BEB) model is carried out. SCOP calculations provide the inelastic cross-section, and the ionization cross-section is extracted from it. This result is modified with PCM, a model previously applied to ion collisions which takes geometric screening corrections into account. The BEB model is used as an independent approach to calculate the ionization cross-section. It is demonstrated that all model results are in reasonable agreement with each other. Comparison is also made with other theoretical data where available.}, language = {en} } @article{KrishnadasCharronFournier2025, author = {Krishnadas, Anirudh and Charron, Nicolas and Fournier, Rene}, title = {Melting Transitions in Small Aluminum Clusters Simulated with Energies Approaching DFT Accuracy.}, journal = {ACS Publications- Journal of Chemical Theory and Computation}, year = {2025}, abstract = {We describe a computational framework for modelling melting-like transitions in atomic clusters that combines first-principles energy calculations, global optimization, and machine-learned interatomic potentials. A diverse set of configurations is generated by global optimization, and energies are calculated by Density Functional Theory. The energies are fitted to an accuracy of 10 meV/atom or better with an Allegro E(3)-equivariant neural network potential. The model allows efficient parallel tempering Monte Carlo simulations with near DFT-level accuracy. This methodology was validated by simulating Na_20 and comparing it to earlier experimental and computational results. We used it to study melting-like transitions in Al_n+ clusters (n=9 to 16), and Al_n and Al_n^- (n=12, 13,14). The simulated heat capacity of these clusters, in particular Al_16+, are in qualitative agreement with experiments. The melting point of Al_n+ clusters with n=11-16 are well above the bulk melting point (934 K). The closed-shell Al_13- species has an exceptionally high melting point, close to 2100 K.}, language = {en} } @article{KrishnadasMoshiQuintanaetal.2025, author = {Krishnadas, Anirudh and Moshi, Maryam and Quintana, Ramon Alain Miranda and Fournier, Rene}, title = {Geometric Features and a Neural Network Classifier for Detecting Melting-Like Transitions in Clusters.}, journal = {AIP Publications - The Journal of Chemical Physics}, year = {2025}, abstract = {Melting-like transitions in clusters are normally identified by a peak in the heat capacity curve C(T ) at T = Tc. Computing C(T ) requires costly simulations with millions of steps. We discuss four easily calculated functions of temperature that help detect and characterize melting-like transitions. The first, f1 (or WU ), is the width of the potential energy distribution which shows an abrupt increase near Tc. The other three are statistics of the ordered set of N(N - 1)/2 interatomic distances rij: (i) f2 is a measure of dissimilarity to the lowest energy configuration, or global minimum; (ii) f3 is the effective number of rij's found in a small interval centered around (r1 + r2)/2 where r1, r2 are the positions of the first two peaks in the pair distribution function; and (iii) f4 is a measure of non-uniformity in the distribution of the ri j's. Numerical tests with empirical potentials that model three types of bonding (van der Waals, covalent, and metallic) show that f1, f2, f3, and f4 produce estimates for the middle of the melting region in general agreement with Tc. An Artificial Neural Network (ANN) classifier that takes, as inputs, f2, f3, and many variants of f4, is used to calculate the solid fraction FS(T) and find the solid-liquid coexistence region between freezing and melting temperatures, [Tf , Tm]. Inflection points in f3(T ) and FS(T ) are very sensitive indicators of phase transitions. Estimates of Tc obtained from them converge one to three orders of magnitude faster, in simulation time, than those obtained with C(T ).}, language = {en} } @article{HoelterLemkeWeinzierletal.2025, author = {H{\"o}lter, Arne and Lemke, Mathias and Weinzierl, Stefan and Stein, Lewin}, title = {Non-Reflecting Characteristic Boundary Conditions for Adjoint Time-Domain Acoustic Simulations}, journal = {Journal of Theoretical and Computational Acoustics}, year = {2025}, abstract = {Accurate acoustic simulations in the free field require non-reflective boundary conditions to suppress spurious reflections at the computational domain boundaries. Although several characteristic-based formulations for direct (forward) simulations have been proposed in recent decades, the adjoint formulations of such characteristic-based boundary conditions (CBCs) have received limited atten- tion in the literature and lack a comprehensive analysis. This paper presents the derivation and evaluation of adjoint CBCs complementing the existing direct CBCs. Both the forward and adjoint CBCs are applied to the (nonlinear) Euler equations and linear acoustic equations in time-domain simulations. In this manner, the CBCs are investigated and subsequently compared to assess their respective accuracy and consistency. The CBCs were implemented using both a single-point and a zonal approach, with the former optionally combined with a sponge layer. Both approaches yielded comparable results in direct and adjoint simulations, while the zonal CBC exhibited improved ac- curacy at lower frequencies. Across the evaluated frequency range of approximately 350 - 5600 Hz, spurious reflections were attenuated by up to -70dB in both forward and adjoint cases, demon- strating the effectiveness and consistency of the proposed boundary treatment.}, language = {en} } @article{StraubeHoefling2024, author = {Straube, Arthur and H{\"o}fling, Felix}, title = {Memory effects in colloidal motion under confinement and driving}, volume = {57}, journal = {J. Phys. A: Math. Theor.}, publisher = {IOP Publishing}, issn = {1751-8113}, doi = {10.1088/1751-8121/ad5b2d}, pages = {295003}, year = {2024}, abstract = {The transport of individual particles in inhomogeneous environments is complex and exhibits non-Markovian responses. The latter may be quantified by a memory function within the framework of the linear generalised Langevin equation (GLE). Here, we exemplify the implications of steady driving on the memory function of a colloidal model system for Brownian motion in a corrugated potential landscape, specifically, for one-dimensional motion in a sinusoidal potential. To this end, we consider the overdamped limit of the GLE, which is facilitated by separating the memory function into a singular (Markovian) and a regular (non-Markovian) part. Relying on exact solutions for the investigated model, we show that the random force entering the GLE must display a bias far from equilibrium, which corroborates a recent general prediction. Based on data for the mean-square displacement (MSD) obtained from Brownian dynamics simulations, we estimate the memory function for different driving strengths and show that already moderate driving accelerates the decay of the memory function by several orders of magnitude in time. We find that the memory may persist on much longer timescales than expected from the convergence of the MSD to its long-time asymptote. Furthermore, the functional form of the memory function changes from a monotonic decay to a non-monotonic, damped oscillatory behaviour, which can be understood from a competition of confined motion and depinning. Our analysis of the simulation data further reveals a pronounced non-Gaussianity, which questions the Gaussian approximation of the random force entering the GLE.}, language = {en} } @misc{Secker2023, author = {Secker, Christopher}, title = {Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists (Dataset)}, doi = {10.12752/9622}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-96220}, year = {2023}, abstract = {Virtual Screening Dataset for the paper "Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists" by Secker et al. (https://doi.org/10.1186/s13321-023-00746-4)}, language = {en} } @masterthesis{Bena2023, type = {Bachelor Thesis}, author = {Bena, Noor-Cella}, title = {Finding Rules and Patterns in Egyptological Research Objects using Boolean Polynomial Rings}, pages = {35}, year = {2023}, language = {en} }