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On data-driven parameterizations of multidimensional generalized Langevin dynamics in the presence of a quadratic potential
Authors:
Maximilian Braun,
Martin Hanke,
Niklas Wolf
Abstract:
We propose a numerical algorithm to construct a Markov model with an extended list of variables to parameterize the equation of motion of a multidimensional coarse-grained physical system in an external potential, when memory effects are relevant. Our method uses autocorrelation data of the stationary velocities, but it avoids the inverse problem of finding the corresponding memory kernel from the…
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We propose a numerical algorithm to construct a Markov model with an extended list of variables to parameterize the equation of motion of a multidimensional coarse-grained physical system in an external potential, when memory effects are relevant. Our method uses autocorrelation data of the stationary velocities, but it avoids the inverse problem of finding the corresponding memory kernel from these data in a first step. Rather, the data are used to construct a Prony series approximation of the autocorrelation function, and the parameters of this Prony series provide the corresponding Markov model. Numerical results for molecular dynamics data show a good match for parameterized models with five auxiliary variables for a one-dimensional, and twelve auxiliary variables for a two-dimensional system.
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Submitted 6 July, 2026;
originally announced July 2026.
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Memory Effects in Contact Line Friction
Authors:
Niklas Wolf,
Nico van der Vegt
Abstract:
When a drop of liquid comes into contact with a solid surface, it relaxes towards an equilibrium configuration, either wetting the surface or remaining in a droplet-like shape with a finite contact angle. The force driving the process towards equilibrium is the corresponding out-of-balance Young's force. However, the speed with which the liquid front advances depends strongly on an opposing fricti…
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When a drop of liquid comes into contact with a solid surface, it relaxes towards an equilibrium configuration, either wetting the surface or remaining in a droplet-like shape with a finite contact angle. The force driving the process towards equilibrium is the corresponding out-of-balance Young's force. However, the speed with which the liquid front advances depends strongly on an opposing friction force arising from dissipative processes due to the moving solid-liquid-gas contact line. In analogy to the treatment of hydrodynamic friction we present an exact method, based on the Mori-Zwanzig formalism, to extract this friction from equilibrium data. We find that the contact line exhibits long-lasting memory with a characteristic power-law decay due to coupling to the systems hydrodynamic modes. Within linear response regime, we obtain the frequency-dependent dissipative and elastic response of the contact line to an external perturbation, including a frequency-dependent friction coefficient. Similar to hydrodynamic friction in liquids, we find that the friction decreases beyond a characteristic frequency and the system exhibits predominantly elastic behavior.
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Submitted 25 November, 2025;
originally announced November 2025.
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Learning conformational ensembles of proteins based on backbone geometry
Authors:
Nicolas Wolf,
Leif Seute,
Vsevolod Viliuga,
Simon Wagner,
Jan Stühmer,
Frauke Gräter
Abstract:
Deep generative models have recently been proposed for sampling protein conformations from the Boltzmann distribution, as an alternative to often prohibitively expensive Molecular Dynamics simulations. However, current state-of-the-art approaches rely on fine-tuning pre-trained folding models and evolutionary sequence information, limiting their applicability and efficiency, and introducing potent…
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Deep generative models have recently been proposed for sampling protein conformations from the Boltzmann distribution, as an alternative to often prohibitively expensive Molecular Dynamics simulations. However, current state-of-the-art approaches rely on fine-tuning pre-trained folding models and evolutionary sequence information, limiting their applicability and efficiency, and introducing potential biases. In this work, we propose a flow matching model for sampling protein conformations based solely on backbone geometry - BBFlow. We introduce a geometric encoding of the backbone equilibrium structure as input and propose to condition not only the flow but also the prior distribution on the respective equilibrium structure, eliminating the need for evolutionary information. The resulting model is orders of magnitudes faster than current state-of-the-art approaches at comparable accuracy, is transferable to multi-chain proteins, and can be trained from scratch in a few GPU days. In our experiments, we demonstrate that the proposed model achieves competitive performance with reduced inference time, across not only an established benchmark of naturally occurring proteins but also de novo proteins, for which evolutionary information is scarce or absent. BBFlow is available at https://github.com/graeter-group/bbflow.
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Submitted 12 November, 2025; v1 submitted 19 February, 2025;
originally announced March 2025.
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A Gauss-Newton method for iterative optimization of memory kernels for generalized Langevin thermostats in coarse-grained molecular dynamics simulations
Authors:
V. Klippenstein,
N. Wolf,
N. F. A. van der Vegt
Abstract:
In molecular dynamics simulations, dynamically consistent coarse-grained (CG) models commonly use stochastic thermostats to model friction and fluctuations that are lost in a CG description. While Markovian, i.e., time-local, formulations of such thermostats allow for an accurate representation of diffusivities/long-time dynamics, a correct description of the dynamics on all time scales generally…
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In molecular dynamics simulations, dynamically consistent coarse-grained (CG) models commonly use stochastic thermostats to model friction and fluctuations that are lost in a CG description. While Markovian, i.e., time-local, formulations of such thermostats allow for an accurate representation of diffusivities/long-time dynamics, a correct description of the dynamics on all time scales generally requires non-Markovian, i.e., non-time-local, thermostats. These thermostats are typically in the form of a Generalized Langevin Equation (GLE) determined by a memory kernel. In this work, we use a Markovian embedded formulation of a position-independent GLE thermostat acting independently on each CG degree of freedom. Extracting the memory kernel of this CG model from atomistic reference data requires several approximations. Therefore, this task is best understood as an inverse problem. While our recently proposed approximate Newton scheme, Iterative Optimization of memory kernels (IOMK), allows for the iterative optimization of a memory kernel, Markovian embedding remained potentially error-prone and computationally expensive. In this work, we present a IOMK-Gauss-Newton scheme (IOMK-GN) based on IOMK, that allows for the direct parameterization of a Markovian embedded model.
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Submitted 21 June, 2024; v1 submitted 16 February, 2024;
originally announced February 2024.
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Bose-Einstein Condensation of Photons in a Four-Site Quantum Ring
Authors:
Andreas Redmann,
Christian Kurtscheid,
Niels Wolf,
Frank Vewinger,
Julian Schmitt,
Martin Weitz
Abstract:
Thermalization of radiation by contact to matter is a well-known concept, but the application of thermodynamic methods to complex quantum states of light remains a challenge. Here we observe Bose-Einstein condensation of photons into the hybridized ground state of a four-site ring potential with coherent tunnel couplings. In our experiment, the periodically-closed ring lattice superimposed by a we…
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Thermalization of radiation by contact to matter is a well-known concept, but the application of thermodynamic methods to complex quantum states of light remains a challenge. Here we observe Bose-Einstein condensation of photons into the hybridized ground state of a four-site ring potential with coherent tunnel couplings. In our experiment, the periodically-closed ring lattice superimposed by a weak harmonic trap for photons is realized inside a spatially structured dye-filled microcavity. Photons thermalize to room temperature, and above a critical photon number macroscopically occupy the symmetric linear combination of the site eigenstates with zero phase winding, which constitutes the ground state of the system. The mutual phase coherence of photons at different lattice sites is verified by optical interferometry.
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Submitted 6 September, 2024; v1 submitted 22 December, 2023;
originally announced December 2023.
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Cesium-involved electron transfer and electron-electron interaction in high-pressure metallic CsPbI3
Authors:
Feng Ke,
Jiejuan Yan,
Shanyuan Niu,
Jiajia Wen,
Ketao Yin,
Nathan R. Wolf,
Yan-Kai Tzeng,
Hemamala I. Karunadasa,
Young S. Lee,
Wendy L. Mao,
Yu Lin
Abstract:
Electron-phonon coupling was believed to govern the carrier transport in halide perovskites and related phases. Here we demonstrate that electron-electron interaction plays a direct and prominent role in the low-temperature electrical transport of compressed CsPbI3 and renders Fermi liquid (FL)-like behavior. By compressing δ-CsPbI3 to 80 GPa, an insulator-to-metal transition occurs, concomitant w…
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Electron-phonon coupling was believed to govern the carrier transport in halide perovskites and related phases. Here we demonstrate that electron-electron interaction plays a direct and prominent role in the low-temperature electrical transport of compressed CsPbI3 and renders Fermi liquid (FL)-like behavior. By compressing δ-CsPbI3 to 80 GPa, an insulator-to-metal transition occurs, concomitant with the completion of a sluggish structural transition from the one-dimensional (1D) Pnma (δ) phase to a 3D Pmn21 (ε) phase. Deviation from FL behavior is observed in CsPbI3 upon entering the metallic ε phase, which progressively evolves into a FL-like state at 186 GPa. First-principles density functional theory calculations reveal that the enhanced electron-electron coupling is related to the Cs-involved electron transfer and sudden increase of the 5d state occupation of the high-pressure ε phase. Our study presents a promising strategy for tuning the electronic interaction in halide perovskites for realizing intriguing electronic states.
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Submitted 2 March, 2022;
originally announced March 2022.