The atomic vibrations of a solid surface can play a significant role in the reactions of surface-bound molecules, as well as their adsorption and desorption. Relevant phonon modes can involve the collective motion of atoms over a wide array of length scales. In this paper, we demonstrate how the generalized Langevin equation can be utilized to describe these collective motions weighted by their coupling to individual sites. Our approach builds upon the generalized Langevin oscillator (GLO) model originally developed by Tully. We extend the GLO by deriving parameters from atomistic simulation data. We apply this approach to study the memory kernel of a model platinum surface and demonstrate that the memory kernel has a bimodal form due to coupling to both low-energy acoustic modes and high-energy modes near the Debye frequency. The same bimodal form was observed across a wide variety of solids of different elemental compositions, surface structures, and solvation states. By studying how these dominant modes depend on the simulation size, we argue that the acoustic modes are frozen in the limit of macroscopic lattices. By simulating periodically replicated slabs of various sizes, we quantify the influence of phonon confinement effects in the memory kernel and their concomitant effect on simulated sticking coefficients.
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http://dx.doi.org/10.1021/acs.jctc.3c00473 | DOI Listing |
J Chem Phys
January 2025
CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China.
While most thermostats in molecular dynamics are designed for equilibrium systems, their extension to non-equilibrium simulations has little theoretical justification. In the literature, an artifact referred to as "lane formation" was discovered; however, its cause remained unclear and was simply attributed to a constraint on velocity fluctuations or non-ergodicity in thermostats. In addition, global deterministic thermostatted dynamics was found to exhibit unceasing phase-space compression in steady states, incompatible with their expected stationary distributions and Gibbs entropy, which was mistakenly perceived as inescapable.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Steklov Mathematical Institute of Russian Academy of Sciences, Gubkina St. 8, Moscow 119991, Russia.
While there are many works on the applications of machine learning, not so many of them are trying to understand the theoretical justifications to explain their efficiency. In this work, overfitting control (or generalization property) in machine learning is explained using analogies from physics and biology. For stochastic gradient Langevin dynamics, we show that the Eyring formula of kinetic theory allows to control overfitting in the algorithmic stability approach-when wide minima of the risk function with low free energy correspond to low overfitting.
View Article and Find Full Text PDFNanomaterials (Basel)
December 2024
Department of Industrial and Information Engineering and Economics, University of L'Aquila, Piazzale E. Pontieri 1, Monteluco di Roio, Roio Poggio, 67100 L'Aquila, AQ, Italy.
The aim of the present paper is to propose an innovative, one-step and sustainable process allowing us to obtain almost 10 kg/week of pure and crystalline simonkolleite nanoparticles (SK NPs) in only 8 min of reaction, working in water, under ambient conditions of pressure/temperature, guaranteeing at the same time low environmental impact and a high yield of NP production. In addition, the obtained NPs can also act as ZnO precursors at ambient temperature, and this result supports the sustainability of the process considering that, generally, the production of ZnO from SK occurred via annealing at high temperatures. The SK NPs appeared pure and crystalline, characterized by a highly uniform hexagonal lamellar feature.
View Article and Find Full Text PDFAm Stat
February 2024
Department of Biostatistics, UCLA.
This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Bayesian imaging literature, ProxMCMC employs the Moreau-Yosida envelope for a smooth approximation of the total-variation regularization term, fixes variance and regularization strength parameters as constants, and uses the Langevin algorithm for the posterior sampling. We extend ProxMCMC to be fully Bayesian by providing data-adaptive estimation of all parameters including the regularization strength parameter.
View Article and Find Full Text PDFJ Chem Phys
December 2024
Department of Physics, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
In this study, we present a comprehensive analysis of the motion of a tagged monomer within a Gaussian semiflexible polymer model. We carefully derived the generalized Langevin equation (GLE) that governs the motion of a tagged central monomer. This derivation involves integrating out all the other degrees of freedom within the polymer chain, thereby yielding an effective description of the viscoelastic motion of the tagged monomer.
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