Hasil untuk "physics.comp-ph"

C. L. Karr, E. Gentry

T. Clayton, R. Byrne

J. Russell, D. Wilson

Ruminant animals depend on cellulolytic ruminal bacteria to digest cellulose, but these bacteria cannot resist the low ruminal pH that modern feeding practices can create. Because the cellulolytic bacteria cannot grow on cellobiose at low pH, pH sensitivity is a general aspect of growth and not just a limitation of the cellulases per se. Acid-resistant ruminal bacteria have evolved the capacity to let their intracellular pH decrease, maintain a small pH gradient across the cell membrane, and prevent an intracellular accumulation of VFA anions. Cellulolytic bacteria cannot grow with a low intracellular pH, and an increase in pH gradient leads to anion toxicity. Prevotella ruminicola cannot digest native cellulose, but it grows at low pH and degrades the cellulose derivative, carboxymethylcellulose. The Prevotella ruminicola carboxymethylcellulase cannot bind to cellulose, but a recombinant enzyme having the Prevotella ruminicola catalytic domain and a binding domain from Thermomonspora fusca was able to bind and had cellulase activity that was at least 10-fold higher. Based on these results, gene reconstruction offers a means of converting Prevotella ruminicola into a ruminal bacterium that can digest cellulose at low pH.

J. Fallingborg

Rhyan Barrett, Johannes C. B. Dietschreit, Julia Westermayr

Simulating long-range interactions remains a significant challenge for molecular machine learning potentials due to the need to accurately capture interactions over large spatial regions. In this work, we introduce FieldMACE, an extension of the message-passing atomic cluster expansion (MACE) architecture that integrates the multipole expansion to model long-range interactions more efficiently. By incorporating the multipole expansion, FieldMACE effectively captures environmental and long-range effects in both ground and excited states. Benchmark evaluations demonstrate its superior performance in predictions and computational efficiency compared to previous architectures, as well as its ability to accurately simulate nonadiabatic excited-state dynamics. Furthermore, transfer learning from foundational models enhances data efficiency, making FieldMACE a scalable, robust, and transferable framework for large-scale molecular simulations.

Alexios A Michailidis, Christian Fenton, Martin Kiffner

We present the Tensor Train Multiplication (TTM) algorithm for the elementwise multiplication of two tensor trains with bond dimension $χ$. The computational complexity and memory requirements of the TTM algorithm scale as $χ^3$ and $χ^2$, respectively. This represents a significant improvement compared with the conventional approach, where the computational complexity scales as $χ^4$ and memory requirements scale as $χ^3$.We benchmark the TTM algorithm using flows obtained from artificial turbulence generation and numerically demonstrate its improved runtime and memory scaling compared with the conventional approach. The TTM algorithm paves the way towards GPU accelerated tensor network simulations of computational fluid dynamics problems with large bond dimensions due to its dramatic improvement in memory scaling.

Edwin A. Yates, B. Philipp, C. Buckley et al.

Fatemeh Hafezianzade, Morad Biagooi, SeyedEhsan Nedaaee Oskoee

In this paper, we developed a new PINN-based model to predict the potential of point-charged particles surrounded by conductive walls. As a result of the proposed physics-informed neural network model, the mean square error and R2 score are less than 7% and more than 90% for the corresponding example simulation, respectively. Results have been compared with typical neural networks and random forest as a standard machine learning algorithm. The R2 score of the random forest model was 70%, and a standard neural network could not be trained well. Besides, computing time is significantly reduced compared to the finite element solver.

Yuki Sakamoto, Masahito Ueda

We consider an iterated multiplayer prisoner's dilemma game on a square lattice and regular graphs based on the pairwise-Fermi update rule, and obtain heat-maps of the fraction of cooperators and the correlation of neighboring pairs. In the heat-map, there is a mixed region where cooperators and defectors coexist, and in the mixed region the correlation between neighbors is enhanced. Moreover, we observe pink-noise behavior in the mixed region, where the power spectrum can be fitted by a power-law function of frequency. We also find that the pink-noise behavior can be reproduced in a simple random-walk model. In particular, we propose a modified random-walk model which can reproduce not only the pink-noise behavior but also the deviation from it observed in a low-frequency region.

H. Izumi, T. Torigoe, H. Ishiguchi et al.

J. Rodríguez-Hernández, S. Lecommandoux

Jianzhong Du, Yiqing Tang, A. Lewis et al.

Valentin Niess

The PUMAS library is a transport engine for muon and tau leptons in matter. It can operate with a configurable level of details, from a fast deterministic CSDA mode to a detailed Monte~Carlo simulation. A peculiarity of PUMAS is that it is revertible, i.e. it can run in forward or in backward mode. Thus, the PUMAS library is particularly well suited for muography applications. In the present document, we provide a detailed description of PUMAS, of its physics and of its implementation.

J. Antelo, M. Avena, S. Fiol et al.

The surface properties of a well-crystallized synthetic goethite have been studied by acid-base potentiometric titrations, electrophoresis, and phosphate and arsenate adsorption isotherms at different pH and electrolyte concentrations. The PZC and IEP of the studied goethite were 9.3+/-0.1 and 9.3+/-0.2, respectively. Phosphate and arsenate adsorption decrease as the pH increases in either 0.1 or 0.01 M KNO(3) solutions. Phosphate adsorption is more sensitive to changes in pH and ionic strength than that of arsenate. The combined effects of pH and ionic strength result in higher phosphate adsorption in acidic media at most ionic strengths, but result in lower phosphate adsorption in basic media and low ionic strengths. The CD-MUSIC model yields rather good fit of the experimental data. For phosphate it was necessary to postulate the presence of three inner-sphere surface complexes (monodentate nonprotonated, bidentate nonprotonated, and bidentate protonated). In contrast, arsenate could be well described by postulating only the presence of the two bidenate species. A small improvement of the arsenate adsorption data could be achieved by assuming the presence of a monodentate protonated species. Model predictions are in agreement with spectroscopic evidence, which suggest, especially for the case of arsenate, that mainly bidentate inner-sphere complexes are formed at the goethite-water interface.

T. Rabilloud, C. Adessi, A. Giraudel et al.

Membrane and nuclear proteins of poor solubility have been separated by high resolution two‐dimensional (2‐D) gel electrophoresis. Isoelectric focusing with immobilized pH gradients leads to severe quantitative losses of proteins in the resulting 2‐D map, although the resolution is usually high. Protein solubility could be improved by using denaturing solutions containing various detergents and chaotropes. Best results were obtained with a denaturing solution containing urea, thiourea, and detergents (both nonionic and zwitterionic). The usefulness of thiourea‐containing denaturing mixtures is shown for microsomal and nuclear proteins as well as for tubulin, a protein highly prone to aggregation.

Rick Orij, J. Postmus, Alexander Ter Beek et al.

Bernd Albert Berg

A simple 3-parameter random walk model for monthly fluctuations $\triangle T$ of a temperature $T$ is introduced. Applied to a time range of 170 years, temperature fluctuations of the model produce for about 14\% of the runs warming that exceeds the observed global warming of the earth surface temperature from 1850 to 2019. On the other hand, there is a 50\% likelihood for runs of our model resulting in cooling. If a similar random walk process can be used as an effective model for fluctuations of the global earth surface temperature, effects due to internal and external forcing could be considerably over- or underestimated.

Alexander V. Oleynichenko, Andréi Zaitsevskii, Ephraim Eliav

Modern challenges arising in the fields of theoretical and experimental physics require new powerful tools for high-precision electronic structure modelling; one of the most perspective tools is the relativistic Fock space coupled cluster method (FS-RCC). Here we present a new extensible implementation of the FS-RCC method designed for modern parallel computers. The underlying theoretical model, algorithms and data structures are discussed. The performance and scaling features of the implementation are analyzed. The software developed allows to achieve a completely new level of accuracy for prediction of properties of atoms and molecules containing heavy and superheavy nuclei.

Felix Sharipov, Victor J. Benites

The viscosity and thermal conductivity of binary, ternary and quaternary mixtures of helium, neon, argon, and krypton at low density are computed for wide ranges of temperature and molar fractions, applying the Chapman-Enskog method. Ab initio interatomic potentials are employed in order to calculate the omega-integrals. The relative numerical errors of the viscosity and thermal conductivity do not exceed 1.e-6 and 1.e-5, respectively. The relative uncertainty related to the interatomic potential is about 0.1%. A comparison of the present data with results reported in other papers available in the literature shows a significant improvement of accuracy of the transport coefficients considered here.

Markus Kantner, Thomas Koprucki

Electro-thermal transport phenomena in semiconductors are described by the non-isothermal drift-diffusion system. The equations take a remarkably simple form when assuming the Kelvin formula for the thermopower. We present a novel, non-isothermal generalization of the Scharfetter-Gummel finite volume discretization for degenerate semiconductors obeying Fermi-Dirac statistics, which preserves numerous structural properties of the continuous model on the discrete level. The approach is demonstrated by 2D simulations of a heterojunction bipolar transistor.