Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations.

Constructing a self-consistent first-principles framework that accurately predicts the properties of electron transfer reactions through finite-temperature molecular dynamics simulations is a dream of theoretical electrochemists and physical chemists. Yet, predicting even the absolute standard hydrogen electrode potential, the most fundamental reference for electrode potentials, proves to be extremely challenging. Here, we show that a hybrid functional incorporating 25% exact exchange enables quantitative predictions when statistically accurate phase-space sampling is achieved thermodynamic integrations and thermodynamic perturbation theory calculations, utilizing machine-learned force fields and Δ-machine learning models.

Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential.

Polymers are a class of materials that are highly challenging to deal with using first-principles methods. Here, we present an application of machine-learned interatomic potentials to predict structural and dynamical properties of dry and hydrated perfluorinated ionomers. An improved active-learning algorithm using a small number of descriptors allows to efficiently construct an accurate and transferable model for this multielemental amorphous polymer.

First-principles hydration free energies of oxygenated species at water-platinum interfaces.

The hydration free energy of atoms and molecules adsorbed at liquid-solid interfaces strongly influences the stability and reactivity of solid surfaces. However, its evaluation is challenging in both experiments and theories. In this work, a machine learning aided molecular dynamics method is proposed and applied to oxygen atoms and hydroxyl groups adsorbed on Pt(111) and Pt(100) surfaces in water.

On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations.

The on-the-fly generation of machine-learning force fields by active-learning schemes attracts a great deal of attention in the community of atomistic simulations. The algorithms allow the machine to self-learn an interatomic potential and construct machine-learned models on the fly during simulations. State-of-the-art query strategies allow the machine to judge whether new structures are out of the training data set or not.

Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials.

When determining machine-learning models for inter-atomic potentials, the potential energy surface is often described as a non-linear function of descriptors representing two- and three-body atomic distribution functions. It is not obvious how the choice of the descriptors affects the efficiency of the training and the accuracy of the final machine-learned model. In this work, we formulate an efficient method to calculate descriptors that can separately represent two- and three-body atomic distribution functions, and we examine the effects of including only two- or three-body descriptors, as well as including both, in the regression model.

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference.

Realistic finite temperature simulations of matter are a formidable challenge for first principles methods. Long simulation times and large length scales are required, demanding years of computing time. Here we present an on-the-fly machine learning scheme that generates force fields automatically during molecular dynamics simulations.

Clinical experiences with rabies in cattle in Hungary.

Over the past years, bovine rabies has become a significant problem in Hungary. Not only do foxes play and important role in the transmission of the virus to cattle, but also cats have recently been recognized as an important source of infection in cattle. In the investigated regions, seasonal peaks of transmission could be observed.

Ultrasonographic findings of renal dysplasia in cocker spaniels: eight cases.

A retrospective study of eight young Cocker Spaniels aged 9-24 months was performed to describe the ultrasonographic findings of histologically confirmed renal dysplasia. Ultrasonography revealed kidneys of significantly (p < 0.001) reduced volume in all dogs.

Effect of cholagogues on the volume of the gallbladder of dogs.

The effect of cholagogues on the volume of the gallbladder was studied by two-dimensional ultrasonography in six healthy dogs fasted for 24 hours. The volume was measured immediately before the administration of each test substance and at 10-minute intervals for 120 minutes thereafter. Tap water administered orally as a control did not elicit significant contractions; only the normal cyclic contractions of the gallbladder, occurring at intervals of 60 to 90 minutes, were recorded.

[Cholic acid levels in the serum of dairy cows with metabolically-caused liver diseases].

Three groups of freshly calved dairy cows suffering from fatty liver syndrome of different stages were examined for serum cholic acid (SCA) levels to assess the value of SCA-determination as a diagnostic approach to fatty liver syndrome. A. Comparison of the SCS levels between cows with and without ketonuria: Cows with ketonuria (n = 13) and with elevated plasma ASAT activity, indicating a moderate liver damage, showed a rise of their mean SCA level to 35.

[Blood serum level of primary bile acids in cattle, horses, swine and dogs].

The levels of the two primary bile acids, cholic acid (CA) and chenodeoxycholic acid (CDCA), were determined by radioimmunoassay in cattle, horse, pig and dog serum. The mean serum cholic acid (SCA) and deoxycholic acid (SCDCA) levels of cows varied with their reproductive status, being 7.8 (+/- 3.

Changes in the serum levels of primary bile acids in dairy cows.

Serum cholic acid (SCA) and serum chenodeoxycholic acid (SCDCA) concentrations were determined in healthy dairy cows by radioimmunoassay (RIA). The levels of these two primary bile acids were correlated with the cows' reproductive status. The lowest concentrations were measured in dry cows (SCA: 7.