We present an efficient first-principles based method geared toward reliably predicting the structures of solid materials across the Periodic Table. To this end, we use a density functional theory baseline with a compact, near-minimal min+s basis set, yielding low computational costs and memory demands. Since the use of such a small basis set leads to systematic errors in chemical bond lengths, we develop a linear pairwise correction, available for elements Z = 1-86 (excluding the lanthanide series), parameterized for use with the Perdew-Burke-Ernzerhof exchange-correlation functional. We demonstrate the reliability of this corrected approach for equilibrium volumes across the Periodic Table and the transferability to differently coordinated environments and multi-elemental crystals. We examine relative energies, forces, and stresses in geometry optimizations and molecular dynamics simulations.
Department of Genetics, Animal Breeding and Ethology, Faculty of Animal Science, University of Agriculture in Krakow, al. Mickiewicza 24/28, Krakow 30-059, Poland. Electronic address:
The purpose of the paper was to apply an Artificial Neural Networks with Radial Basis Function to develop an application model for diagnosing a subclinical ketosis type I and II in dairy cattle. While building the neural network model, applied methodology was compatible to the procedures used in Data Mining processes. The data set was created based on the composition of milk samples of 1520 Polish Holstein-Friesian cows.
In this study, modeling and optimization of Hydrothermal Carbonization (HTC) of Poultry litter were conducted to convert it into high-value materials. The aim was to understand the process and predict the effect of the influencing parameters on the product properties. The recovery of Inorganic Phosphorous (IP) and Carbon (C) was regarded as the model's response, although temperature and reaction time were thought to be important variables.
We have investigated the title question for both a subset of the W4-11 total atomization energies benchmark, and for the A24x8 noncovalent interactions benchmark. Overall, counterpoise corrections to post-CCSD(T) contributions are about two orders of magnitude less important than those to the CCSD(T) interaction energy. Counterpoise corrections for connected quadruple substitutions (Q) are negligible, and or especially so.
Anxiety disorders are one of the most common mental health pathologies in the world. They require searc h and development of novel effective pharmacologically active substances. Thus, the development of new approaches to the search for anxiolytic substances by artificial intelligence methods is an important area of modern bioinformatics and pharmacology.
Anuclear blood cells, platelets, are the basis for the formation of blood clots in human vessels. While antiplatelet therapy is most often used after ischemic events, there is a need for its personalization due to the limited effectiveness and risks of bleeding. Previously, we developed a series of computational models to describe intracellular platelet signaling and a set of experimental methods to characterize the platelets of a given patient.
