The influence of phonon anharmonicity on thermal and elastic properties of neptunium

A self-consistent thermodynamic model describing the thermal and elastic properties of α- and β-phases of neptunium was developed. The presence of strong phonon anharmonicity of Np is established. The obtained results are in good agreement with the experimental data and enable to predict the Np properties in wide temperature range. © 2013 Elsevier B.V. All rights reserved

Thermodynamic simulation of thermophysical and elastic properties of plutonium

A self-consistent thermodynamic model taking into account phonon anharmonicity effects is used for calculating the temperature dependences of the lattice components of thermophysical and elastic properties of the alpha-phase of plutonium. The theoretical curves are in good agreement with experimental data and make it possible to compare the mechanisms of formation of thermophysical and elastic properties of the alpha- and delta-phases of plutonium. The entropy of both phases is estimated, the role of the lattice magnetic anharmonicity is analyzed, and the key question concerning the reasons for stabilization of the delta-phase of plutonium relative to its α-phase is considered. © 2013 Pleiades Publishing, Ltd

A thermodynamic model of thermal end elastic properties of curium

A self-consistent thermodynamic model of curium is developed. In the framework of this model the temperature dependencies of heat capacity, coefficient of thermal expansion, bulk modulus and Debye temperature of Cm are calculated. It is shown that the phonon anharmonicity of Cm is weaker than in the case of Np and δ-Pu, but stronger than in lanthanides. © 2013 Elsevier B.V. All rights reserved

Electron heat capacity and lattice properties of Americium

The temperature dependence of the electron heat capacity of americium is calculated using the concepts on the electronic structure and magnetic properties of this element. The Debye temperature, the thermal expansion coefficient, and the bulk modulus of americium are determined on the basis of the results of calculations and experimental data on heat capacity. © 2013 Pleiades Publishing, Ltd

Machine Learning Methods for Predicting the Lattice Characteristics of Materials

Data on 5244 crystalline compounds from the open AFLOWlib repository are used to build machine learning models, which enable to predict important features of phonon spectrum of a material (Debye temperature and Gruneisen parameter) required for simulation of its lattice properties. We build two types of descriptors: the first one contains data solely on the chemical composition of a compound and the second one incorporates information on the elemental properties of atoms that make up the compound and additionally contains several features regarding its crystal structure. The regression models are built using four popular approaches - gradient boosting (GB), random forests (RF), artificial neural networks (ANN) and support vector machines (SVM). Prior the regression a search for the best values of hyperparameters has been performed for each of the model supplemented with a 5-fold cross validation. We compare prediction accuracy of models based on different methods as well as trained on each of the descriptors. © 2020 IEEE

Study of a spring pendulum sinker harmonic oscillations in LabVIEW software package

В данной статье приведено описание демонстрационной установки «Пружинный маятник», совмещенной с ПК. Колебания груза на пружине при помощи датчика расстояния и блока АЦП визуализируются на экране компьютера. Осциллограммы, выводимые на экране монитора, позволяют сделать сравнительный анализ положения максимумов и минимумов гармонических колебаний смещения, скорости и ускорения колеблющегося груза, а также амплитуд указанных зависимостей. Также можно проследить зависимость частоты собственных колебаний от массы груза и жесткости пружины.This paper describes demonstration installation “Spring pendulum” combined with the PC. Sinker oscillations on the spring with the help of distance sensor and ADC block are visualized on a computer screen. The oscillograms are displayed on the screen that makes it possible to do a comparative analysis of the harmonic displacement oscillations maxima and minima positions, velocity and acceleration of the oscillating sinker, as well as the amplitudes of these mentioned dependencies. The natural frequency dependence on the sinker weight and the stiffness of the spring is also can be observed

Prediction of Thermodynamic and Electronic Properties of Double Half Heusler Alloys Based on Machine Learning Methods

Based on data from the open Aflow database and other sources, a regression model is constructed that is used to predict properties of new class of double half Heusler alloys, such as lattice thermal conductivity, Grüneisen parameter, Debye temperature, band gap, magnetic moment per atom, etc

SIMULATION OF LATTICE PROPERTIES OF SnSe BASED ON AB INITIO CALCULATIONS AND MACHINE LEARNING

We develop a thermodynamic model of the lattice properties of a promising thermoelec-tric material SnSe. The model is based on the results of first principle calculations of the ground state energy of SnSe as well as on the parameters of lattice vibrations estimated using machine learning methods

DEVELOPMENT OF IT SUPPORT FOR NUMERICAL INVESTIGATION OF THE PROPERTIES OF THERMOELECTRIC MATERIALS

The need to study the properties of thermoelectric materials is due to the prospects for the development of this industry. This requires appropriate programs that can interact with each other