scholarly journals Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis

2022 ◽  
Vol 202 ◽  
pp. 110938
Author(s):  
Rasmus Tranås ◽  
Ole Martin Løvvik ◽  
Oliver Tomic ◽  
Kristian Berland
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Density Functional Theory ◽  
Principal Component Analysis ◽  
Density Functional ◽  
Lattice Thermal Conductivity ◽  
Principal Component ◽  
Component Analysis ◽  
Active Sampling ◽  
Functional Theory

scholarly journals Ultralow and anisotropic thermal conductivity in semiconductor As2Se3

2018 ◽  
Vol 20 (3) ◽  
pp. 1809-1816 ◽  
Author(s):  
Robert L. González-Romero ◽  
Alex Antonelli ◽  
Anderson S. Chaves ◽  
Juan J. Meléndez
Keyword(s):  
Thermal Conductivity ◽  
Density Functional Theory ◽  
First Principles ◽  
Density Functional ◽  
Lattice Thermal Conductivity ◽  
Boltzmann Transport Equation ◽  
First Principles Calculations ◽  
Boltzmann Transport ◽  
Functional Theory ◽  
Anisotropic Thermal Conductivity

An ultralow lattice thermal conductivity of 0.14 W m−1 K−1 along the b⃑ axis of As2Se3 single crystals was obtained at 300 K by first-principles calculations involving density functional theory and the resolution of the Boltzmann transport equation.

Ultra low lattice thermal conductivity and high carrier mobility of monolayer SnS2and SnSe2: a first principles study

2017 ◽  
Vol 19 (31) ◽  
pp. 20677-20683 ◽  
Author(s):  
Aamir Shafique ◽  
Abdus Samad ◽  
Young-Han Shin
Keyword(s):  
Thermal Conductivity ◽  
Density Functional Theory ◽  
First Principles ◽  
Carrier Mobility ◽  
Density Functional ◽  
Lattice Thermal Conductivity ◽  
Functional Theory ◽  
First Principles Study ◽  
High Carrier Mobility ◽  
High Carrier

Using density functional theory, we systematically investigate the lattice thermal conductivity and carrier mobility of monolayer SnX2(X = S, Se).

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

Physical properties of chalcopyrite-type Cu1−xAgxGaTe2 by first-principles study

2016 ◽  
Vol 30 (30) ◽  
pp. 1650373 ◽  
Author(s):  
Li Xue ◽  
Yi-Ming Ren ◽  
Zheng-Long Hu
Keyword(s):  
Thermal Conductivity ◽  
Elastic Constants ◽  
Density Functional ◽  
Lattice Thermal Conductivity ◽  
Lattice Parameter ◽  
Functional Theory ◽  
Lattice Volume ◽  
Te Material ◽  
Ag Substitution ◽  
Good Agreement

[Formula: see text] is a promising thermoelectric (TE) material for high temperature TE applications. This work systematically investigated the structural, elastic and thermodynamic properties of [Formula: see text] ([Formula: see text] = 0, 0.25, 0.5, 0.75 and 1) by density functional theory. The calculated lattice volume is expanded with the increase of Ag content, but this expansion is anisotropic. The lattice parameter along [Formula: see text]-axis is linear expansion, and along [Formula: see text]-axis is parabolic expansion, which is in good agreement with available experimental data. The phase stability of [Formula: see text] alloy is studied by analyzing the formation energy, cohesive energy and elastic constants. Shear modulus, Young’s modulus, sound velocities, Debye temperature and the minimum thermal conductivity are obtained from the calculated elastic constants. The results show that Ag substitution could reduce the lattice thermal conductivity, which is helpful for improving the TE properties of [Formula: see text].

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