scholarly journals Deformation potential extraction and computationally efficient mobility calculations in silicon from first principles

2021 ◽  
Vol 104 (19) ◽  
Author(s):  
Zhen Li ◽  
Patrizio Graziosi ◽  
Neophytos Neophytou
Keyword(s):  
First Principles ◽  
Deformation Potential ◽  
Computationally Efficient

scholarly journals When band convergence is not beneficial for thermoelectrics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Junsoo Park ◽  
Maxwell Dylla ◽  
Yi Xia ◽  
Max Wood ◽  
G. Jeffrey Snyder ◽  
...  
Keyword(s):  
Charge Carrier ◽  
First Principles ◽  
Design Principle ◽  
Phonon Scattering ◽  
Charge Carrier Concentration ◽  
Deformation Potential ◽  
Interband Scattering ◽  
Improved Performance ◽  
Full Heusler ◽  
Electron Phonon Scattering

AbstractBand convergence is considered a clear benefit to thermoelectric performance because it increases the charge carrier concentration for a given Fermi level, which typically enhances charge conductivity while preserving the Seebeck coefficient. However, this advantage hinges on the assumption that interband scattering of carriers is weak or insignificant. With first-principles treatment of electron-phonon scattering in the CaMg2Sb2-CaZn2Sb2 Zintl system and full Heusler Sr2SbAu, we demonstrate that the benefit of band convergence can be intrinsically negated by interband scattering depending on the manner in which bands converge. In the Zintl alloy, band convergence does not improve weighted mobility or the density-of-states effective mass. We trace the underlying reason to the fact that the bands converge at a one k-point, which induces strong interband scattering of both the deformation-potential and the polar-optical kinds. The case contrasts with band convergence at distant k-points (as in the full Heusler), which better preserves the single-band scattering behavior thereby successfully leading to improved performance. Therefore, we suggest that band convergence as thermoelectric design principle is best suited to cases in which it occurs at distant k-points.

Thermoelectric properties of organic charge transfer salts from first-principle investigations: Role of molecular packing and triiodide anions

2022 ◽  
Author(s):  
Yishan Wang ◽  
Meng Zhao ◽  
Hu Zhao ◽  
Shuzhou Li ◽  
Jia Zhu ◽  
...  
Keyword(s):  
Charge Transfer ◽  
First Principles ◽  
Transport Theory ◽  
Deformation Potential ◽  
First Principles Calculations ◽  
Boltzmann Transport ◽  
Boltzmann Transport Theory ◽  
Charge Transfer Salts ◽  
Deformation Potential Theory

The potency of charge transfer (CT) salts in thermoelectric (TE) applications based on (5-CNB-EDT-TTF)4I3 is systematically explored by first-principles calculations combined with Boltzmann transport theory and deformation potential theory, focusing...

Fundamentals of Atomistic Simulations

2006 ◽  
Author(s):  
Vasily Bulatov ◽  
Wei Cai
Keyword(s):  
First Principles ◽  
Large Scale ◽  
Dislocation Core ◽  
Boltzmann Distribution ◽  
Atomistic Simulations ◽  
Computationally Efficient ◽  
Interacting Electrons ◽  
Subsequent Discussion ◽  
Necessary And Sufficient ◽  
Interacting Atoms

Fundamentally, materials derive their properties from the interaction between their constituent atoms. These basic interactions make the atoms assemble in a particular crystalline structure. The same interactions also define how the atoms prefer to arrange themselves in the dislocation core. Therefore, to understand the behavior of dislocations, it is necessary and sufficient to study the collective behavior of atoms in crystals populated by dislocations. This chapter introduces the basic methodology of atomistic simulations that will be applied to the studies of dislocations in the following chapters. Section 1 discusses the nature of interatomic interactions and introduces empirical models that describe these interactions with various degrees of accuracy. Section 2 introduces the significance of the Boltzmann distribution that describes statistical properties of a collection of interacting atoms in thermal equilibrium. This section sets the stage for a subsequent discussion of basic computational methods to be used throughout this book. Section 3 covers the methods for energy minimization. Sections 4 and 5 give a concise introduction to Monte Carlo and molecular dynamics methods. When put close together, atoms interact by exerting forces on each other. Depending on the atomic species, some interatomic interactions are relatively easy to describe, while others can be very complicated. This variability stems from the quantum mechanical motion and interaction of electrons [15, 16]. Henceforth, rigorous treatment of interatomic interactions should be based on a solution of Schrödinger’s equation for interacting electrons, which is usually referred to as the first principles or ab initio theory. Numerical calculations based on first principles are computationally very expensive and can only deal with a relatively small number of atoms. In the context of dislocation modelling, relevant behaviors often involve many thousands of atoms and can only be approached using much less sophisticated but more computationally efficient models. Even though we do not use it in this book, it is useful to bear in mind that the first principles theory provides a useful starting point for constructing approximate but efficient models that are needed to study large-scale problems involving many atoms.

scholarly journals Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Haoyue Guo ◽  
Qian Wang ◽  
Annika Stuke ◽  
Alexander Urban ◽  
Nongnuch Artrith
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
First Principles ◽  
Reference Method ◽  
Atomic Scale ◽  
Chemical Compositions ◽  
Computationally Efficient ◽  
Battery Materials ◽  
Scale Modeling ◽  
Solid State Batteries

Materials for solid-state batteries often exhibit complex chemical compositions, defects, and disorder, making both experimental characterization and direct modeling with first principles methods challenging. Machine learning (ML) has proven versatile for accelerating or circumventing first-principles calculations, thereby facilitating the modeling of materials properties that are otherwise hard to access. ML potentials trained on accurate first principles data enable computationally efficient linear-scaling atomistic simulations with an accuracy close to the reference method. ML-based property-prediction and inverse design techniques are powerful for the computational search for new materials. Here, we give an overview of recent methodological advancements of ML techniques for atomic-scale modeling and materials design. We review applications to materials for solid-state batteries, including electrodes, solid electrolytes, coatings, and the complex interfaces involved.

scholarly journals LaPtSb: a half-Heusler compound with high thermoelectric performance

2016 ◽  
Vol 18 (27) ◽  
pp. 17912-17916 ◽  
Author(s):  
Q. Y. Xue ◽  
H. J. Liu ◽  
D. D. Fan ◽  
L. Cheng ◽  
B. Y. Zhao ◽  
...  
Keyword(s):  
Transport Properties ◽  
Potential Theory ◽  
First Principles ◽  
Thermoelectric Performance ◽  
Deformation Potential ◽  
First Principles Calculations ◽  
Heusler Compound ◽  
Deformation Potential Theory

The electronic and transport properties of the half-Heusler compound LaPtSb are investigated by performing first-principles calculations combined with semi-classical Boltzmann theory and deformation potential theory.

scholarly journals Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jose Antonio Garrido Torres ◽  
Vahe Gharakhanyan ◽  
Nongnuch Artrith ◽  
Tobias Hoffmann Eegholm ◽  
Alexander Urban
Keyword(s):  
Machine Learning ◽  
High Temperature ◽  
First Principles ◽  
Temperature Effects ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Free Energies ◽  
Computationally Efficient ◽  
Machine Learning Model ◽  
Energy Economy

AbstractThe prediction of temperature effects from first principles is computationally demanding and typically too approximate for the engineering of high-temperature processes. Here, we introduce a hybrid approach combining zero-Kelvin first-principles calculations with a Gaussian process regression model trained on temperature-dependent reaction free energies. We apply this physics-based machine-learning model to the prediction of metal oxide reduction temperatures in high-temperature smelting processes that are commonly used for the extraction of metals from their ores and from electronics waste and have a significant impact on the global energy economy and greenhouse gas emissions. The hybrid model predicts accurate reduction temperatures of unseen oxides, is computationally efficient, and surpasses in accuracy computationally much more demanding first-principles simulations that explicitly include temperature effects. The approach provides a general paradigm for capturing the temperature dependence of reaction free energies and derived thermodynamic properties when limited experimental reference data is available.

Oxygen-induced degradation of the electronic properties of thin-layer InSe

2018 ◽  
Vol 20 (4) ◽  
pp. 2238-2250 ◽  
Author(s):  
Xin Wei ◽  
Chaofang Dong ◽  
Aoni Xu ◽  
Xiaogang Li ◽  
Digby D. Macdonald
Keyword(s):  
Band Structure ◽  
Thin Layer ◽  
Electronic Properties ◽  
Potential Theory ◽  
Structural Relaxation ◽  
First Principles ◽  
Carrier Mobility ◽  
Deformation Potential ◽  
First Principles Calculations ◽  
Deformation Potential Theory

The degradation of thin-layer InSe induced by O atoms was quantificationally studied by first-principles calculations and deformation potential theory from the aspects of structural relaxation, band structure, and carrier mobility.

Sign in / Sign up

Export Citation Format

Share Document