scholarly journals Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6633
Author(s):  
Simon Anniés ◽  
Chiara Panosetti ◽  
Maria Voronenko ◽  
Dario Mauth ◽  
Christiane Rahe ◽  
...  
Keyword(s):  
Density Functional ◽  
Full Range ◽  
Computational Cost ◽  
Dielectric Constants ◽  
Layer Spacing ◽  
Migration Barriers ◽  
Repulsion Potential ◽  
Equilibrium Configurations ◽  
And Migration ◽  
Non Equilibrium

Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of state of charge (SOC). The performance of approaches based on density functional theory (DFT) varies greatly depending on the choice of functional, and their computational cost is far too high for the large supercells necessary to study dilute and non-equilibrium configurations which are of paramount importance for understanding a complete charging cycle. On the other hand, cheap machine learning methods have made some progress in predicting, e.g., formation energetics, but fail to provide the full picture, including electrostatics and migration barriers. Following up on our previous work, we deliver on the promise of providing a complete and affordable simulation framework for Li-GICs. It is based on density functional tight binding (DFTB), which is fitted to dispersion-corrected DFT data using Gaussian process regression (GPR). In this work, we added the previously neglected lithium–lithium repulsion potential and extend the training set to include superdense Li-GICs (LiC6−x; x>0) and lithium metal, allowing for the investigation of dendrite formation, next-generation modified GIC anodes, and non-equilibrium states during fast charging processes in the future. For an extended range of structural and energetic properties—layer spacing, bond lengths, formation energies and migration barriers—our method compares favorably with experimental results and with state-of-the-art dispersion-corrected DFT at a fraction of the computational cost. We make use of this by investigating some larger-scale system properties—long range Li–Li interactions, dielectric constants and domain-formation—proving our method’s capability to bring to light new insights into the Li-GIC system and bridge the gap between DFT and meso-scale methods such as cluster expansions and kinetic Monte Carlo simulations.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

A complete ab initio thermodynamic and kinetic catalogue of the defect chemistry of hematite α-Fe2O3, its cation diffusion, and sample donor dopants

2021 ◽  
Author(s):  
Shehab Shousha ◽  
Sarah Khalil ◽  
Mostafa Youssef
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Defect Formation ◽  
Defect Chemistry ◽  
Cation Diffusion ◽  
Functional Theory ◽  
Migration Barriers ◽  
Hubbard U ◽  
And Migration ◽  
Formation Energies

This paper studies comprehensively the defect chemistry and cation diffusion in α-Fe2O3. Defect formation energies and migration barriers are calculated using density functional theory with a theoretically calibrated Hubbard U...

scholarly journals An ab initio study of the effect of charge localization on oxygen defect formation and migration energies in magnesium oxide

2011 ◽  
Vol 467 (2131) ◽  
pp. 2054-2065 ◽  
Author(s):  
J. Mulroue ◽  
D. M. Duffy
Keyword(s):  
Density Functional ◽  
Oxygen Vacancies ◽  
Defect Formation ◽  
Frenkel Defect ◽  
Charge Localization ◽  
Migration Barriers ◽  
Pair Defect ◽  
Defect Energies ◽  
And Migration ◽  
Doubly Charged

Plane-wave density functional theory was used to study the properties of oxygen vacancies and interstitials, with different charge states, in MgO. The calculated properties were the relaxed configurations, the Frenkel defect formation energies and the energies of the migration barriers, and all properties were found to be strongly dependent on the defect charge state. The lowest energy configuration of the O 2− interstitial was found to be the cube centre; however, the O − and O 0 interstitials formed dumb-bell configurations. The Frenkel defect energies were also strongly dependent on the defect charge, with the neutral pair energy calculated to be 3 eV lower than the doubly charged Frenkel pair defect energy. The migration barriers of the oxygen vacancies were found to increase as the net charge of the oxygen vacancies decreased, which suggests that vacancies with trapped electrons are much less mobile than the F 2+ vacancies modelled by classical potentials. The migration of the oxygen interstitials showed particularly interesting behaviour. The O 0 interstitial was found to have a higher migration barrier than the O 2− interstitial but a very low barrier (0.06 eV) was found for the O − interstitial. The results have significant implications for the reliability of classical radiation damage simulations.

Electrical conduction along porphyrin wires using the self-consistent extended-Huckel and non-equilibrium Green’s function methods

2012 ◽  
Vol 1414 ◽  
Author(s):  
G. Jones ◽  
M. Elliott ◽  
C. C. Matthai
Keyword(s):  
Optical Properties ◽  
Charge Transport ◽  
Density Functional ◽  
Molecular Conformation ◽  
Computational Cost ◽  
Molecular Wires ◽  
Band Alignment ◽  
Extended Hückel ◽  
Self Consistent ◽  
Non Equilibrium

ABSTRACTIn recent years, first-principle electronic structure calculations have been carried out to investigate such diverse phenomena as charge transport in molecular wires, optical properties of quantum structures and in photonics. However, at this time the prohibitive computational cost does not allow for such calculations to be easily carried out on nano-scale device structures comprising thousands of atoms. In addition, there are issues relating to the applicability of these approaches to describing the excitations that ought to be involved in charge transport.Self-consistent extended Huckel theory (SC-EHT) has proved very effective in describing the band alignment at semiconductor interfaces, and optical properties of partially covered surfaces, as well as being employed in studying the electronic states of large molecules. We have developed a non-equilibrium Greens function (NEGF) SC-EHT code that may be applied to study charge transport through molecular wires. We study the transmission of a porphyrin molecule attached via thiol linkers to gold electrodes, compare our results with those obtained from density functional theory (DFT). We have studied the influence the thiol position on the Au substrate has on the conduction and the dependence of the electron transmission on the molecular conformation. In addition, we also report on the results of some preliminary investigations studying the influence of water on the conduction pathways.

Migration Barriers and Evolution of Mechanical Properties of Oxide Nanoclusters Containing Helium

2015 ◽  
Vol 1743 ◽  
Author(s):  
Thomas Danielson ◽  
Celine Hin
Keyword(s):  
Mechanical Properties ◽  
Density Functional ◽  
Complex Oxide ◽  
Functional Theory ◽  
Ferritic Alloys ◽  
Helium Concentration ◽  
Migration Barriers ◽  
Bcc Iron ◽  
And Migration ◽  
Trapping Sites

ABSTRACTHigh number densities of complex oxide nanoclusters in nanostructured ferritic alloys have been shown to act as effective trapping sites for the transmutation product helium. Density functional theory has been used to investigate the evolution of the mechanical properties of oxide nanoclusters as helium concentration increases. The migration barrier and migration path of helium in the oxide has also been tested in order to make a comparison with the barriers in BCC iron and offer insight to the helium trapping mechanisms of the oxides.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

Ab-Initio Study of Boron Diffusion Retardation in Si1-xGex

2006 ◽  
Vol 912 ◽  
Author(s):  
Yonghyun Kim ◽  
Taras A. Kirichenko ◽  
Sanjay K. Banerjee ◽  
Gyeong S. Hwang
Keyword(s):  
First Principles ◽  
Density Functional ◽  
Density Functional Theory Calculation ◽  
Boron Diffusion ◽  
Ab Initio Study ◽  
Functional Theory ◽  
Migration Barriers ◽  
And Migration ◽  
Theory Calculation ◽  
And Diffusion

AbstractWe study B diffusion in the presence of Ge by using a first principles density functional theory calculation. We investigate the relative stability and migration barriers of Si and Ge interstitials as well as binding energy and diffusion pathway of Boron-Interstitial (BI) pair comprised of Boron and Si or Ge interstitials. We find that Ge interstitials are more stable but less mobile compared to Si interstitials, leading to higher population of interstitials in the implanted Si1-xGex. However, BI pair comprised of Ge interstitial and Boron is less stable compared to Si interstitial –Boron pair and migration barrier of BI pair in presence of Ge is increased, leading to less TED.

General Cluster Sorption Isotherm

2020 ◽  
Author(s):  
Christoph Buttersack
Keyword(s):  
Density Functional ◽  
Capillary Condensation ◽  
Cluster Formation ◽  
Full Range ◽  
Full Description ◽  
Cross Sectional ◽  
Macroporous Silica ◽  
Isotherm Equation ◽  
Type Iv ◽  
Classical Thermodynamics

<p>Adsorption isotherms are an essential tool in chemical physics of surfaces. However, several approaches based on a different theoretical basis exist and for isotherms including capillary condensation existing approaches can fail. Here, a general isotherm equation is derived and applied to literature data both concerning type IV isotherms of argon and nitrogen in ordered mesoporous silica, and type II isotherms of disordered macroporous silica. The new isotherm covers the full range of partial pressure (10<sup>-6</sup> - 0.7). It relies firstly on the classical thermodynamics of cluster formation, secondly on a relationship defining the free energy during the increase of the cluster size. That equation replaces the Lennard-Jones potentials used in the classical density functional theory. The determination of surface areas is not possible by this isotherm because the cross-sectional area of a cluster is unknown. Based on the full description of type IV isotherms, most known isotherms are accessible by respective simplifications. </p>

Evaluating Transition Metal Barrier Heights with the Latest DFT Exchange–Correlation Functionals – the MOBH35 Benchmark Dataset

2019 ◽  
Author(s):  
Mark Iron ◽  
Trevor Janes
Keyword(s):  
Transition Metal ◽  
Density Functional ◽  
Computational Cost ◽  
Basis Set ◽  
Functional Theory ◽  
Exchange Correlation ◽  
Barrier Heights ◽  
Complete Basis Set ◽  
Complete Basis Set Limit ◽  
Hybrid Functionals

A new database of transition metal reaction barrier heights – MOBH35 – is presented. Benchmark energies (forward and reverse barriers and reaction energy) are calculated using DLPNO-CCSD(T) extrapolated to the complete basis set limit using a Weizmann1-like scheme. Using these benchmark energies, the performance of a wide selection of density functional theory (DFT) exchange–correlation functionals, including the latest from the Truhlar and Head-Gordon groups, is evaluated. It was found, using the def2-TZVPP basis set, that the ωB97M-V (MAD 1.8 kcal/mol), ωB97X-V (MAD 2.1 kcal/mol) and SCAN0 (MAD 2.1 kcal/mol) hybrid functionals are recommended. The double-hybrid functionals PWPB95 (MAD 1.6 kcal/mol) and B2K-PLYP (MAD 1.8 kcal/mol) did perform slightly better but this has to be balanced by their increased computational cost.

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