scholarly journals Machine Learning Based Methodology to Predict Point Defect Energies in Multi-Principal Element Alloys

2021 ◽  
Vol 8 ◽  
Author(s):  
Anus Manzoor ◽  
Gaurav Arora ◽  
Bryant Jerome ◽  
Nathan Linton ◽  
Bailey Norman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Point Defect ◽  
Defect Formation ◽  
Binary Alloys ◽  
Face Centered Cubic ◽  
Principal Element ◽  
Defect Energy ◽  
Defect Energies ◽  
And Migration ◽  
The Impact

Multi-principal element alloys (MPEAs) are a new class of alloys that consist of many principal elements randomly distributed on a crystal lattice. The random presence of many elements lends large variations in the point defect formation and migration energies even within a given alloy composition. Compounded by the fact that there could be exponentially large number of MPEA compositions, there is a major computational challenge to capture complete point-defect energy phase-space in MPEAs. In this work, we present a machine learning based framework in which the point defect energies in MPEAs are predicted from a database of their constituent binary alloys. We demonstrate predictions of vacancy migration and formation energies in face centered cubic ternary, quaternary and quinary alloys in Ni-Fe-Cr-Co-Cu system. A key benefit of building this framework based on the database of binary alloys is that it enables defect-energy predictions in alloy compositions that may be unearthed in future. Furthermore, the methodology enables identifying the impact of a given alloying element on the defect energies thereby enabling design of alloys with tailored defect properties.

Point defect formation near the epitaxial Ge(001) growth surface and the impact on phosphorus doping activation

2021 ◽  
Vol 130 (12) ◽  
pp. 125702
Author(s):  
Anurag Vohra ◽  
Geoffrey Pourtois ◽  
Roger Loo ◽  
Wilfried Vandervorst
Keyword(s):  
Point Defect ◽  
Defect Formation ◽  
Growth Surface ◽  
Phosphorus Doping ◽  
The Impact

scholarly journals Extracting bulk defect parameters in silicon wafers using machine learning models

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Yoann Buratti ◽  
Quoc Thong Le Gia ◽  
Josef Dick ◽  
Yan Zhu ◽  
Ziv Hameiri
Keyword(s):  
Machine Learning ◽  
Energy Level ◽  
Cross Section ◽  
Capture Cross Section ◽  
Complex Data ◽  
Capture Cross ◽  
Cross Section Ratio ◽  
Defect Energy ◽  
Section Ratio ◽  
The Impact

Abstract The performance of high-efficiency silicon solar cells is limited by the presence of bulk defects. Identification of these defects has the potential to improve cell performance and reliability. The impact of bulk defects on minority carrier lifetime is commonly measured using temperature- and injection-dependent lifetime spectroscopy and the defect parameters, such as its energy level and capture cross-section ratio, are usually extracted by fitting the Shockley-Read-Hall equation. We propose an alternative extraction approach by using machine learning trained on more than a million simulated lifetime curves, achieving coefficient of determinations between the true and predicted values of the defect parameters above 99%. In particular, random forest regressors, show that defect energy levels can be predicted with a high precision of ±0.02 eV, 87% of the time. The traditional approach of fitting to the Shockley-Read-Hall equation usually yields two sets of defect parameters, one in each half bandgap. The machine learning model is trained to predict the half bandgap location of the energy level, and successfully overcome the traditional approach’s limitation. The proposed approach is validated using experimental measurements, where the machine learning predicts defect energy level and capture cross-section ratio within the uncertainty range of the traditional fitting method. The successful application of machine learning in the context of bulk defect parameter extraction paves the way to more complex data-driven physical models which have the potential to overcome the limitation of traditional approaches and can be applied to other materials such as perovskite and thin film.

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.

scholarly journals Point defect formation and migration in Ga stabilised δ-Pu

2012 ◽  
Vol 423 (1-3) ◽  
pp. 16-21 ◽  
Author(s):  
M. Robinson ◽  
S.D. Kenny ◽  
R. Smith ◽  
M.T. Storr
Keyword(s):  
Point Defect ◽  
Defect Formation ◽  
And Migration

scholarly journals Determination of Vacancy Formation Energies in Binary UZr Alloys Using Special Quasirandom Structure Methods

2021 ◽  
Vol 8 ◽  
Author(s):  
Daniel Vizoso ◽  
Chaitanya Deo
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Defect Formation ◽  
Vacancy Formation ◽  
Random Structure ◽  
Local Configuration ◽  
Random Structures ◽  
And Migration ◽  
The Impact ◽  
Formation Energies

The use of predictive models to examine defect production and migration in metallic systems requires a thorough understanding of the energetics of defect formation and migration. In fully miscible alloys, atomistic properties will all have a range of values that are heavily dependent on local atomic configurations. In this work we have used the atomistic simulation tool Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) to investigate the impact of first nearest neighbor configuration on vacancy formation energies at 0 K in γ-U-Zr alloys of varying Zr concentrations. The properties of randomly generated alloy microstructures were also compared with those produced as special quasi-random structures (SQS) using the “mcsqs” code within the Alloy Theoretic Automated Toolkit. Results have confirmed that local configuration can have a significant impact on measured properties and must be considered when characterizing miscible alloy systems. Results also indicated that the generation method of the random structure (i.e., via random species assignment or a method of enforced randomness) does not result in a measurable difference in average vacancy formation energies in miscible U-Zr systems.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Syrian Crisis and Migration

2015 ◽  
Vol 12 (3) ◽  
pp. 181-192 ◽  
Author(s):  
Pinar Yazgan ◽  
Deniz Eroglu Utku ◽  
Ibrahim Sirkeci
Keyword(s):  
Human Mobility ◽  
Special Issue ◽  
Host Countries ◽  
Syrian Crisis ◽  
Mass Migration ◽  
Large Numbers ◽  
And Migration ◽  
Europe Today ◽  
The Impact ◽  
Near Future

With the growing insurrections in Syria in 2011, an exodus in large numbers have emerged. The turmoil and violence have caused mass migration to destinations both within the region and beyond. The current "refugee crisis" has escalated sharply and its impact is widening from neighbouring countries toward Europe. Today, the Syrian crisis is the major cause for an increase in displacement and the resultant dire humanitarian situation in the region. Since the conflict shows no signs of abating in the near future, there is a constant increase in the number of Syrians fleeing their homes. However, questions on the future impact of the Syrian crisis on the scope and scale of this human mobility are still to be answered. As the impact of the Syrian crisis on host countries increases, so does the demand for the analyses of the needs for development and protection in these countries. In this special issue, we aim to bring together a number of studies examining and discussing human mobility in relation to the Syrian crisis.

Race, Gender, and Border Control in the Western Balkans

2018 ◽  
Author(s):  
Sanja Milivojević
Keyword(s):  
Knowledge Production ◽  
Asylum Seekers ◽  
Former Yugoslavia ◽  
Border Control ◽  
Western Balkans ◽  
Irregular Migrants ◽  
Criminological Inquiry ◽  
Gender And Migration ◽  
And Migration ◽  
The Impact

This chapter looks at the intersection of race, gender, and migration in the Western Balkans. Immobilizing mobile bodies from the Global South has increasingly been the focus of criminological inquiry. Such inquiry, however, has largely excluded the Western Balkans. A difficult place to research, comprising countries of the former Yugoslavia and Albania, the region is the second-largest route for irregular migrants in Europe (Frontex 2016). Indeed, EU expansion and global developments such as wars in Syria, Afghanistan, and Iraq have had a major impact on mobility and migration in the region. The chapter outlines racialized hierarchies in play in contemporary border policing in the region, and how these racialized and gendered practices target racially different Others and women irregular migrants and asylum seekers. Finally, this chapter maps the impact of such practices and calls for a shift in knowledge production in documenting and addressing such discriminatory practices.

Sign in / Sign up

Export Citation Format

Share Document