Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning

2019 ◽  
Vol 21 (3) ◽  
pp. 1078-1088 ◽  
Author(s):  
Heesoo Park ◽  
Raghvendra Mall ◽  
Fahhad H. Alharbi ◽  
Stefano Sanvito ◽  
Nouar Tabet ◽  
...  
Keyword(s):  
Machine Learning ◽  
New Approaches ◽  
Halide Perovskites ◽  
Light Absorbers

Recent years have witnessed a growing effort in engineering and tuning the properties of hybrid halide perovskites as light absorbers.

scholarly journals Memristors for the Curious Outsiders

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 118 ◽  
Author(s):  
Francesco Caravelli ◽  
Juan Carbajal
Keyword(s):  
Machine Learning ◽  
Physical Mechanism ◽  
Neural Computation ◽  
Dynamic Resistance ◽  
Passive Component ◽  
Internal Parameter ◽  
New Approaches ◽  
Memristive Behavior ◽  
Memristive Circuits

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.

scholarly journals Lead-Free Halide Double Perovskites: A Review of the Structural, Optical, and Stability Properties as Well as Their Viability to Replace Lead Halide Perovskites

Metals ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 667 ◽  
Author(s):  
Edson Meyer ◽  
Dorcas Mutukwa ◽  
Nyengerai Zingwe ◽  
Raymond Taziwa
Keyword(s):  
Solar Cells ◽  
Perovskite Solar Cells ◽  
Lead Free ◽  
Double Perovskites ◽  
Stability Properties ◽  
Perovskite Materials ◽  
Halide Perovskites ◽  
Halide Perovskite ◽  
Light Absorbers ◽  
Lead Halide

Perovskite solar cells employ lead halide perovskite materials as light absorbers. These perovskite materials have shown exceptional optoelectronic properties, making perovskite solar cells a fast-growing solar technology. Perovskite solar cells have achieved a record efficiency of over 20%, which has superseded the efficiency of Gräztel dye-sensitized solar cell (DSSC) technology. Even with their exceptional optical and electric properties, lead halide perovskites suffer from poor stability. They degrade when exposed to moisture, heat, and UV radiation, which has hindered their commercialization. Moreover, halide perovskite materials consist of lead, which is toxic. Thus, exposure to these materials leads to detrimental effects on human health. Halide double perovskites with A2B′B″X6 (A = Cs, MA; B′ = Bi, Sb; B″ = Cu, Ag, and X = Cl, Br, I) have been investigated as potential replacements of lead halide perovskites. This work focuses on providing a detailed review of the structural, optical, and stability properties of these proposed perovskites as well as their viability to replace lead halide perovskites. The triumphs and challenges of the proposed lead-free A2B′B″X6 double perovskites are discussed here in detail.

Some New Approaches to Machine Learning

1969 ◽  
pp. 304-324 ◽  
Author(s):  
Nicholas V. Findler
Keyword(s):  
Machine Learning ◽  
New Approaches

Bandgap prediction of metal halide perovskites using regression machine learning models

2021 ◽  
pp. 127800
Author(s):  
V. Vakharia ◽  
Ivano E. Castelli ◽  
Keval Bhavsar ◽  
Ankur Solanki
Keyword(s):  
Machine Learning ◽  
Metal Halide ◽  
Learning Models ◽  
Halide Perovskites ◽  
Machine Learning Models

scholarly journals Morphology and Interaction of Galaxies using Deep Learning

2016 ◽  
Vol 12 (S325) ◽  
pp. 205-208
Author(s):  
Fernando Caro ◽  
Marc Huertas-Company ◽  
Guillermo Cabrera
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Visual Inspection ◽  
Interaction Analysis ◽  
Machine Learning Techniques ◽  
Huge Amount ◽  
New Approaches ◽  
Learning Techniques ◽  
First Galaxies ◽  
The Way

AbstractIn order to understand how galaxies form and evolve, the measurement of the parameters related to their morphologies and also to the way they interact is one of the most relevant requirements. Due to the huge amount of data that is generated by surveys, the morphological and interaction analysis of galaxies can no longer rely on visual inspection. For dealing with such issue, new approaches based on machine learning techniques have been proposed in the last years with the aim of automating the classification process. We tested Deep Learning using images of galaxies obtained from CANDELS to study the accuracy achieved by this tool considering two different frameworks. In the first, galaxies were classified in terms of their shapes considering five morphological categories, while in the second, the way in which galaxies interact was employed for defining other five categories. The results achieved in both cases are compared and discussed.

scholarly journals Method of detecting virus-encoders in computer system using analysis of their behavior

2020 ◽  
Vol 2020 (1) ◽  
pp. 41-49
Author(s):  
Arthur Kaliev ◽  
Alexandr Marenkov
Keyword(s):  
Machine Learning ◽  
Computer System ◽  
Gradient Boosting ◽  
Support Vector ◽  
Model Quality ◽  
Network Support ◽  
New Approaches ◽  
Model Training ◽  
Low Efficiency ◽  
A New Technique

The article considers the low efficiency of existing methods of ransomware fighting. The importance of developing new approaches to the ransomware identification in computer systems (CS) is substantiated. Heuristic analysis methods are considered as new approaches to ransomware detecting. A new technique for ransomware detecting is based on the analysis of changes in CS parameters. Using machine-learning methods there have been constructed models, which allow detecting ransomware attacks on the computer system. The aim of the experiment was to obtain a model that has the highest percentage of ransomware attacks detection and the least number of false triggering. The machine learning lgorithms used for research are the following: naive Bayes classifier, multilayer neural network, support vector machine, CatBoost gradient boosting algorithm. To build the models training datasets written in Python programming language were used. The raining datasets were collected as a result of experiments with the most popular virus-encoders. The following typical metrics were selected as key metrics for the effectiveness of machine learning models: precision, recall, F1-metric, accuracy, AUC. In the course of experiments, the values of the error matrices were formed and the main indicators of the model quality metrics were obtained. In addition to the classification efficiency metrics, the average time for performing classification operations for each of the models is given. During the process of model training and testing it was revealed that the best model for detecting ransomware is that built on the CatBoost algorithm. The conclusions were drawn about the possibility of applying the approach to detect the ransomware attacks on various computer systems.

scholarly journals A Review on Lead-Free Hybrid Halide Perovskites as Light Absorbers for Photovoltaic Applications Based on Their Structural, Optical, and Morphological Properties

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 5039
Author(s):  
Shadrack J. Adjogri ◽  
Edson L. Meyer
Keyword(s):  
Perovskite Solar Cells ◽  
Lead Free ◽  
Morphological Properties ◽  
Chemical Compositions ◽  
Hybrid Perovskite ◽  
Photovoltaic Applications ◽  
Halide Perovskites ◽  
Halide Perovskite ◽  
The Stability ◽  
Light Absorbers

Despite the advancement made by the scientific community in the evolving photovoltaic technologies, including the achievement of a 29.1% power conversion efficiency of perovskite solar cells over the past two decades, there are still numerous challenges facing the advancement of lead-based halide perovskite absorbers for perovskite photovoltaic applications. Among the numerous challenges, the major concern is centered around the toxicity of the emerging lead-based halide perovskite absorbers, thereby leading to drawbacks for their pragmatic application and commercialization. Hence, the replacement of lead in the perovskite material with non-hazardous metal has become the central focus for the actualization of hybrid perovskite technology. This review focuses on lead-free hybrid halide perovskites as light absorbers with emphasis on how their chemical compositions influence optical properties, morphological properties, and to a certain extent, the stability of these perovskite materials.

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