Periodic table of elements and nanotechnology

Semenova, Anna A.; Tarasov, Alexey B.; Goodilin, Eugene A.

doi:10.1016/j.mencom.2019.09.001

Cited by 14 publications

(4 citation statements)

References 177 publications

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“…Subsequently, feature engineering was performed to convert elemental electronic properties and structural site information into alloy compound features using open-source data packages and toolkits (detailed in section 3.2). [42][43][44] After feature engineering, the dataset included 280 features, which constituted 3 structural site features and 277 elemental properties. The structural site features were encoded from the adsorption site on the surface of alloys.…”

Section: Resultsmentioning

confidence: 99%

“…The feature engineering transformed the individual electronic and elemental properties into compound features and created structural adsorption site features from the dataset. To create dependable compound features, the pertinent properties of different electrons and elements were queried from the Mendeleev, 42 Matminter platform, 43 and online WebElements. 44 The attributes of compound electronic and elemental features were designed by following three aspects, according to the open-source material property datasets, online material websites, published literature on elemental properties, and scientific data processing methods.…”

Section: Structural and Electronic Feature Engineeringmentioning

confidence: 99%

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Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys

et al. 2023

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Section: Resultsmentioning

confidence: 99%

Section: Structural and Electronic Feature Engineeringmentioning

confidence: 99%

Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys

et al. 2023

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“…The maximum, minimum, mean, average deviation, and other figures of atomic weight, melting temperature, covalent radius, and electronegativity were derived from Matminer. In addition, the properties, such as "Atomic Volume", "Vdw Radius", "Density", "Lattice Parameter", "Specific Heat", and "Dipole Polarizability", were acquired by using Python in the Mendeleev 44 module. The Mendeleev package provided all the functionality for each single element.…”

Section: Building the Data Set And Data Processingmentioning

confidence: 99%

Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit

Zhang

et al. 2022

ACS Appl. Mater. Interfaces

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The figure of merit (zT) is a key parameter to measure the performance of thermoelectric materials. At present, the prediction of zT values via machine leaning has emerged as a promising method for exploring high-performance materials. However, the machine learning-based predictions still suffer from unsatisfactory accuracy, and this is related to the size of the data set, the hyperparameters of models, and the quality of the data. In this work, 5038 pieces of data of thermoelectric materials were selected, and several regression models were generated to predict zT values. This large data set-driven light gradient boosting (LGB) model with 57 features performed with an excellent accuracy, achieving a coefficient of determination (R 2) value of 0.959, a root mean squared error (RMSE) of 0.094, a mean absolute error (MAE) of 0.057, and a correlation coefficient (R) of 0.979. Owing to the large size of the data set, the prediction accuracy exceeds that of most reported zT predictions via machine learning. The “ME Lattice Parameter” was verified as the most important feature in the zT prediction. Furthermore, nine potential candidates were screened out from among one million pieces of data. This study solves the problem of the data set size, adjusts the hyperparameters of the models, uses feature engineering to improve data quality, and provides an efficient strategy to perform wide-ranging screening for promising materials.

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“…Lesson number one focused on the Chemical Elements and the Periodic Table, in order to celebrate the 150th anniversary of the Periodic Table of Chemical Elements and also the "International Year of the Periodic Table of Chemical Elements (IYPT2019)" declared by the United Nations General Assembly and UNESCO. The development of the Periodic Table of Chemical Elements is one of the most significant achievements in science and a consolidating scientific concept with broad implications in chemistry, physics, biology and other sciences (Imberti & Sadler, 2020;Semenova, Tarasov, & Goodilin, 2019). Consolidate, deepen and expand knowledge by understanding concepts related to the Periodic Table provides the students a solid base of knowledge of chemistry and science values.…”

Section: Project Descriptionmentioning

confidence: 99%

Molecular School – a pre-university chemistry school

Nogueira

Silva

Mendes

et al. 2021

Chemistry Teacher International

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The planning, implementation and results of the first edition of the Molecular School are presented, as the first pre-university school project held in Portugal. This is not, however, a strictly Portuguese project, since it can be replicated in other countries at the secondary school level, with minor adjustments. Herein, the pilot edition of Molecular School is detailed and discussed, where 36 secondary school students have participated. The plan for the second edition, to be held in the first semester of 2021, with the confirmed participation of around 100 students, is further presented. Briefly, the project is divided in two modules: theoretical and laboratory work. These were prepared in a complementary way and performed to achieve the same purpose: deliver a wider vision of what chemistry really is. Hence, the classes were designed having in mind the applications that chemistry has in our everyday life, in the different academic research fields and in industry. A better preparation and training at the laboratory level was also a goal of this project. The enthusiasm, happiness and the motivation shown by the students, and their eagerness to participate in the future editions of the Molecular School, were clear signs of this project success.

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Periodic table of elements and nanotechnology

Cited by 14 publications

References 177 publications

Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys

Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys

Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit

Molecular School – a pre-university chemistry school

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