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Rakshit, Medha; Chowdhury, Suman; Majumdar, Arnab; Banerjee, Dipali; Jana, Debnarayan

doi:10.1016/j.commatsci.2023.112526

Cited by 3 publications

(1 citation statement)

References 94 publications

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“…In a work by Ghosal et al [26], tetragonal monolayers of group-V elements (T-Sb, T-P, T-As and T-Bi) have been used to train machine learned potentials by generating datasets depicting forces, stresses, coordinates and energy at each image of the AIMD simulation which has been used to predict phonon bandstructure, phonon density of states and thermoelectric properties of the materials taken in their study. Similar studies on Ba 2 MnSe 3 and germanium sulfide using ML approach and semiclassical Boltzmann transport equation provided results of thermoelectric figure of merit ∼0.74 and 0.73 respectively at 700 K [27,28].…”

Section: Introductionmentioning

confidence: 56%

Bandgap prediction of non-metallic crystals through machine learning approach

Barman,

Dua,

Sarkar

2024

J. Phys.: Condens. Matter

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The determination of bandgap is the heart of electronic structure of any material and is a crucial factor for thermoelectric performance of it. Due to large amount to data (features) that are related to bandgap are now a days available, it is possible to make use of machine learning approach to predict the bandgap of the material. The study commences by selecting the feature through Pearson correlation study between bandgap and various thermoelectric parameters in non-metallic crystals. Among the forty two parameters available in the dataset, the seebeck coefficient and its corresponding temperatures show high correlation with the bandgap. With these three selected features we have used different machine learning models like multilinear regression, polynomial regression, random forest regression, support vector regression to predict the bandgap. Amongst the different machine learning models considered, random forest regression outperforms the other models to predict the bandgap with R2 value of 97.55 % between actual bandgap and predicted bandgap.

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Section: Introductionmentioning

confidence: 56%

Bandgap prediction of non-metallic crystals through machine learning approach

Barman,

Dua,

Sarkar

2024

J. Phys.: Condens. Matter

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Computational investigation on the structural, electronic and optical characteristics of earth-abundant solar absorbers Cu2BeSnX4 (X= S, Se, Te)

Serai,

Ziane,

Bennacer

et al. 2024

Optik

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Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review

Urata,

Bertani,

Pedone

2024

J Am Ceram Soc.

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The emergence of artificial intelligence has provided efficient methodologies to pursue innovative findings in material science. Over the past two decades, machine‐learning potential (MLP) has emerged as an alternative technology to density functional theory (DFT) and classical molecular dynamics (CMD) simulations for computational modeling of materials and estimation of their properties. The MLP offers more efficient computation compared to DFT, while providing higher accuracy compared to CMD. This enables us to conduct more realistic simulations using models with more atoms and for longer simulation times. Indeed, the number of research studies utilizing MLPs has significantly increased since 2015, covering a broad range of materials and their structures, ranging from simple to complex, as well as various chemical and physical phenomena. As a result, there are high expectations for further applications of MLPs in the field of material science and industrial development. This review aims to summarize the applications, particularly in ceramics and glass science, and fundamental theories of MLPs to facilitate future progress and utilization. Finally, we provide a summary and discuss perspectives on the next challenges in the development and application of MLPs.

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A first-principles study of ternary metal chalcogenides Ba2MnX3 (X
Medha Rakshit,
Suman Chowdhury,
Arnab Majumdar
et al.

Cited by 3 publications

References 94 publications

Bandgap prediction of non-metallic crystals through machine learning approach

Bandgap prediction of non-metallic crystals through machine learning approach

Computational investigation on the structural, electronic and optical characteristics of earth-abundant solar absorbers Cu2BeSnX4 (X= S, Se, Te)

Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review

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A first-principles study of ternary metal chalcogenides Ba2MnX3 (XMedha Rakshit, Suman Chowdhury, Arnab Majumdar et al.

Cited by 3 publications

References 94 publications

Bandgap prediction of non-metallic crystals through machine learning approach

Bandgap prediction of non-metallic crystals through machine learning approach

Computational investigation on the structural, electronic and optical characteristics of earth-abundant solar absorbers Cu2BeSnX4 (X= S, Se, Te)

Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review

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A first-principles study of ternary metal chalcogenides Ba2MnX3 (X
Medha Rakshit,
Suman Chowdhury,
Arnab Majumdar
et al.