Accelerated Data-Driven Accurate Positioning of the Band Edges of MXenes

Mishra, Avanish; Satsangi, Swanti; Rajan, Arunkumar Chitteth; Mizuseki, Hiroshi; Lee, Kwang-Ryeol; Singh, Abhishek K.

doi:10.1021/acs.jpclett.9b00009

Cited by 52 publications

(40 citation statements)

References 39 publications

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“…Taking (Mo 1– x Ti x ) 3 C 2 as an example (Figure a,b), a temperature-dependent ordered configuration becomes stable at x = 2/3, i.e., Mo 2 TiC 2 , in agreement with the previous experimental report by Anasori et al Meanwhile, the high-throughput computation technique has also been successfully applied to predict the catalytic properties of M n +1 X n O 2 via a linear relationship between the hydrogen adsorption free energy difference Δ G H and the oxygen vacancy formation energy E f (Figure c) or via the composition-dependent band-edge position (Figure d) . In brief, with the modularization, integration, and generalization of modern first-principles methodologies in materials design, a combination of high-throughput computation, database construction, and data mining, − is becoming one core topic in designing novel MXenes (Figure e).…”

Section: Summary and Perspectivessupporting

confidence: 85%

Rational Design of Flexible Two-Dimensional MXenes with Multiple Functionalities

et al. 2019

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In the past decade, two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides (MXenes) have attracted attention and interest from the scientific community due to their superior mechanical strength and flexibility, physical/chemical properties, and multiple exciting functionalities. Among these materials, the ingenious and effective combination of the mechanical and functional properties of MXenes provides a promising opportunity for designing flexible and wearable devices. This review summarizes the recent research progress in the structural stabilities, mechanical strength and deformation mechanism, strain-tunable energy storages, and catalytic and thermoelectric properties along with certain strain modifications and strain-controllable electronic/topological properties of MXenes from a combined theoretical and experimental perspective and illustrates their electronic origins. Taking the design principles as a focus, the theoretical predictions provide guidance, while the experimental work gives a thorough validation, thus setting the foundation for the current scientific achievements, challenges, and prospects in the field of MXenes.

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Section: Summary and Perspectivessupporting

confidence: 85%

Rational Design of Flexible Two-Dimensional MXenes with Multiple Functionalities

et al. 2019

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“…Among the three models, GBDT gives the highest prediction accuracy with low variance; the KRR model performs better than LRR. Note that we obtain lower MAE than previous models 26,31,39,49,50 What are the main atomic and structural properties that determine the band-gap and bandedge corrections? The answer is shown in Fig.…”

Section: Resultscontrasting

confidence: 51%

Predicting band gaps and band-edge positions of oxide perovskites using DFT and machine learning

Li¹,

ZigengWang²,

Xiao³

et al. 2020

Preprint

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Density functional theory within the local or semilocal density approximations (DFT-LDA/GGA) has become a workhorse in electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. Accurate prediction of band gaps using firstprinciples methods is time consuming, requiring hybrid functionals, quasi-particle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveilling the main chemical and structural factors involved in this correction is desirable for discovering novel materials in high-throughput calculations. In this direction, we use DFT and machine learning techniques to correct band gaps and band-edge positions of a repre-1 sentative subset of ABO 3 perovskite oxides. Relying on results of HSE06 hybrid functional calculations as target values of band gaps, we find a systematic band gap correction of ∼1.5 eV for this class of materials, where ∼1 eV comes from downward shifting the valence band and ∼0.5 eV from uplifting the conduction band. The main chemical and structural factors determining the band gap correction are determined through a feature selection procedure.

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“…Fueled by the developments in algorithms and advancement in computational resources, data-driven machine learning (ML) methods are revolutionizing the materials science domain. Using these ML-based approaches, there is a rich profusion of prediction models for resource extensive materials properties [1][2][3][4][5][6][7][8][9][10][11]. Among these, lattice thermal conductivity κ l is of great relevance in variety of applications such as thermoelectrics, barrier coatings, and electronic devices [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Guided patchwork kriging to develop highly transferable thermal conductivity prediction models

Juneja

Singh

2020

J. Phys. Mater.

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The machine learning models developed on a dataset comprising particular class of materials show poor transferability across different classes. The problem can be partially solved by increasing the variability in the dataset at the cost of prediction accuracy. To develop a model on a highly variable database, we propose a localized regression based patchwork kriging approach for capturing most of the complex details in the data. In this approach, the data is partitioned into smaller regions with shared patches of few datapoints across the neighboring boundaries. Local regression functions are developed in each partition with a constrain to give similar performance at the boundary. Out of 17 different properties tried for partitioning the data, the decomposition with respect to target output κ l gave local models with unprecedented accuracies. The partitioning with respect to κ l , however, requires its estimate for any unknown compound beforehand. To address this, we developed a global model for the entire database. The global model accurately predicts the order of magnitude of κ l for the compounds in the dataset and hence, directs them towards a particular partition for more accurate prediction. We define this stepwise approach as guided patchwork kriging, which can be applied to develop highly accurate transferable prediction models.

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Accelerated Data-Driven Accurate Positioning of the Band Edges of MXenes

Cited by 52 publications

References 39 publications

Rational Design of Flexible Two-Dimensional MXenes with Multiple Functionalities

Rational Design of Flexible Two-Dimensional MXenes with Multiple Functionalities

Predicting band gaps and band-edge positions of oxide perovskites using DFT and machine learning

Guided patchwork kriging to develop highly transferable thermal conductivity prediction models

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