A structure map for AB<sub>2</sub> type 2D materials using high-throughput DFT calculations

Fukuda, Masahiro; Zhang, Jingning; Lee, Yung-Ting; Ozaki, Taisuke

doi:10.1039/d0ma00999g

Cited by 26 publications

(20 citation statements)

References 117 publications

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“…Some previous work by Pettifor et al applied the “structure map” technology and well-classified properties like the heat formation energy, revealing the bonding strength of a structure-dependent p–d orbital. Recently, the structure map technology also predicted the correct 1T or 1H type of most 2D structures, including transition metal dichalcogenides and MXenes; some ML methods also support the structure classification …”

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confidence: 94%

Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction

Liang

Zhu

2019

J. Phys. Chem. Lett.

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In this work, inspired by Phillips's ionicity theory in solid-state physics, we directly sort out the critical factors of the band gap's feature correlations in the machine learning architected with the Lasso algorithm. Even based on a small 2D materials data set, we can fundamentally approach an accurate and rational model about the band gap and exciton binding energy with robust transferability to other databases. Our machine learning outputs can reveal the exact physics pictures behind the predicted quantity as well as the "secondary understanding" of the correlation between the approximated physics models in exciton. This work stresses the significant value of physics endorsement on the machine learning (ML) algorithm and provides a symbolic regression solution for the "few-shot" training scheme for ML technology in materials science. Moreover, physics-inspired secondary understanding could be an essential supplement for ML in scientific research fields.

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confidence: 94%

Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction

Liang

Zhu

2019

J. Phys. Chem. Lett.

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“…This is can be easily understood, as oxides are usually simpler to work with under ambient experimental conditions. Furthermore, a computational high-throughput search of new compositions for the garnet prototype is challenging, as the garnet cubic primitive unit cell contains 80 atoms, which is an order of magnitude larger than most structure prototypes used in recent high-throughput searches such as (double-)perovskites 22,23 , (half)-Heuslers 24 , dichalcogenides 25 , etc.…”

Section: Introductionmentioning

confidence: 99%

Machine learning guided high-throughput search of non-oxide garnets

et al. 2023

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Garnets have found important applications in modern technologies including magnetorestriction, spintronics, lithium batteries, etc. The overwhelming majority of experimentally known garnets are oxides, while explorations (experimental or theoretical) for the rest of the chemical space have been limited in scope. A key issue is that the garnet structure has a large primitive unit cell, requiring a substantial amount of computational resources. To perform a comprehensive search of the complete chemical space for new garnets, we combine recent progress in graph neural networks with high-throughput calculations. We apply the machine learning model to identify the potentially (meta-)stable garnet systems before performing systematic density-functional calculations to validate the predictions. We discover more than 600 ternary garnets with distances to the convex hull below 100 meV ⋅ atom−1. This includes sulfide, nitride, and halide garnets. We analyze their electronic structure and discuss the connection between the value of the electronic band gap and charge balance.

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“…Due to the fact that they find application in a wide range of technological areas, they have been the focus of intense research more recently (for example, [5][6][7][8][9][10]). Stable TMDCs (approximately 60 in number) have the generic formula AB 2 , [11,12], where A is a transition metal and B is chalcogen (S, Se, Te). These materials are sometimes described with formulae such as ME 2 , MX 2 , and MCh 2 (M = transition metal and X, Ch, E = chalcogen derivative) [3,[13][14][15], among others.…”

Section: Introductionmentioning

confidence: 99%

Chalcogen Bonding in the Molecular Dimers of WCh2 (Ch = S, Se, Te): On the Basic Understanding of the Local Interfacial and Interlayer Bonding Environment in 2D Layered Tungsten Dichalcogenides

Varadwaj

Marques

et al. 2022

IJMS

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Layered two-dimensional transition metal dichalcogenides and their heterostructures are of current interest, owing to the diversity of their applications in many areas of materials nanoscience and technologies. With this in mind, we have examined the three molecular dimers of the tungsten dichalcogenide series, (WCh2)2 (Ch = S, Se, Te), using density functional theory to provide insight into which interactions, and their specific characteristics, are responsible for the interfacial/interlayer region in the room temperature 2H phase of WCh2 crystals. Our calculations at various levels of theory suggested that the Te···Te chalcogen bonding in (WTe2)2 is weak, whereas the Se···Se and S···S bonding interactions in (WSe2)2 and (WS2)2, respectively, are of the van der Waals type. The presence and character of Ch···Ch chalcogen bonding interactions in the dimers of (WCh2)2 are examined with a number of theoretical approaches and discussed, including charge-density-based approaches, such as the quantum theory of atoms in molecules, interaction region indicator, independent gradient model, and reduced density gradient non-covalent index approaches. The charge-density-based topological features are shown to be concordant with the results that originate from the extrema of potential on the electrostatic surfaces of WCh2 monomers. A natural bond orbital analysis has enabled us to suggest a number of weak hyperconjugative charge transfer interactions between the interacting monomers that are responsible for the geometry of the (WCh2)2 dimers at equilibrium. In addition to other features, we demonstrate that there is no so-called van der Waals gap between the monolayers in two-dimensional layered transition metal tungsten dichalcogenides, which are gapless, and that the (WCh2)2 dimers may be prototypes for a basic understanding of the physical chemistry of the chemical bonding environments associated with the local interfacial/interlayer regions in layered 2H-WCh2 nanoscale systems.

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A structure map for AB₂ type 2D materials using high-throughput DFT calculations

Abstract: A structure map for AB2 type monolayers of 3844 compounds is constructed by high-throughput DFT calculations with the symmetry-unconstrained geometry optimizations starting from ferromagnetic 1T, 1H and planar structures as initial states.

Cited by 26 publications

References 117 publications

Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction

Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction

Machine learning guided high-throughput search of non-oxide garnets

Chalcogen Bonding in the Molecular Dimers of WCh2 (Ch = S, Se, Te): On the Basic Understanding of the Local Interfacial and Interlayer Bonding Environment in 2D Layered Tungsten Dichalcogenides

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A structure map for AB2 type 2D materials using high-throughput DFT calculations

Abstract: A structure map for AB2 type monolayers of 3844 compounds is constructed by high-throughput DFT calculations with the symmetry-unconstrained geometry optimizations starting from ferromagnetic 1T, 1H and planar structures as initial states.

Cited by 26 publications

References 117 publications

Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction

Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction

Machine learning guided high-throughput search of non-oxide garnets

Chalcogen Bonding in the Molecular Dimers of WCh2 (Ch = S, Se, Te): On the Basic Understanding of the Local Interfacial and Interlayer Bonding Environment in 2D Layered Tungsten Dichalcogenides

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A structure map for AB₂ type 2D materials using high-throughput DFT calculations