Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

Choudhary, Kamal; Garrity, Kevin F.; Hartman, Steven; Pilania, Ghanshyam; Tavazza, Francesca

doi:10.1103/physrevmaterials.7.014009

Cited by 13 publications

(18 citation statements)

References 63 publications

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“…We also use another prediction scheme where we start from what is expected (defined research targets, e.g., defined band alignment) to obtain label values in line with expectations (sample characteristics suiting the actual research target). This prediction scheme is breaking with the usual trend of ML works (from one material to its properties) mentioned earlier. ,,,,, Heterostructures from the literature, similar to those that the meta-estimator can predict, are shown, highlighting the relevance of the model to the field.…”

Section: Introductionmentioning

confidence: 69%

“…Heterostructure samples were produced applying Anderson’s rule . This assumption has been previously used to predict heterostructure properties with ML approaches . From 410 2D materials that were taken at random and reproduced from the database, 79,003 heterostructures were created.…”

Section: Methodsmentioning

confidence: 99%

“…Other works focused on the prediction of properties for different types of heterojunctions by choosing several well-known 2D materials, losing in generalization power . Sampling a large part of the material space following Anderson’s rule for band alignment has also been used in previous work . Another model has been trained to determine the heterojunction type (I, II, or III) obtained by combination of materials .…”

Section: Introductionmentioning

confidence: 99%

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Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design

Botella,

Kistanov,

Cao

2023

J. Chem. Inf. Model.

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Two-dimensional (2D) semiconductors are central to many scientific fields. The combination of two semiconductors (heterostructure) is a good way to lift many technological deadlocks. Although ab initio calculations are useful to study physical properties of these composites, their application is limited to few heterostructure samples. Herein, we use machine learning to predict key characteristics of 2D materials to select relevant candidates for heterostructure building. First, a label space is created with engineered labels relating to atomic charge and ion spatial distribution. Then, a meta-estimator is designed to predict label values of heterostructure samples having a defined band alignment (descriptor). To this end, independently trained k-nearest neighbors (KNN) regression models are combined to boost the regression. Then, swarm intelligence principles are used, along with the boosted estimator’s results, to further refine the regression. This new “swarm smart” algorithm is a powerful and versatile tool to select, among experimentally existing, computationally studied, and not yet discovered van der Waals heterostructures, the most likely candidate materials to face the scientific challenges ahead.

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Section: Introductionmentioning

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design

Botella,

Kistanov,

Cao

2023

J. Chem. Inf. Model.

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“…To compute the relaxed heterostructures of Cr 2 Te 3 with 2D materials, the relative orientation of the two materials in the calculation was not taken from an experiment, but chosen in a systematic way [34] to minimize the lattice mismatch. This captures more realistically the weak epitaxy of the heterostructure.…”

Section: B Calculation Methodsmentioning

confidence: 99%

Epitaxial van der Waals heterostructures of Cr2Te3 on 2D materials

Guillet,

Vojacek,

Dosenovic

et al. 2023

Preprint

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Achieving large-scale growth of two-dimensional (2D) ferromagnetic materials with high Curie temperature (TC) and perpendicular magnetic anisotropy (PMA) is highly desirable for the development of ultra-compact magnetic sensors and magnetic memories. In this context, van der Waals (vdW) Cr2Te3 appears as a promising candidate. Bulk Cr2Te3 exhibits strong PMA and a TC of 180 K. Moreover, both PMA and TC might be adjusted in ultrathin films by engineering composition, strain, or applying an electric field. In this work, we demonstrate the molecular beam epitaxy (MBE) growth of vdW heterostructures of five-monolayer quasi-freestanding Cr2Te3 on three classes of 2D materials: graphene (semimetal), WSe2 (semiconductor) and Bi2Te3 (topological insulator). By combining structural and chemical analysis down to the atomic level with ab initio calculations, we confirm the single crystalline character of Cr2Te3 films on the 2D materials with sharp vdW interfaces. They all exhibit PMA and TC close to the bulk Cr2Te3 value of 180 K. Ab initio calculations confirm this PMA and show how its strength depends on strain. Finally, Hall measurements reveal a strong anomalous Hall effect, which changes sign at a given temperature. We theoretically explain this effect by a sign change of the Berry phase close to the Fermi level. This transition temperature depends on the 2D material in proximity, notably as a consequence of charge transfer. MBE-grown Cr2Te3/2D material bilayers constitute model systems for the further development of spintronic devices combining PMA, large spin-orbit coupling and sharp vdW interface.

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“…Furthermore, machine learning techniques have become popular for direct structure to property prediction, accelerating conventional methods such as DFT. Thus, we performed machine learning inference for selected properties of our model catalyst systems using a model trained on the JARVIS-DFT 2D data set that is arranged by the Materials Genome Initiative (MGI) under the National Institute of Standards and Technology (NIST) …”

Section: Introductionmentioning

confidence: 99%

2D–2D Nanoheterostructure of an Exposed {001}-Facet CuO and MoS₂ Based Bifunctional Catalyst Showing Excellent Surface Chemistry and Conductivity for Cathodic CO₂ Reduction

Akhond,

Islam,

Irfan

et al. 2023

ACS Omega

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A novel CuO-MoS 2 based heterostructure catalyst model system is proposed where a CuO nanosheet with exposed {001} facet with proper termination is the active surface for the catalysis and a MoS 2 nanosheet is the supporting layer. Density functional theory (DFT) calculations were performed to validate the model. The MoS 2 bilayer forms a stable heterostructure with {001} faceted CuO with different terminations exposing oxygen and copper atoms (active sites) on the surface. The heterostructure active sites with a low oxidation state of the copper atoms and subsurface oxygen atoms provide a suitable chemical environment for the selective production of multicarbon products from CO 2 electrocatalytic reduction. Furthermore, our heterostructure model exhibits good electrical conductivity, efficient electron transport to active surface sites, and less interfacial resistance compared to similar heterostructure systems. Additionally, we propose a photoenhanced electrocatalysis mechanism due to the photoactive nature of MoS 2 . We suggest that the photogenerated carrier separation occurs because of the interface-induced dipole. Moreover, we utilized a machine learning model trained on a 2D DFT materials database to predict selected properties and compared them with the DFT results. Overall, our study provides insights into the structure− property relationship of a MoS 2 supported 2D CuO nanosheet based bifunctional catalyst and highlights the advantages of heterostructure formation with selective morphology and properly terminated surface in tuning the catalytic performance of nanocomposite materials.

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Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

Cited by 13 publications

References 63 publications

Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design

Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design

Epitaxial van der Waals heterostructures of Cr2Te3 on 2D materials

2D–2D Nanoheterostructure of an Exposed {001}-Facet CuO and MoS₂ Based Bifunctional Catalyst Showing Excellent Surface Chemistry and Conductivity for Cathodic CO₂ Reduction

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Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

Cited by 13 publications

References 63 publications

Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design

Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design

Epitaxial van der Waals heterostructures of Cr2Te3 on 2D materials

2D–2D Nanoheterostructure of an Exposed {001}-Facet CuO and MoS2 Based Bifunctional Catalyst Showing Excellent Surface Chemistry and Conductivity for Cathodic CO2 Reduction

Contact Info

Product

Resources

About

2D–2D Nanoheterostructure of an Exposed {001}-Facet CuO and MoS₂ Based Bifunctional Catalyst Showing Excellent Surface Chemistry and Conductivity for Cathodic CO₂ Reduction