MPInterfaces: A Materials Project based Python tool for high-throughput computational screening of interfacial systems

Mathew, Kiran; Singh, Arunima K.; Gabriel, Joshua J.; Choudhary, Kamal; Sinnott, Susan B.; Davydov, Albert V.; Tavazza, Francesca; Hennig, Richard G.

doi:10.1016/j.commatsci.2016.05.020

Cited by 104 publications

(92 citation statements)

References 52 publications

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“…The phase-field equations (Eqs. (30) and (31)) can recover the sharp-interface BCF model in the limit of W(θ) → 0 140 . The applications of the phase-field models in 2D materials growth can be categorized into the following three aspects.…”

Section: Mesoscale Simulationsmentioning

confidence: 87%

Multiscale computational understanding and growth of 2D materials: a review

et al. 2020

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The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general.npj Computational Materials (2020) 6:22 ; https://doi.

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Section: Mesoscale Simulationsmentioning

confidence: 87%

Multiscale computational understanding and growth of 2D materials: a review

et al. 2020

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“…Implementations of these approaches, in particular of the FG model and its direct descendants, have been reported for Quantum ESPRESSO, [52,53] BigDFT, [3,54,55] FHI-Aims, ONETEP, [56] CP2K, [6] GPAW, [57] and VASP. [2,[58][59][60] Among these recent developments, publicly available distributions of continuum embedding approaches have been released for Quantum ESPRESSO via the Environ module, [61] for VASP through the VASPsol package, [62] in the J-DFTx code, [47] in the CP2K package, [63,64] in the FHI-Aims package, [65][66][67] and for ONETEP exploiting the DM_LG library. [68] This review aims at covering the basic physical and numerical aspects of these models, their specific features and their main differences with respect to state-of-the-art continuum solvation models in quantum-chemistry simulations.…”

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confidence: 99%

Continuum embeddings in condensed‐matter simulations

Andreussi

Fisicaro

2018

Int J of Quantum Chemistry

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Continuum models have a long tradition in computational chemistry, where they have provided a compact and efficient way to characterize environment effects in quantum-mechanical simulations of solvated systems. Fattebert and Gygi pioneered the development of continuum dielectric embedding schemes for periodic systems and their seamless extension toward molecular dynamics simulations. Following their work, continuum embedding approaches in condensedmatter simulations have thrived. The possibility to model wet and electrified interfaces, with a reduced computational overhead with respect to isolated systems, is opening new perspectives in the characterization of materials and devices. Important applications of these new techniques are in the field of catalysis, electro-chemistry, electro-catalysis, etc. Here we will address the main physical and computational aspects of continuum embedding schemes recently developed for condensed-matter simulations, underlying their peculiarities and their differences with respect to the quantum-chemistry state-of-the-art.condensed matter, continuum models, electro-chemistry, materials, solvation 1 | INTRODUCTION An embedding liquid environment can substantially affect the electronic properties, geometries and spectroscopic responses of the embedded system. Dealing with this kind of effects is a difficult computational task, due to the multiscale nature of the system. A brute force approach to handle these effects would require including all of the solvent atomistic details in the quantum-mechanical (QM) calculation of the solvated system. The total number of degrees of freedom, in particular the number of electrons that need to be described in a first-principles calculation, can thus increase by orders of magnitude. As a result, a calculation that would be feasible in vacuum can easily become untreatable in solution. Moreover, statistical sampling and averaging of the results over different configurations of the liquid would be required, thus further increasing the computational cost of such an approach. These limitations have motivated the development of incredibly powerful simulation programs, [1][2][3] able to take advantage of the rapid evolution of scientific computing hardware and software, for example, by fully exploiting parallel and hybrid architectures. Regardless of its computational feasibility, the explicit description of liquid environments may also suffer from some well-known limitations of current state-of-the-art ab initio methods, in particular regarding their structural [4][5][6][7] and dielectric [8][9][10] properties.On the other hand, when looking at the properties of a solvated system, it is often the case that the detailed effects caused by solute-solvent interactions are less important than the intrinsic properties of the solute. If this is the case, one can exploit a hierarchical approach, where solvent degrees of freedom are treated with a computationally less expensive technique, but are coupled with an highly accurate description of the solvated system...

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“…Forisolated Pb x Sn 1Àx Se rock salt bilayers,the structure relaxes such that the layers are puckered with cations on the outside and Se on the inside, agreeing with the positions of these planes determined from Rietveld analysis of 00l diffraction data. Lattice-matched heterostructure models created with MPInterfaces [20] show that, with the TiSe 2 layers present, the formation energy for the structure with Pb on the outside layer is consistently lower than that for Sn in the order of 10 meV per metal atom (Supporting Information, Figure S2), in agreement with the experimental. We observed as quare in-plane unit cell with a = 0.6092 nm, virtually identical to experimental inplane diffraction data (a = 0.6095(1) nm).…”

Section: Two-dimensionalmaterialsandtheirintegrationintovandermentioning

confidence: 72%

Interface‐Driven Structural Distortions and Composition Segregation in Two‐Dimensional Heterostructures

et al. 2017

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The discovery of emergent phenomena in 2D materials has sparked substantial research efforts in the materials community. A significant experimental challenge for this field is exerting atomistic control over the structure and composition of the constituent 2D layers and understanding how the interactions between layers drive both structure and properties. While no segregation for single bilayers was observed, segregation of Pb to the surface of three bilayer thick PbSe-SnSe alloy layers was discovered within [(Pb Sn Se) ] (TiSe ) heterostructures using electron microscopy. This segregation is thermodynamically favored to occur when Pb Sn Se layers are interdigitated with TiSe monolayers. DFT calculations indicate that the observed segregation depends on what is adjacent to the Pb Sn Se layers. The interplay between interface- and volume-free energies controls both the structure and composition of the constituent layers, which can be tuned using layer thickness.

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MPInterfaces: A Materials Project based Python tool for high-throughput computational screening of interfacial systems

Cited by 104 publications

References 52 publications

Multiscale computational understanding and growth of 2D materials: a review

Multiscale computational understanding and growth of 2D materials: a review

Continuum embeddings in condensed‐matter simulations

Interface‐Driven Structural Distortions and Composition Segregation in Two‐Dimensional Heterostructures

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