Topology of the valley-Chern effect

Qian, Kai; Apigo, David J.; Prodan, Camelia; Barlas, Yafis; Prodan, Emil

doi:10.1103/physrevb.98.155138

Cited by 78 publications

(72 citation statements)

References 46 publications

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“…It is worth pointing out that at 7.51 and 7.98 kHz (figures 4(F) and (H)), the wave back-scatters around the sharp bends. A similar behavior was also observed recently in experiments on a mechanical counterpart of quantum valley Hall effect [35], where guided topological waves are back-scattered around a sharp bend for some frequencies inside the topological bandgap. However, here we observe that the wave is able to pass through the left bend at a lower frequency ( figure 4(F)) and through the right bend at a higher frequency ( figure 4(H)).…”

Section: Steady-state Transmissionsupporting

confidence: 84%

Experimental demonstration of topological waveguiding in elastic plates with local resonators

2018

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The recent emergence of topological insulators in condensed matter physics has inspired analogous wave phenomena in mechanical systems. However, to date, the design of these mechanical systems has been limited mostly to discrete lattices or perforated structures. Here, we take a ubiquitous design of a bolted elastic plate and demonstrate that it can guide flexural waves crisply around sharp bends. We show that this continuum system eliminates unwanted in-plane plate modes and allows the manipulation of low-frequency flexural modes by exploiting the local resonance of the bolts. We report the existence of a pair of double Dirac cones near the resonant frequency of the bolts, one of which leads to the creation of a topological complete bandgap that forbids all the plate modes. These findings open new possibilities of managing multiple wave modes in elastic solids for applications in energy harvesting, impact mitigation, and structural health monitoring.

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Section: Steady-state Transmissionsupporting

confidence: 84%

Experimental demonstration of topological waveguiding in elastic plates with local resonators

2018

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Section: Imentioning

confidence: 99%

“…This is a challenge with standard potentials. To-date ML-based potentials have been developed for many elemental systems such as Al 29,30 , Cu 31 30 , Fe 32 , Zr [33][34] Mo 35 , and Si 22,36 . In contrast, fewer versatile potentials for multi-element systems exist owing to the complexity of generating the DFT database as well as the optimization of the ML force-field.In the present study, we develop a deep neural net potential (DP) for the Cu-Zr binary alloy system using the DeepMD-Kit package 37 , and systematically analyze its fidelity in describing a wide range of properties and for different phases of the system.…”

mentioning

confidence: 99%

Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy

Andolina

Williamson

Saidi

2020

The Journal of Chemical Physics

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We show that a deep-learning neural network potential (DP) based on density functional theory (DFT) calculations can well describe Cu-Zr materials, an example of a binary alloy system that can coexist in several ordered intermetallics and as an amorphous phase. The complex phase diagram for Cu-Zr makes it a challenging system for traditional atomistic force-fields that fail to describe well the different properties and phases. Instead, we show that a DP approach using a large database with ~300k configurations can render results generally on par with DFT. The training set includes configurations of pristine and bulk elementary metals and intermetallics in the liquid and solid phases in addition to slab and amorphous configurations. The DP model was validated by comparing bulk properties such as lattice constants, elastic constants, bulk moduli, phonon spectra, surface energies to DFT values for identical structures. Further, we contrast the DP results with values obtained using well-established two embedded atom method potentials. Overall, our DP potential provides near DFT accuracy for the different Cu-Zr phases but with a fraction of its computational cost, thus enabling accurate computations of realistic atomistic models especially for the amorphous phase. I.quenching rate. Both requirements pose a challenge for DFT simulations. Classical atomistic simulation methods, of which molecular dynamic simulations (MD) is the most common, have been employed in materials design for several decades to provide fast computations of atomic energies and forces. However, the parameters for these potentials to describe interatomic interactions are nontrivial to optimize. To date, the most accurate classical potentials for Cu-Zr systems are based on embedded atom method 12 (EAM) particularly those developed by Sheng et al. [13][14][15] (EAM-HS) and Mendeleev et al. 16 (EAM-MM) by fitting to a mixed experimental and ab-initio input data. While these two EAMs provide a general good description of many properties of Cu-Zr systems especially for properties that are included in the training such as the Cu-Zr melting curve as is the case of EAM-MM, they have limited transferability to configurations not included in the training as we demonstrate here. Further, and most importantly, EAM potentials have a limited functional form, and thus cannot be utilized to describe complex interactions such as for covalent and ionic bonds between Cu-Zr and reactive gasses.Machine learning (ML) and particularly deep neural network-based force-fields have the flexibility and non-linearity necessary to describe complex potential energy surfaces. [17][18][19][20][21][22][23] In traditional atomistic force-field methods such as the EAMs, the complex potential energy surface is approximated using privileged functional forms that are selected based on physical motivation. On the other hand, ML potentials do not employ any explicit functional form for the dependence of the energies and forces on the atomic coordinates, but rather "learn" how atoms interac...

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“…After such a data-driven process, ML potential can precisely map the atomistic configurations to the corresponding energies and forces from the ab-initio data. Due to its ability of accurately reproducing ab-initio data, several different forms of ML potentials have been proposed to obtain the thermal and mechanical properties of crystalline solids such as Si [17], Zr [20], graphene [21] and single-layer MoS2 [22].…”

mentioning

confidence: 99%

A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases

Lee

Luo

2020

Materials Today Physics

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Molecular dynamics simulations have been extensively used to predict thermal properties, but simulating different phases with similar precision using a unified force field is often difficult, due to the lack of accurate and transferrable interatomistic potential fields. As a result, this issue has become a major barrier to predicting the phase change of materials and their transport properties with atomistic-level modeling techniques. Recently, machine learning based algorithms have emerged as promising tools to develop accurate potentials for molecular dynamics simulations. In this work, we approach the problem of predicting the thermal conductivity of silicon in different phases by performing molecular dynamics simulations with a deep neural network potential. This neural network potential is trained with ab-initio data of silicon in the crystalline, liquid and amorphous phases. The accuracy of our potential is first validated through reproducing the atomistic structures during the phase transition, where other empirical potentials usually fail. The thermal conductivity of different phases is then calculated, showing a good agreement with the experimental results and ab-initio calculation results. Our work shows that a unified neural network-based potential can be a promising tool for studying phase change and thermal transport of materials with high accuracy.

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Topology of the valley-Chern effect

Cited by 78 publications

References 46 publications

Experimental demonstration of topological waveguiding in elastic plates with local resonators

Experimental demonstration of topological waveguiding in elastic plates with local resonators

Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy

A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases

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