Dynamic Observation of Dendritic Quasicrystal Growth upon Laser-Induced Solid-State Transformation

Han, Insung; McKeown, Joseph T.; Tang, Ling; Wang, Cai‐Zhuang; Parsamehr, Hadi; Xi, Zhucong; Lu, Ying‐Rui; Kramer, M. J.; Shahani, Ashwin J.

doi:10.1103/physrevlett.125.195503

Cited by 11 publications

(9 citation statements)

References 65 publications

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“…We discover from Monte Carlo (MC) simulation runs that phason strain remains small during DQC growth and only weakly relaxes further, resulting in a high-quality DQC with negligible phason strain directly from the melt. We also observe that an approximant structure, a periodic crystal closely resembling the DQC and with inherent linear phason strain (25,26), can relax to the DQC via continuous phason strain relaxation; that is, the solid-solid transition (27,28) occurs via a process analogous to the repair step of the error-and-repair model (16,17).…”

mentioning

confidence: 80%

Entropic formation of a thermodynamically stable colloidal quasicrystal with negligible phason strain

Lee

Teich

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Quasicrystals have been discovered in a variety of materials ranging from metals to polymers. Yet, why and how they form is incompletely understood. In situ transmission electron microscopy of alloy quasicrystal formation in metals suggests an error-and-repair mechanism, whereby quasiperiodic crystals grow imperfectly with phason strain present, and only perfect themselves later into a high-quality quasicrystal with negligible phason strain. The growth mechanism has not been investigated for other types of quasicrystals, such as dendrimeric, polymeric, or colloidal quasicrystals. Soft-matter quasicrystals typically result from entropic, rather than energetic, interactions, and are not usually grown (either in laboratories or in silico) into large-volume quasicrystals. Consequently, it is unknown whether soft-matter quasicrystals form with the high degree of structural quality found in metal alloy quasicrystals. Here, we investigate the entropically driven growth of colloidal dodecagonal quasicrystals (DQCs) via computer simulation of systems of hard tetrahedra, which are simple models for anisotropic colloidal particles that form a quasicrystal. Using a pattern recognition algorithm applied to particle trajectories during DQC growth, we analyze phason strain to follow the evolution of quasiperiodic order. As in alloys, we observe high structural quality; DQCs with low phason strain crystallize directly from the melt and only require minimal further reduction of phason strain. We also observe transformation from a denser approximant to the DQC via continuous phason strain relaxation. Our results demonstrate that soft-matter quasicrystals dominated by entropy can be thermodynamically stable and grown with high structural quality––just like their alloy quasicrystal counterparts.

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confidence: 80%

Entropic formation of a thermodynamically stable colloidal quasicrystal with negligible phason strain

Lee

Teich

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…An Al-Mg-Zn DP was developed [138] and applied to confirm the co-segregation of Mg and Zn atoms at a precipitate and matrix interface. DPs have also been found to be powerful and promising for the prediction of the structure and dynamics of metallic liquids, glasses, and quasi-crystal [140][141][142][143][144][145][146][147]. The DP developed for Pd-Si accurately represented the structure of liquid and crystal structures, melting points, and glass-forming ability at compositions near Pd 3 Si and Pd 9 Si 2 (more accurately than existing EAM potential) [140].…”

Section: B Multi-element Bulk Systemsmentioning

confidence: 99%

“…Tang, et al [144][145][146][147] performed DP MD simulations of a series Al-based alloys; we focus now on Al-Cr quasicrystals [146] as an example and a demonstration of how DPs can be used together with experimental studies. Dendritic growth of metastable quasicrystals were observed in the Al 13 Cr 2 ap-proximant phase (formed from Al 90 Cr 10 thin film) by pulsed laser deposition [146] which is structurally similar to quasicrystal of the Al 13 Cr 2 matrix. The Al-Cr DP was used to simulate the quenching of the Al 90 Cr 10 alloy from 2200 to 700 K at 10 11 K/s.…”

Section: B Multi-element Bulk Systemsmentioning

confidence: 99%

Deep Potentials for Materials Science

Wen¹,

Zhang²,

Wang³

et al. 2022

Preprint

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To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

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“…Recently, a highly optimized implementation of linear-scaling Deep Potential Molecular Dynamics (DeePMD), a deep learning-based MD scheme, has * Electronic address: mohanchen@pku.edu.cn pushed the limit of molecular dynamics with ab initio accuracy to 100 million atoms, with a computational cost that typically requires one day for nano-second (ns) simulations [9,10]. DeePMD has enabled various applications in, for example, reactive uptake of nitrogen oxides by aqueous aerosol [11], crystal nucleation of liquid silicon [12], liquid-liquid phase transition of water [13], one dimensional cooperative diffusion in three dimensional crystal [14], structural order in quasicrystal growth [15], phase diagram of water [16], and warm dense matter [17,18], etc. However, for many important problems that require large system sizes or long time scales, the estimated computational cost is still prohibitive.…”

Section: Introductionmentioning

confidence: 99%

DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models

Lu¹,

Jiang²,

Chen³

et al. 2021

Preprint

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Machine learning based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from a lower efficiency as compared to typical empirical force fields due to more sophisticated computations involved. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning based PES model. This scheme, we call DP Compress, is an efficient post-processing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DPbased molecular dynamics simulations by an order of magnitude faster, and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available at https://github.com/deepmodeling/deepmd-kit.

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Dynamic Observation of Dendritic Quasicrystal Growth upon Laser-Induced Solid-State Transformation

Cited by 11 publications

References 65 publications

Entropic formation of a thermodynamically stable colloidal quasicrystal with negligible phason strain

Entropic formation of a thermodynamically stable colloidal quasicrystal with negligible phason strain

Deep Potentials for Materials Science

DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models

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