Remarks on the recent progress of Materials Genome Initiative

Lu, Xiao-Gang

doi:10.1007/s11434-015-0937-2

Cited by 16 publications

(6 citation statements)

References 20 publications

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“…Driven by a diverse set of communities with unique and assorted requirements, the Materials Genome Initiative has attracted great attention globally to yield the new methods, metrologies, and capabilities necessary for accelerated materials development. The availability of high-quality materials data, including quantitative characterization and interoperability standards for material properties is crucial to achieving the advances of materials in multi-fields in science, engineering, and designs 1,2 . The triboelectric effect is a type of contact-induced electrification, owing to which a material would become electrically charged after it comes into frictional contact with another dissimilar material 3–7 .…”

Section: Introductionmentioning

confidence: 99%

Quantifying the triboelectric series

et al. 2019

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Triboelectrification is a well-known phenomenon that commonly occurs in nature and in our lives at any time and any place. Although each and every material exhibits triboelectrification, its quantification has not been standardized. A triboelectric series has been qualitatively ranked with regards to triboelectric polarization. Here, we introduce a universal standard method to quantify the triboelectric series for a wide range of polymers, establishing quantitative triboelectrification as a fundamental materials property. By measuring the tested materials with a liquid metal in an environment under well-defined conditions, the proposed method standardizes the experimental set up for uniformly quantifying the surface triboelectrification of general materials. The normalized triboelectric charge density is derived to reveal the intrinsic character of polymers for gaining or losing electrons. This quantitative triboelectric series may serve as a textbook standard for implementing the application of triboelectrification for energy harvesting and self-powered sensing.

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Section: Introductionmentioning

confidence: 99%

Quantifying the triboelectric series

et al. 2019

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“…In 2011, the US Government launched the materials genome initiative (MGI) with the aim of finding a more expeditious and economical paradigm to accelerate materials discovery, design, and deployment into manufactured products [274] . The kernel idea of this MGI paradigm lies in the seamless integration of high‐throughput computations with available experimental data and theoretical work to establish a link between material properties and structures [275] . As a representative example, we envision a scenario involving the labor‐saving screening of high molecular weight polymeric scale inhibitors.…”

Section: Discussionmentioning

confidence: 99%

Atomistic simulations of nucleation and growth of CaCO₃ with the influence of inhibitors: A review

Li,

Zeng,

Zhang

2023

Materials Genome Engineering Advances

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Calcium carbonate (CaCO3) is a crucial mineral with great scientific relevance in biomineralization and geoscience. However, excessive precipitation of CaCO3 is posing a threat to industrial production and the aquatic environment. The utilization of chemical inhibitors is typically considered an economical and successful route for addressing the scaling issues, while the underlying mechanism is still debated and needs to be further investigated. In this context, a deep understanding of the crystallization process of CaCO3 and how the inhibitors interact with CaCO3 nuclei and crystals are of great significance in evaluating the performance of scale inhibitors. In recent years, with the rapid development of computing facilities, computer simulations have provided an atomic‐level perspective on the kinetics and thermodynamics of possible association events in CaCO3 solutions as well as the predictions of nucleation pathway and growth mechanism of CaCO3 crystals as a complement to experiment. This review surveys several computational methods and their achievements in this field with a focus on analyzing the functional mechanisms of different types of inhibitors. A general discussion of the current challenges and future directions in applying atomistic simulations to the discovery, design, and development of more effective water‐scale inhibitors is also discussed.

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“…One of the most pervasive challenges of materials discovery and development is that the process demands an enormous amount of time, labor, and capital. Reducing these demands has been a central driver for a number of international initiatives [1][2][3][4][5][6][7]. This challenge stems from the inefficient use of a linear approach to explore high-dimensional materials spaces, which is compounded by critical metrics for material classes that account for both processing variations and complexity of application environment.…”

Section: Introductionmentioning

confidence: 99%

3D Grain Shape Generation in Polycrystals Using Generative Adversarial Networks

Jangid

Brodnik

Khan

et al. 2022

Integr Mater Manuf Innov

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This paper presents a generative adversarial network (GAN) capable of producing realistic microstructure morphology features and demonstrates its capabilities on a dataset of crystalline titanium grain shapes. Alongside this, we present an approach to train deep learning networks to understand material-specific descriptor features, such as grain shapes, based on existing conceptual relationships with established learning spaces, such as functional object shapes. A style-based GAN with Wasserstein loss, called M-GAN, was first trained to recognize distributions of morphology features from function objects in the ShapeNet dataset and was then applied to grain morphologies from a 3D crystallographic dataset of Ti-6Al-4V. Evaluation of feature recognition on objects showed comparable or better performance than state-of-the-art voxel-based network approaches. When applied to experimental data, M-GAN generated realistic grain morphologies comparable to those seen in Ti-6Al-4V. A quantitative comparison of moment invariant distributions showed that the generated grains were similar in shape and structure to the ground truth, but scale invariance learned from object recognition led to difficulty in distinguishing between the physical features of small grains and spatial resolution artifacts. The physical implications of M-GAN's learning capabilities are discussed, as well as the extensibility of this approach to other material characteristics related to grain morphology.

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Remarks on the recent progress of Materials Genome Initiative

Cited by 16 publications

References 20 publications

Quantifying the triboelectric series

Quantifying the triboelectric series

Atomistic simulations of nucleation and growth of CaCO₃ with the influence of inhibitors: A review

3D Grain Shape Generation in Polycrystals Using Generative Adversarial Networks

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Remarks on the recent progress of Materials Genome Initiative

Cited by 16 publications

References 20 publications

Quantifying the triboelectric series

Quantifying the triboelectric series

Atomistic simulations of nucleation and growth of CaCO3 with the influence of inhibitors: A review

3D Grain Shape Generation in Polycrystals Using Generative Adversarial Networks

Contact Info

Product

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Atomistic simulations of nucleation and growth of CaCO₃ with the influence of inhibitors: A review