An official website of the United States government
Abstract Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.
more »
« less
Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy
Abstract Understanding lattice deformations is crucial in determining the properties of nanomaterials, which can become more prominent in future applications ranging from energy harvesting to electronic devices. However, it remains challenging to reveal unexpected deformations that crucially affect material properties across a large sample area. Here, we demonstrate a rapid and semi-automated unsupervised machine learning approach to uncover lattice deformations in materials. Our method utilizes divisive hierarchical clustering to automatically unveil multi-scale deformations in the entire sample flake from the diffraction data using four-dimensional scanning transmission electron microscopy (4D-STEM). Our approach overcomes the current barriers of large 4D data analysis without a priori knowledge of the sample. Using this purely data-driven analysis, we have uncovered different types of material deformations, such as strain, lattice distortion, bending contour, etc., which can significantly impact the band structure and subsequent performance of nanomaterials-based devices. We envision that this data-driven procedure will provide insight into materials’ intrinsic structures and accelerate the discovery of materials.
more »
« less
- Award ID(s):
- 1719875
- PAR ID:
- 10411465
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
As an innovative technology, four‐dimentional (4D) printing is built upon the principles of three‐dimentional (3D) printing with an additional dimension: time. While traditional 3D printing creates static objects, 4D printing generates “responsive 3D printed structures”, enabling them to transform or self‐assemble in response to external stimuli. Due to the dynamic nature, 4D printing has demonstrated tremendous potential in a range of industries, encompassing aerospace, healthcare, and intelligent devices. Nanotechnology has gained considerable attention owing to the exceptional properties and functions of nanomaterials. Incorporating nanomaterials into an intelligent matrix enhances the physiochemical properties of 4D printed constructs, introducing novel functions. This review provides a comprehensive overview of current applications of nanomaterials in 4D printing, exploring their synergistic potential to create dynamic and responsive structures. Nanomaterials play diverse roles as rheology modifiers, mechanical enhancers, function introducers, and more. The overarching goal of this review is to inspire researchers to delve into the vast potential of nanomaterial‐enabled 4D printing, propelling advancements in this rapidly evolving field.more » « less
-
Fluorographene, a fluorinated graphene-derivative, is expected to feature both high thermal conductivity and electrical insulation simultaneously, making it an emerging material for thermal management in electronic devices. In this paper, we investigated the lattice thermal conductivity and phonon transport properties of monolayer fluorographene using first-principles calculation. The solution of the fully linearized phonon Boltzmann transport equation gives the lattice thermal conductivity of monolayer fluorographene as 145.2 W m−1 K−1 at 300 K, which is about 20 times smaller than that of monolayer graphene. We systematically compared the phonon transport properties of all phonon modes in graphene and fluorographene in terms of phonon polarization. The significantly reduced thermal conductivity of fluorographene can be attributed to the lowering of both the lifetime of the flexural acoustic phonons and the group velocities of all acoustic phonons. We concluded that the broken in-plane mirror symmetry and the weaker in-plane chemical bonds induced by fluorination led to the suppression of the lattice thermal conductivity of fluorographene. Finally, we investigated the anomalously large contribution of optical phonons to the thermal transport process in fluorographene, where the large group velocities of selected optical phonons were derived from the in-plane acoustic modes of graphene. Our work provides a new approach to studying the influence of chemical functionalization on the phonon structure and exploring graphene-derived thermal management materials.more » « less
-
A comparative study is presented to solve the inverse problem in elasticity for the shear modulus (stiffness) distribution utilizing two constitutive equations: (1) linear elasticity assuming small strain theory, and (2) finite elasticity with a hyperelastic neo-Hookean material model. Assuming that a material undergoes large deformations and material nonlinearity is assumed negligible, the inverse solution using (2) is anticipated to yield better results than (1). Given the fact that solving a linear elastic model is significantly faster than a nonlinear model and more robust numerically, we posed the following question: How accurately could we map the shear modulus distribution with a linear elastic model using small strain theory for a specimen undergoing large deformations? To this end, experimental displacement data of a silicone composite sample containing two stiff inclusions of different sizes under uniaxial displacement controlled extension were acquired using a digital image correlation system. The silicone based composite was modeled both as a linear elastic solid under infinitesimal strains and as a neo-Hookean hyperelastic solid that takes into account geometrically nonlinear finite deformations. We observed that the mapped shear modulus contrast, determined by solving an inverse problem, between inclusion and background was higher for the linear elastic model as compared to that of the hyperelastic one. A similar trend was observed for simulated experiments, where synthetically computed displacement data were produced and the inverse problem solved using both, the linear elastic model and the neo-Hookean material model. In addition, it was observed that the inverse problem solution was inclusion size-sensitive. Consequently, an 1-D model was introduced to broaden our understanding of this issue. This 1-D analysis revealed that by using a linear elastic approach, the overestimation of the shear modulus contrast between inclusion and background increases with the increase of external loads and target shear modulus contrast. Finally, this investigation provides valuable information on the validity of the assumption for utilizing linear elasticity in solving inverse problems for the spatial distribution of shear modulus associated with soft solids undergoing large deformations. Thus, this work could be of importance to characterize mechanical property variations of polymer based materials such as rubbers or in elasticity imaging of tissues for pathology.more » « less
-
Abstract Material properties strongly depend on the nature and concentration of defects. Characterizing these features may require nano- to atomic-scale resolution to establish structure–property relationships. 4D-STEM, a technique where diffraction patterns are acquired at a grid of points on the sample, provides a versatile method for highlighting defects. Computational analysis of the diffraction patterns with virtual detectors produces images that can map material properties. Here, using multislice simulations, we explore different virtual detectors that can be applied to the diffraction patterns that go beyond the binary response functions that are possible using ordinary STEM detectors. Using graphene and lead titanate as model systems, we investigate the application of virtual detectors to study local order and in particular defects. We find that using a small convergence angle with a rotationally varying detector most efficiently highlights defect signals. With experimental graphene data, we demonstrate the effectiveness of these detectors in characterizing atomic features, including vacancies, as suggested in simulations. Phase and amplitude modification of the electron beam provides another process handle to change image contrast in a 4D-STEM experiment. We demonstrate how tailored electron beams can enhance signals from short-range order and how a vortex beam can be used to characterize local symmetry.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1038/s41524-022-00793-9
