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Register a new userIntelligent Identification of Two-Dimensional Structure by Machine-Learning Optical Microscopy
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Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted numerous interest and triggered revolutions of corresponding device applications. However, facile methods to realize accurate, intelligent and large-area characterizations of these 2D structures are still highly desired. Here, we report a successful application of machine-learning strategy in the optical identification of 2D structure. The machine-learning optical identification method (MOI method) endows optical microscopy with intelligent insight into the characteristic colour information in the optical photograph. Experimental results indicate that the MOI method enables accurate, intelligent and large-area characterizations of graphene, molybdenum disulphide (MoS2) and their heterostructures, including identifications of the thickness, the existence of impurities, and even the stacking order. Thanks to the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote the fundamental research and wafer-scale device application of 2D structures.
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Indium selenide (InSe), as a novel van der Waals layered semiconductor, has attracted a large research interest thanks to its excellent optical and electrical properties in the ultra-thin limit. Here, we discuss four different optical methods to quantitatively identify the thickness of thin InSe flakes on various substrates, such as SiO2/Si or transparent polymeric substrates. In the case of thin InSe deposited on a transparent substrate, the transmittance of the flake in the blue region of the visible spectrum can be used to estimate the thickness. For InSe supported by SiO2/Si, the thickness of the flakes can be estimated either by assessing their apparent colors or accurately analyzed using a Fresnel-law based fitting model of the optical contrast spectra. Finally, we also studied the thickness dependency of the InSe photoluminescence emission energy, which provides an additional tool to estimate the InSe thickness and it works both for InSe deposited on SiO2/Si and on a transparent polymeric substrate.
Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the intuition of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural-network-based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, segment sizes and their distributions, based on which we develop an ensemble approach topredict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other applications such as identifying layer numbers of a new 2D material, or materials produced by a different synthetic approach. Our artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials and potentially accelerate new material discoveries.
Ferroelectricity and metallicity are usually believed not to coexist because conducting electrons would screen out static internal electric fields. In 1965, Anderson and Blount proposed the concept of ferroelectric metal, however, it is only until recently that very rare ferroelectric metals were reported. Here, by combining high-throughput ab initio calculations and data-driven machine learning method with new electronic orbital based descriptors, we systematically investigated a large family (2,964) of two-dimensional (2D) bimetal phosphates, and discovered 60 stable ferroelectrics with out-of-plane polarization, including 16 ferroelectric metals and 44 ferroelectric semiconductors that contain seven multiferroics. The ferroelectricity origins from spontaneous symmetry breaking induced by the opposite displacements of bimetal atoms, and the full-d-orbital coinage metal elements cause larger displacements and polarization than other elements. For 2D ferroelectric metals, the odd electrons per unit cell without spin polarization may lead to a half-filled energy band around Fermi level and is responsible for the metallicity. It is revealed that the conducting electrons mainly move on a single-side surface of the 2D layer, while both the ionic and electric contributions to polarization come from the other side and are vertical to the above layer, thereby causing the coexistence of metallicity and ferroelectricity. Van der Waals heterostructures based on ferroelectric metals may enable the change of Schottky barrier height or the Schottky-Ohmic contact type and induce a dramatic change of their vertical transport properties. Our work greatly expands the family of 2D ferroelectric metals and will spur further exploration of 2D ferroelectric metals.
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending and generalizing crystal graph convolutional neural networks to systems with planar periodicity, and train an ensemble of models to predict thermodynamic, mechanical, and electronic properties. To demonstrate the utility of this approach, we carry out a screening of nearly 45,000 structures for two largely disjoint applications: namely, mechanically robust composites and photovoltaics. An analysis of the uncertainty associated with our methods indicates the ensemble of neural networks is well-calibrated and has errors comparable with those from accurate first-principles density functional theory calculations. The ensemble of models allows us to gauge the confidence of our predictions, and to find the candidates most likely to exhibit effective performance in their applications. Since the datasets used in our screening were combinatorically generated, we are also able to investigate, using an innovative method, structural and compositional design principles that impact the properties of the structures surveyed and which can act as a generative model basis for future material discovery through reverse engineering. Our approach allowed us to recover some well-accepted design principles: for instance, we find that hybrid organic-inorganic perovskites with lead and tin tend to be good candidates for solar cell applications.
Important recent advances in transmission electron microscopy instrumentation and capabilities have made it indispensable for atomic-scale materials characterization. At the same time, the availability of two-dimensional materials has provided ideal samples where each atom or vacancy can be resolved. Recent studies have also revealed new possibilities for a different application of focused electron irradiation: the controlled manipulation of structures and even individual atoms. Evaluating the full range of future possibilities for this method requires a precise physical understanding of the interactions of electrons with energies as low as 15 keV now used in (scanning) transmission electron microscopy, becoming feasible due to advances both in experimental techniques and in theoretical models. We summarize the state of current knowledge of the underlying physical processes based on the latest results on two-dimensional materials, with a focus on the physical principles of the electron-matter interaction, rather than the material-specific irradiation-induced defects it causes.
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