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Register a new userMachine learning driven synthesis of few-layered WTe2
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Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribbons (NRs) is thus highly desirable and essential for the further study. Traditional exploration of the optimal synthesis conditions of novel materials is based on the trial-and-error approach, which is time consuming, costly and laborious. Recently, machine learning (ML) has demonstrated promising capability in guiding material synthesis through effectively learning from the past data and then making recommendations. Here, we report the implementation of supervised ML for the chemical vapor deposition (CVD) synthesis of high-quality 1D few-layered WTe2 nanoribbons (NRs). The synthesis parameters of the WTe2 NRs are optimized by the trained ML model. On top of that, the growth mechanism of as-synthesized 1T few-layered WTe2 NRs is further proposed, which may inspire the growth strategies for other 1D nanostructures. Our findings suggest that ML is a powerful and efficient approach to aid the synthesis of 1D nanostructures, opening up new opportunities for intelligent material development.
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Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods leads to high uncertainty, numerous trials and exorbitant cost. Recently, machine learning (ML) has demonstrated tremendous potential for material research. Here, we report the application of ML to optimize and accelerate material synthesis process in two representative multi-variable systems. A classification ML model on chemical vapor deposition-grown MoS2 is established, capable of optimizing the synthesis conditions to achieve higher success rate. While a regression model is constructed on the hydrothermal-synthesized carbon quantum dots, to enhance the process-related properties such as the photoluminescence quantum yield. Progressive adaptive model is further developed, aiming to involve ML at the beginning stage of new material synthesis. Optimization of the experimental outcome with minimized number of trials can be achieved with the effective feedback loops. This work serves as proof of concept revealing the feasibility and remarkable capability of ML to facilitate the synthesis of inorganic materials, and opens up a new window for accelerating material development.
The recent growth in data volumes produced by modern electron microscopes requires rapid, scalable, and flexible approaches to image segmentation and analysis. Few-shot machine learning, which can richly classify images from a handful of user-provided examples, is a promising route to high-throughput analysis. However, current command-line implementations of such approaches can be slow and unintuitive to use, lacking the real-time feedback necessary to perform effective classification. Here we report on the development of a Python-based graphical user interface that enables end users to easily conduct and visualize the output of few-shot learning models. This interface is lightweight and can be hosted locally or on the web, providing the opportunity to reproducibly conduct, share, and crowd-source few-shot analyses.
Broken symmetry is the essence of exotic properties in condensed matters. Tungsten ditelluride, WTe$_2$, exceptionally takes a non-centrosymmetric crystal structure in the family of transition metal dichalcogenides, and exhibits novel properties$^{1-4}$, such as the nonsaturating magnetoresistance$^1$ and ferroelectric-like behavior$^4$. Herein, using the first-principles calculation, we show that unique layer stacking in WTe$_2$ generates surface dipoles with different strengths on the top and bottom surfaces in few-layer WTe$_2$. This leads to a layer-dependence for electron/hole carrier ratio and the carrier compensation responsible for the unusual magnetoresistance. The surface dipoles are tunable and switchable using the interlayer shear displacement. This could explain the ferroelectric-like behavior recently observed in atomically thin WTe$_2$ films$^4$. In addition, we reveal that exfoliation of the surface layer flips the out-of-plane spin textures. The presented results will aid in the deeper understanding, manipulation, and further exploration of the physical properties of WTe$_2$ and related atom-layered materials, for applications in electronics and spintronic devices.
We propose a novel active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled to Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO2) dataset generated from a melt-quench ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144-atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates (i.e. 1.0 K/ps) not accessible via AIMD. The melt and amorphous x-ray structural factors generated from our simulation are in good agreement with experiment. Additionally, the calculated diffusion constants are in good agreement with previous ab initio studies.
When a crystal becomes thinner and thinner to the atomic level, peculiar phenomena discretely depending on its layer-numbers (n) start to appear. The symmetry and wave functions strongly reflect the layer-numbers and stacking order, which brings us a potential of realizing new properties and functions that are unexpected in either bulk or simple monolayer. Multilayer WTe2 is one such example exhibiting unique ferroelectricity and non-linear transport properties related to the antiphase stacking and Berry-curvature dipole. Here we investigate the electronic band dispersions of multilayer WTe2 (2-5 layers), by performing laser-based micro-focused angle-resolved photoelectron spectroscopy on exfoliated-flakes that are strictly sorted by n and encapsulated by graphene. We clearly observed the insulator-semimetal transition occurring between 2- and 3-layers, as well as the 30-70 meV spin-splitting of valence bands manifesting in even n as a signature of stronger structural asymmetry. Our result fully demonstrates the possibility of the large energy-scale band and spin manipulation through the finite n stacking procedure.
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