Metal-insulator Transition Material Research Articles

Boosting-Crystal Graph Convolutional Neural Network for Predicting Highly Imbalanced Data: A Case Study for Metal-Insulator Transition Materials.

Applying machine-learning techniques for imbalanced data sets presents a significant challenge in materials science since the underrepresented characteristics of minority classes are often buried by the abundance of unrelated characteristics in majority of classes. Existing approaches to address this focus on balancing the counts of each class using oversampling or synthetic data generation techniques. However, these methods can lead to loss of valuable information or overfitting. Here, we introduce a deep learning framework to predict minority-class materials, specifically within the realm of metal-insulator transition (MIT) materials. The proposed approach, termed boosting-CGCNN, combines the crystal graph convolutional neural network (CGCNN) model with a gradient-boosting algorithm. The model effectively handled extreme class imbalances in MIT material data by sequentially building a deeper neural network. The comparative evaluations demonstrated the superior performance of the proposed model compared to other approaches. Our approach is a promising solution for handling imbalanced data sets in materials science.

Rich oxygen vacancies mediated metal-insulator transition materials toward ultrasensitive sensing and energy conversion

Regulating structural phase transitions of metal insulator transition (MIT) correlated materials is an ideal approach to modulate their properties. However, due to phase transition hysteresis, ultrasensitive electron transition is difficult to achieve. Here, we develop a convenient method via Fe ions surface doping-induced oxygen vacancies (OVs) at the Ti3O5 surface to enable ultrasensitive electronic transitions. The Fe doping promotes the formation of free electrons, which is attributed to the reduced bandwidth, making it relatively easy for valence band electrons to leap to the conduction band. The formation of OVs increase the electron concentration and enhance the electronic conductivity (2.49×10−4 S/cm) at low temperatures, resulting in an ultrasensitive sensing over a wide temperature range before metal insulator transition. This unique property facilitates its application in fire warning with ultrasensitive sensing (response time 0.63 s, and response temperature 150 °C) and good durability. Moreover, the as-obtained MIT correlated material with rich-OVs structure exhibits excellent oxygen sensing and photothermal conversion properties. These results offer a novel design for MIT correlated materials and provide an innovative insight for modulating their multifunctional properties.

Zero-dimensionality of a scaled-down VO2 metal-insulator transition via high-resolution electrostatic gating

An understanding of the phase transitions at the nanoscale is essential in state-of-the-art engineering1–5, instead of simply averaging the heterogeneous domains formed during phase transitions6,7. However, as materials are scaled down, the steepness of the phase transition rapidly increases8–13 and requires extremely high precision in the control method. Here, a three-terminal device, which could precisely control the phase transition electrically14–19, was applied for the first time to a scaled-down metal-insulator transition material VO2. The crossover from continuous to binary transitions with the scaled-down material was clarified, and the critical channel length was successfully elucidated via phase boundary energy. Notably, below the critical channel length, the spatial degrees of freedom degenerated, and the impact of drain voltage application disappeared in the phase transition, indicating zero-dimensionality of the VO2 channel. This zero-dimensionality could be the fundamental property in the scaled-down phase transition and have a significant impact on various fields that need nanoscale engineering.

Phase-Engineered VO2 Metal Nanofiber Enables a Metal-Insulator Transition for Bidirectional Thermal Reallocation.

Phase change materials (PCMs) are appealing for their fascinating capability of thermal reallocation, assisting widely in many areas of human productivity and life. However, it has remained a significant challenge to attain shape stability, temperature resistance, and microscale continuity in PCMs while maintaining sufficient phase change performance. Here we report a sol epitaxial fabrication strategy to create metal-insulator transition nanofibers (MIT-NFs) composed of monoclinic vanadium dioxide. The MIT-NFs are further assembled into self-standing two-dimensional membranes and three-dimensional aerogels with structural robustness. The resulting series of metal-insulator transition materials exhibits the integrated features of solid-solid phase change properties, shape stability, and thermal reallocation properties. The integral ceramic characteristic also provides the MIT-NFs with surface stiffness (54 GPa), temperature resistance (-196° to 330 °C), and thermal insulator properties. The successful fabrication of these captivating MIT materials may provide new perspectives for next-generation, shape-stable, and self-standing PCMs.

A Review of Phase-Change Materials and Their Potential for Reconfigurable Intelligent Surfaces.

Phase-change materials (PCMs) and metal-insulator transition (MIT) materials have the unique feature of changing their material phase through external excitations such as conductive heating, optical stimulation, or the application of electric or magnetic fields, which, in turn, results in changes to their electrical and optical properties. This feature can find applications in many fields, particularly in reconfigurable electrical and optical structures. Among these applications, the reconfigurable intelligent surface (RIS) has emerged as a promising platform for both wireless RF applications as well as optical ones. This paper reviews the current, state-of-the-art PCMs within the context of RIS, their material properties, their performance metrics, some applications found in the literature, and how they can impact the future of RIS.

Facile Synthesis and Single-Switch Antenna Application of Germanium-Doped Vanadium Dioxide

Materials that undergo a phase transition from metallic to insulating, or metal–insulator transition (MIT), materials have become widely popular for their potential in emerging technologies due to their drastic conductivity change upon transitioning. Notable among the MIT materials is vanadium dioxide (VO2), and ongoing efforts are focused on tuning its MIT phase transition temperature (TMIT). In this report, VO2 germanium-doped nanoparticles with various germanium dopant levels were synthesized via a hydrothermal route and used in a simple single-switch antenna. Powder X-ray diffraction (XRD) analysis shows a monoclinic phase (M1) for both the pure and Ge-doped VO2 nanomaterials at room temperature, with no change in the diffraction pattern in the Ge-doped samples at low doping percentages; the M1 phase for both pure and Ge-doped VO2 was further confirmed by Raman spectroscopy. Energy-dispersive X-ray spectroscopy (EDS) showed Ge uniformly distributed in the nanomaterials. The nanoparticles’ morphology, imaged by field-emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM), reveals a morphology change from nanoparticles to nanosheets with increased dopant concentration. Ge-doped VO2 nanoparticle dispersions were used to print a single switch in an antenna solely obtained through a facile printing process. A vector network analyzer used to characterize the antenna performance showed that the germanium doping successfully changed the transition temperature of the material, demonstrating the capability of controlling the antenna operation frequencies as a function of material doping. Density functional theory (DFT) shows that substituting Ge into a V site of the crystal structure distorts the lattice and reduces the band gap at high doping percentages. These results provide insight into the potential of smart switches fabricated from Ge-doped VO2.

Recent Advancements in Reconfigurable mmWave Devices Based on Phase-Change and Metal Insulator Transition Materials

Chalcogenide Phase Change Materials (PCM) and metal insulator transition (MIT) materials are a group of materials that are capable of switching between low resistance and high resistance states. These emerging materials have been widely used in optical storage media and memory devices. Over the past recent years, there have been interests in exploiting the PCM and MIT materials, especially germanium antimony telluride (GST) alloys and vanadium dioxide (VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ), for radio frequency (RF) applications. The PCM and MIT-based RF devices are expected to bridge the gap between semiconductor switches and microelectromechanical system (MEMS) switches as they combine the low insertion loss performance of MEMS technology and the small size and reliability performance of semiconductor technology. This article presents an overview of the PCM and MIT materials for RF circuits and discusses the recent advancements in reconfigurable millimeter-wave (mmWave) devices based on PCM and MIT materials in depth.

Smart windows passively driven by greenhouse effect

The rational thermal management of buildings is of major importance for the reduction in the overall primary energy consumption. Smart windows are promising systems which could save a significant part of this energy. Here, we introduce a double-glazing system made with a thermochromic metal–insulator transition material and a glass layer separated by an air gap which is able to switch from its insulating to its conducting phase thanks to the greenhouse effect occurring in the separation gap. We also show that this passive system can reduce the incoming heat flux by 30% in comparison with a traditional double glazing while maintaining the transmittance around 0.35 over 75% of visible spectrum.

Tunable Tribovoltaic Effect via Metal–Insulator Transition

Tribovoltaic direct-current (DC) nanogenerator made of dynamic semiconductor heterojunction is emerging as a promising mechanical energy harvesting technology. However, fundamental understanding of the mechano-electronic carrier excitation and transport at dynamic semiconductor interfaces remains to be investigated. Here, we demonstrated for the first time, that tribovoltaic DC effect can be tuned with metal-insulator transition (MIT). In a representative MIT material (vanadium dioxide, VO2), we found that the short-circuit current (ISC) can be enhanced by >20 times when the material is transformed from insulating to metallic state upon static or dynamic heating, while the open-circuit voltage (VOC) turns out to be unaffected. Such phenomenon may be understood by the Hubbard model for Mott insulator: orders' magnitude increase in conductivity is induced when the nearest hopping changes dramatically and overcomes the Coulomb repulsion, while the Coulomb repulsion giving rise to the quasi-particle excitation energy remains relatively stable.

Machines for Materials and Materials for Machines: Metal-Insulator Transitions and Artificial Intelligence

In this perspective, we discuss the current and future impact of artificial intelligence and machine learning for the purposes of better understanding phase transitions, particularly in correlated electron materials. We take as a model system the rare-earth nickelates, famous for their thermally-driven metal-insulator transition, and describe various complementary approaches in which machine learning can contribute to the scientific process. In particular, we focus on electron microscopy as a bottom-up approach and metascale statistical analyses of classes of metal-insulator transition materials as a bottom-down approach. Finally, we outline how this improved understanding will lead to better control of phase transitions and present as an example the implementation of rare-earth nickelates in resistive switching devices. These devices could see a future as part of a neuromorphic computing architecture, providing a more efficient platform for neural network analyses – a key area of machine learning.

Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. There is a dearth of thermally-driven MIT materials, however, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Here we report a material database comprising temperature-controlled MITs (and metals and insulators with similar chemical composition and stoichiometries to the MIT compounds) from high quality experimental literature, built through a combination of materials-domain knowledge and natural language processing. We featurize the dataset using compositional, structural, and energetic descriptors, including two MIT relevant energy scales, an estimated Hubbard interaction and the charge transfer energy, as well as the structure-bond-stress metric referred to as the global-instability index (GII). We then perform supervised classification, constructing three electronic-state classifiers: metal vs non-metal (M), insulator vs non-insulator (I), and MIT vs non-MIT (T). We identify two important descriptors that separate metals, insulators, and MIT materials in a 2D feature space: the average deviation of the covalent radius and the range of the Mendeleev number. We further elaborate on other important features (GII and Ewald energy), and examine how they affect classification of binary vanadium and titanium oxides. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. Last, we implement an online version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file.

Switchable wavefront control using an all-dielectric metasurface mediated by VO2

Active metasurfaces using metal–insulator transition materials such as vanadium dioxide (VO2) have been demonstrated recently. As most of them are based on plasmonic metasurfaces, it is difficult to realize transmissive devices at optical frequencies. In this study, we theoretically propose and demonstrate the transmission type of an active all-dielectric metasurface mediated by VO2. We numerically study the optical properties of a cylindrical Mie resonator consisting of crystalline silicon and VO2 layers, and we find that it achieves a 2π phase shift of approximately 1550 nm by tuning the radius. The proposed structure can be applied to non-mechanical beam steering, polarizers, metalenses, etc.

Recent Progress on Vanadium Dioxide Nanostructures and Devices: Fabrication, Properties, Applications and Perspectives.

Vanadium dioxide (VO2) is a typical metal-insulator transition (MIT) material, which changes from room-temperature monoclinic insulating phase to high-temperature rutile metallic phase. The phase transition of VO2 is accompanied by sudden changes in conductance and optical transmittance. Due to the excellent phase transition characteristics of VO2, it has been widely studied in the applications of electric and optical devices, smart windows, sensors, actuators, etc. In this review, we provide a summary about several phases of VO2 and their corresponding structural features, the typical fabrication methods of VO2 nanostructures (e.g., thin film and low-dimensional structures (LDSs)) and the properties and related applications of VO2. In addition, the challenges and opportunities for VO2 in future studies and applications are also discussed.

Thermophysical Properties of Metal-Insulator Transition Materials During Phase Transition for Thermal Control Devices

This study reports on the fabrication of metal-insulator transition materials and measurement of the temperature dependency of their thermophysical properties, namely, specific heat, thermal conductivity, and total hemispherical emittance to evaluate the potential of these materials as multifunctional thermal control devices. Perovskite-type manganese oxide, La0.8Sr0.2MnO3 (LSMO) and La0.8Pb0.2MnO3 (LPMO), and vanadium dioxide (VO2) are selected as candidate materials. LSMO and LPMO are prepared using the solution combustion and simple sintering methods, and VO2 is prepared using the spark plasma sintering method. The phase transition temperatures, specific heat capacities, and heat storage capabilities during the phase transition of these materials are measured via differential scanning calorimetry. The thermal conductivities are measured via AC calorimetric method. The total hemispherical emittances are measured using the steady-state calorimetric method. Results show that VO2 has the highest heat storage capability during phase transition. The large change in the total hemispherical emittances of LSMO and LPMO and the large change in the thermal conductivity of VO2 before and after phase transition are confirmed. Moreover, tungsten-doped vanadium dioxide (VWO2) is fabricated to reduce the phase transition temperature to much lower than that of VO2, and the measurement results of its thermophysical properties are also presented.

Featureless adaptive optimization accelerates functional electronic materials design

Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse.

Discovering new electronic materials despite the small data problem

An adaptive optimization engine identifies 12 functional and synthesizable metal-insulator transition materials without a large starting data set.

Toward High-Precision Control of Transformation Characteristics in VO2 through Dopant Modulation of Hysteresis

Metal–insulator transition materials such as VO2 have garnered much attention in the field of neuromorphic devices because of their nonlinear behavior and orders of magnitude scale property changes...

Modulation of VO2 Metal–Insulator Transition by Ferroelectric HfO2 Gate Insulator

AbstractFerroelectric gating of functional materials has often suffered because ferroelectric materials are poor insulators. In this study, the recently reported ferroelectric HfO2, a large band‐gap oxide with excellent insulating properties, is exploited for electrostatically gating the archetypical metal–insulator transition material, VO2. By protecting the meta‐stable ferroelectric phase from deterioration, the ferroelectric gating is successfully demonstrated with an ultra‐thin VO2 channel in the back‐gate geometry. The observed modulation of the VO2 channel conductivity is originated from the ferroelectric polarization reversal in the HfO2 gate insulator combined with the VO2 metal–insulator transition. These results demonstrate the significant potential of ferroelectric HfO2 for electrostatically controlling the state of matter for both fundamental research and device application.

A comprehensive study of enhanced characteristics with localized transition in interface-type vanadium-based devices

In this research, we investigated the conduction mechanism in metal-insulator transition (MIT) materials. Among these MIT materials (NbOx, NiOx, VOx, and TaS2), vanadium oxide–based selectors have been widely investigated because of their high switching speed (~10-ns transition time), sufficient non-linearity (>103), and endurance stability (~1010). Abnormal temperature-dependent degradation in the high resistive state was observed, as was studied in detail by a current fitting analysis and explored theoretically by electric (E-MIT) and thermal (T-MIT) modeling. The results suggest the existence of a MIT region located between the electrode and the localized filament. To improve the localized transition efficiency, we propose an enhanced-type MIT architecture to bypass the E-MIT and T-MIT universal rule with the novel structure of vanadium top electrode device. As compared with a vanadium oxide middle-layer device, the electrical transition efficiency is improved 2-fold as evidenced by thermal cycling material analysis, as well as boosting endurance reliability to 107 at 65 °C. Finally, for the first time, a potential neuromorphic computing application featuring a damping oscillator has been demonstrated in this enhanced-type MIT architecture, with a high damping ratio with 10-fold smaller area and 5-fold smaller energy than complementary metal–oxide–semiconductor (CMOS) devices. This presents a promising milestone for ultralow power neuromorphic system design and solutions in the near future.

3D Smith charts scattering parameters frequency-dependent orientation analysis and complex-scalar multi-parameter characterization applied to Peano reconfigurable vanadium dioxide inductors

Recently, the field of Metal-Insulator-Transition (MIT) materials has emerged as an unconventional solution for novel energy efficient electronic functions, such as steep slope subthermionic switches, neuromorphic hardware, reconfigurable radiofrequency functions, new types of sensors, terahertz and optoelectronic devices. Employing radiofrequency (RF) electronic circuits with a MIT material like vanadium Dioxide, VO2, requires appropriate characterization tools and fabrication processes. In this work, we develop and use 3D Smith charts for devices and circuits having complex frequency dependences, like the ones resulting using MIT materials. The novel foundation of a 3D Smith chart involves here the geometrical fundamental notions of oriented curvature and variable homothety in order to clarify first theoretical inconsistencies in Foster and Non Foster circuits, where the driving point impedances exhibit mixed clockwise and counter-clockwise frequency dependent (oriented) paths on the Smith chart as frequency increases. We show here the unique visualization capability of a 3D Smith chart, which allows to quantify orientation over variable frequency. The new 3D Smith chart is applied as a joint complex-scalar 3D multi-parameter modelling and characterization environment for reconfigurable RF design exploiting Metal-Insulator-Transition (MIT) materials. We report fabricated inductors with record quality factors using VO2 phase transition to program multiple tuning states, operating in the range 4 GHz to 10 GHz.