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Topological phonons in crystalline materials have been attracting great interest. However, most cases studied so far are direct generalizations of the topological states from electronic systems. Here, we reveal a novel class of topological phonons -- the symmetry-enforced nodal-chain phonons, which manifest features unique for phononic systems. We show that with D2d little co-group at a non-time-reversal-invariant-momentum point, the phononic nodal chain is guaranteed to exist owing to the vector basis symmetry of phonons, which is a unique character distinct from electronic and other systems. Combined with the spinless character, this makes the proposed nodal-chain phonons enforced by symmorphic crystal symmetries. We further screen all 230 space groups, and find five candidate groups. Interestingly, the nodal chains in these five groups exhibit two different patterns: for tetragonal systems, they are one-dimensional along the fourfold axis; for cubic systems, they form a three-dimensional network structure. Based on first-principles calculations, we identify K2O as a realistic material hosting almost ideal nodal-chain phonons. We show that the effect of LO-TO splitting, another unique feature for phonons, helps to expose the nodal-chain phonons in K2O in a large energy window. In addition, all the five candidate groups have spacetime inversion symmetry, so the nodal chains also feature a quantized π Berry phase. This leads to drumhead surface phonon modes that must exist on multiple surfaces of a sample.

MATERIALS FOR HUMANITY (MH 21)

Symmetry-Enforced Nodal Chain Phonons


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The energy cost of storing and moving data is enormous, and is growing at such a rapid pace that within a few years the power demand may overwhelm some developed nations. Non-volatile memory (NVM) and neuromorphic computing are enabling critical advances in this area, but there are many scientific and technological challenges. This conference will cover the materials challenges of different memory technologies, from the fundamental processes and mechanisms, to industry perspectives on materials specifications and manufacturing.

Materials Challenges for Memory

technical paper

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MH 21 is arranged around 6 themes that focus attention on pressing needs that should be addressed to enhance the well-being of communities and provide a forum for presenting breakthrough discoveries.

2D transition metal dichalcogenides (TMDCs) have potential for quantum applications but progress has been slow due to contact problems. In particular, quantum transport studies are difficult as low-quality contacts are exacerbated at cryogenic temperatures where quantum devices are typically operated. Here, we use indium alloy to form contacts with chemical vapour deposition grown single-layer and bilayer WS2 devices. The devices exhibit ultra-low contact resistances and Schottky barrier heights at temperatures as low as 3 K, which in turn enables high carrier mobilities and measurements of resonant quantum tunnelling. Density functional theory calculations provide further insights into the intricacies of the metal/semiconductor interface and quantum transport in the material. Our results unlock the potential for 2D semiconductors in low temperature applications such as in quantum information processing.

Quantum Transport in Two-Dimensional WS2 with High-Efficiency Carrier Injection through Indium Alloy Contacts

We experimentally demonstrate that absorption of one of the photons from the entangled pair can be switched on and off by controlling the Pancharatnam-Berry phase of the other photon, the portion of the light does not go through the absorber. By using the so-called quantum coherent perfect absorption effect [1], we can expect near-perfect switching of the remote photon. The polarization entangled state between ‘idler’ and ‘signal’ photons is generated through spontaneous parametric down conversion process in the non-linear BBO crystal. The signal photon is sent to the interferometer where it excites the standing wave in the middle of the interferometer. The standing wave interacts with a thin absorber by the mechanism of coherent perfect absorption: if the absorber is at the anti-node of the standing wave, strong light-matter interaction takes place and the photon is fully absorbed; if the absorber is at the node of the standing wave, the photon is fully transmitted. The idler photon passes through the combination of three wave plates and acquires Pancharatnam-Berry phase due to the cyclic evolution of the photon polarization in Poincare sphere [2]. The introduced Pancharatnam-Berry phase of the idler photon, that does not go through the interferometer, enables the phase shift inside the interferometer, thus affect the interaction of the signal photon and the absorber. We perform two-photon polarization-sensitive measurements and demonstrate the probability of the signal photon dissipation by the absorber depends on the PB phase applied to the idler photon. 
References
[1] T. Roger, S. Vezzoli, E. Bolduc, J. Valente, J. J. F. Heitz, J. Jeffers, C. Soci, J. Leach, C. Couteau, N. I. Zheludev, and D. Faccio, Nat. Commun. 6, 7031 (2015).
[2] E. Cohen, H. Larocque, F. Bouchard, F. Nejadsattari, Y. Gefen, and E. Karimi, Nat. Rev. Phys. 1, 437–449 (2019).

Non-Local Control of Quantum coherent perfect absorption

Despite being a bona-fide bulk insulator, diamond develops an intriguing two-dimensional (2D) p-type surface conductivity when its surface is terminated by hydrogen and exposed to appropriate surface adsorbate layer such as atmospheric water as a result of the surface transfer doping process. Consequently, the surface of diamond presents a versatile platform for exploiting some of the extraordinary physical and chemical properties of diamond, leading to applications such as chemical/biological sensing and the development of high-power and high-frequency field effect transistors (FETs).

In this talk, I will describe our recent work on the surface transfer doping of diamond by transition metal oxides (TMOs). Specifically, I will show that by interfacing diamond with MoO3 or V2O5 a 2D hole conducting layer with metallic transport behaviours arises on diamond. Magnetotransport studies at low temperature reveal phase coherent transport in the 2D channel with a transition from weak localisation to weak antilocalisation as temperature drops, and are analysed in the context of spin-orbit coupling induced by Rashba effect (Figure 1). The obtained spin-orbit interaction is a few folds higher than that in the air-doped channel or ionic liquid gated channel with similar hole densities, suggesting that the local carrier density could in fact be much higher than that obtained by Hall-effect measurements. We also demonstrate that this surface conducting channel can be exploited to build diamond surface electronic devices such as metal-oxide semiconductor FETs (MOSFETs). Lastly, the prospects for constructing novel quantum devices on diamond surface by making use of this highly tunable 2D conducting layer on diamond are also explored.

Engineering the 2D Hole Gas on Diamond for Carbon-Based Electronics

Quasi-2D (Q2D) lead halide perovskites have emerged as promising materials for light-emitting diodes (LEDs) due to their tunable emission, slowed-down carrier diffusion, and improved stability. However, they are primarily fabricated through solution methods, which hinders its large-scale manufacture and practical applications. Physical-vapor-deposition (PVD) methods have well demonstrated the capability for reproducible, scalable, and layer-by-layer fabrication of high quality organic/inorganic thin films. Herein, for the first time, the full-evaporation fabrication of organic–inorganic hybrid ((BA)2Csn−1PbnBr3n+1) Q2D–3D PeLEDs is demonstrated. The morphology and crystal phase of the perovskite are controlled from 3D to 2D by modulating material composition, annealing temperature, and film thicknesses. The confinement of carriers in 3D layers and the energy funnel effect are discovered and discussed. Importantly, a record high external quantum efficiency (EQE) of 5.3% based on evaporation method is achieved. Moreover, a centimeter-scale PeLED (1.5 cm × 2 cm) is obtained. Furthermore, the T50 lifetime of the device with an initial brightness of 100 cd m−2 is found to be 90 min with a thin layer PMMA passivation, which is among the longest for all PVD processed PeLEDs. Overall, this work casts a solid stepping stone towards the fabrication of high-performance PeLEDs on a large-scale.

Scalable All-Evaporation Fabrication of Efficient Light-Emitting Diodes with Hybrid 2D–3D Perovskite Nanostructures

Upconversion is a nonlinear optical phenomenon that involves the emission of high‐energy photons by sequential absorption of two or more low‐energy excitation photons. Due to their excellent physiochemical properties such as deep penetration depth, little damage to samples, and high chemical stability, upconversion nanoparticles (UCNPs) are extensively applied in bioimaging, biosensing, theranostic, and photochemical reactions. This report will mainly introduce our recent research works of rare earth doped UCNPs for their applications in bioimaging, and cancer therapy.

Upconversion nanoparticles in luminescent detection, imaging, and therapy

Identifying new structurally and electrochemically stable fast ion-conducting solids so far is a slow trial and error process. To rationally accelerate materials design, a dependable rapid screening of materials is required so that promising structures can be shortlisted for higher level computational and experimental characterization. Here we report on our softBV bond-valence site energy-based automated pathway analysis that provides rapid simple visualization of ion transport pathways and yields meaningful approximate predictions of ion transport pathways from crystal structure models within seconds or minutes (a speed-up factor of 3 to 5 orders of magnitude compared to DFT-based NEB methods). Combined with a graphical user interface our software (available free for academic use from http://www.dmse.nus.edu.sg/asn/software.html) aims to enable experimentalists to quickly identify candidate solid electrolytes. Results are benchmarked against experimental and DFT NEB migration barriers. The approach now also comprises an AI-based dopant predictor focusing on bond-valence-based crystal chemical descriptors to assist experimentalists in exploring favourable substitutional doping strategies. 
We will also evaluate the predictability of absolute room temperature conductivities from static energy landscape analysis, bond-valence based empirical MD simulations and ab initio molecular dynamics (AIMD) simulations. While for small fast-ion conductor structures at high temperatures AIMD is the gold standard, the less reliable but computationally cheaper empirical approaches have an advantage in modelling complex disordered materials over longer periods, as they eliminate the need for risky extrapolations from high temperature AIMD simulations. Lastly we explore an extension of our bond-valence based approach by computationally efficient multibody terms in order to optimize their accuracy in studying complex interfaces in energy storage materials.

Tools for automated rapid screening of fast ionic conductors

In this project, two-dimensional (2D) materials are classified into the categories 2D magnetic materials and 2D non-magnetic materials. The 2D magnetic materials are further classified into two categories, namely 2D ferromagnetic materials and 2D anti-ferromagnetic materials. It is important to identify 2D ferromagnetic materials because they are used in various important applications such as data storage mechanisms and spintronic devices. Raw data from the 2D material databases such as 2DMatpedia was used in the project. Classical machine learning involving algorithms such as decision trees and support vector machines were used to classify the data obtained from the above databases into the respective categories anti-ferromagnetic, ferromagnetic and non-magnetic materials. Techniques such as deep learning and convolutional neural networks were also employed to classify the data. The best results for classification of the two-dimensional data into magnetic and non-magnetic materials were obtained for the classical machine learning algorithm, gradient boost algorithm with an accuracy of 93.37%. The most important features of the materials for determining the magnetic properties were band gap, number of magnetic atoms, maximum ionic character, presence of chromium and presence of manganese in that order. The magnetic materials classified were further classified into ferromagnetic and anti-ferromagnetic materials. The best accuracy for this process was obtained to be 94.44% using the classical machine learning algorithm, random forest classifier. The average ionic character, maximum average ionic character, yang-omega, and band-center were the key features for determining whether a material is ferromagnetic or anti-ferromagnetic. A web interface was deployed for the model and can be found here: https://twodferromagnetism-model.herokuapp.com. The code used for obtaining the results can be found here: https://github.com/RiyaBOT/ME4101A-ShethRiyaNimish.

Exploring two-dimensional ferromagnetic materials by machine learning

Many industries use deep learning algorithms to assist in the research and development of products. Therefore, we focus on using deep learning to help predict the parameters of new compounds and to help determine whether the compound is stable.
In this project, we explore the establishment of AI models through different algorithms for machine learning and deep learning. Then, we obtain the important features in the prediction process from the machine learning part, so as to provide a reference for actual research work. The accuracy of the model we get is analyzed and compared to determine the final algorithm and build the model. Using this model, we can predict the formation energy and energy above hull of compounds to determine whether they can exist stably. Finally, we integrate functions into a GUI, so that researchers can use this GUI to make stable predictions of any unknown new compounds easily and conveniently. Throughout the project, the accuracy of the model and the GUI have been continuously improved by us. And show these results in the following part.

Deep learning for accurate predictions of alloy stability

The setting up of the magnetic topological materials database provides the possibility of exploring in this field through the machine learning method. Here, we trained a support vector classification (SVC) model to predict the topological state of the magnetic materials. There are 551 of materials in the topological magnetic materials database, and for each material, the topological state is different for different U values as the date are the DFT results. So, different U values should be different data and finally, 1319 of data are used for the model training. Although the strongly imbalance of the data (only 87 of data are topological insulators (TIs)), the trained model still can reach high precision (0.99) for trivial materials, which means that almost all the un-trivial magnetic materials can be screened by our model. Then, the SVC model is used to predict the topological state of the magnetic materials in MAGNDATA database and select 65 materials as TIs and 15 materials as enforced semimetals (ESs) for all the four times prediction. Topological quantum chemistry method then used to test the topological state of the randomly selected 11 of the un-trivial materials and indicate that 5 of them are ES and 1 is TI. Then first principle method is applied to further inspect the topological state of the only TI. The atom unfilled orbitals and valence electrons are deemed as import features by our model, which is in consistence with the physical analysis results. This SVC model could help screen topological materials from large amount of magnetic materials.

Machine Learning Method to Fastly Screen Topological Magnetic Materials

Two-dimensional topological superconductivity candidate in a van der Waals layered material
Jing-Yang You ,1 Bo Gu ,2 Gang Su,2 and Yuan Ping Feng1,3,*
1Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore 117551
2Kavli Institute for Theoretical Sciences, and CAS Center for Excellence in Topological Quantum Computation, University of Chinese Academy of Sciences, Beijing 100190, China
3Centre for Advanced 2D Materials, National University of Singapore, 6 Science Drive 2, Singapore 117546

Two-dimensional (2D) topological superconductors are highly desired because they not only offer opportunities for exploring novel exotic quantum physics but also possess potential applications in quantum computation. However, there are few reports about 2D superconductors, let alone topological superconductors. Here, we find a 2D monolayer W2N3, which can be exfoliated from its real van der Waals bulk material with much lower exfoliation energy than MoS2, to be a topological metal with exotic topological states at different energy levels. Owing to the Van Hove singularities, the density of states near the Fermi level are high, making the monolayer a compensate metal. Moreover, the monolayer W2N3 is unveiled to be a superconductor with the superconducting transition temperature TC∼22 K and a superconducting gap of about 5 meV based on the anisotropic Migdal-Eliashberg formalism, arising from the strong electron-phonon coupling around the point, and the 2D superconductor is phonon mediated and fits the BCS mechanism with an Ising-type pairing. Because of the strong electron and lattice coupling, the monolayer displays a non-Fermi liquid behavior in its normal states at temperatures lower than 80 K, where the specific heat exhibits T3 behavior and the Wiedemann-Franz law is dramatically violated. Our findings not only provide a platform to study the emergent phenomena in 2D topological superconductors, but also open a door to discover more 2D high-temperature topological superconductors in van der Waals materials.

Reference:
[1] J-Y. You, B Gu, G. Su, Yuan Ping Feng, Physical Review B 103, 104503 (2021).

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