arXiv subjects

Ting Bao

Publications and source records attributed to Ting Bao.

9 recordsLinked to original sources

arXiv2025

Universal giant spin Hall effect in moire metal

While moir\'e phenomena have been extensively studied in low-carrier-density systems such as graphene and semiconductors, their implications for metallic systems with large Fermi surfaces remain largely unexplored. Using GPU-accelerated large-scale ab-initio quantum transport simulations, we investigate spin transport in two distinct platforms: twisted bilayer MoTe$_2$ (semiconductor, from lightly to heavily doping) and NbX$_2$ ($X$ = S, Se; metals). In twisted MoTe$_2$, the spin Hall conductivity (SHC) evolves from $4\tfrac{e}{4\pi}$ at $5.09^\circ$ to $10\tfrac{e}{4\pi}$ at $1.89^\circ$, driven by the emergence of multiple isolated Chern bands. Remarkably, in heavily doped metallic regimes--without isolated Chern bands--we observe a universal amplification of the spin Hall effect from Fermi surface reconstruction under long-wavelength potential, with the peak SHC tripling from $6\tfrac{e}{4\pi}$ at $5.09^\circ$ to $17\tfrac{e}{4\pi}$ at $3.89^\circ$. For prototypical moir\'e metals like twisted NbX$_2$, we identify a record SHC of $-17\tfrac{e}{4\pi}$ (-5200 $(\hbar / e)S/cm$ in 3D units), surpassing all known bulk materials. These results establish moir\'e engineering as a powerful strategy for enhancing spin-dependent transport, and advancing ab-initio methodologies to bridge atomic-scale precision with device-scale predictions in transport simulations.

cond-mat.mes-hall↗

arXiv2025

Transfer learning electronic structure: millielectron volt accuracy for sub-million-atom moir\'e semiconductor

The integration of density functional theory (DFT) with machine learning enables efficient \textit{ab initio} electronic structure calculations for ultra-large systems. In this work, we develop a transfer learning framework tailored for long-wavelength moir\'e systems. To balance efficiency and accuracy, we adopt a two-step transfer learning strategy: (1) the model is pre-trained on a large dataset of computationally inexpensive non-twisted structures until convergence, and (2) the network is then fine-tuned using a small set of computationally expensive twisted structures. Applying this method to twisted MoTe$_2$, the neural network model generates the resulting Hamiltonian for a 1000-atom system in 200 seconds, achieving a mean absolute error below 0.1 meV. To demonstrate $O(N)$ scalability, we model nanoribbon systems with up to 0.25 million atoms ($\sim9$ million orbitals), accurately capturing edge states consistent with predicted Chern numbers. This approach addresses the challenges of accuracy, efficiency, and scalability, offering a viable alternative to conventional DFT and enabling the exploration of electronic topology in large scale moir\'e systems towards simulating realistic device architectures.

cond-mat.mtrl-sci↗

arXiv2024

Imaging moir\'e flat bands and Wigner molecular crystals in twisted bilayer MoTe2

Two-dimensional semiconducting moir\'e materials have emerged as a highly tunable platform for exploring novel quantum phenomena. Recently, tMoTe2 has attracted significant attentions due to the observation of the long-sought fractional quantum anomalous Hall effect. However, a comprehensive microscopic understanding of the tMoTe2 moir\'e superlattice remains elusive. Here, we report STM/STS studies in dual-gated tMoTe2 moir\'e devices with twist angles ranging from 2.3 to 3.8 deg. The device consists of two independent back-gates, one enables an ohmic contact for tMoTe2, while the other fine-tunes the Fermi level of tMoTe2. This dual-gate control enables direct measurement of the electronic structure in tMoTe2 under varied displacement fields and moir\'e filling factors, by fine tuning the gate voltage and the tip bias. Our STS spectra and spatial imaging reveal that the low-energy moir\'e flat bands are predominantly localized in the XM and MX regions of the moir\'e superlattice. At zero E-field, these bands form a honeycomb lattice with non-trivial topology, whereas an applied E-field drives a transition into two distinct triangular lattices with trivial topology. The spatial distributions align with large-scale first-principle calculations, demonstrating that the topological flat bands arise from the K-valley hybridization between the top and bottom MoTe2 layers. Furthermore, we show that the effective moir\'e potential depth can be controlled via gate and tip biases. At sufficient potential depths, we observe the emergence of Wigner molecular crystals, transitioning MX triangular lattice into a Kagome lattice at MX moir\'e filling factor 3. These results elucidate the microscopic origin of topological flat bands in tMoTe2 and demonstrate electric-field control of topology and correlated electronic orders, paving the way to engineer exotic quantum phases in moir\'e simulators.

cond-mat.mes-hall↗

arXiv2024

Universal materials model of deep-learning density functional theory Hamiltonian

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian (DeepH), enabling computational modeling of the complicated structure-property relationship of materials in general. By constructing a large materials database and substantially improving the DeepH method, we obtain a universal materials model of DeepH capable of handling diverse elemental compositions and material structures, achieving remarkable accuracy in predicting material properties. We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models. This work not only demonstrates the concept of DeepH's universal materials model but also lays the groundwork for developing large materials models, opening up significant opportunities for advancing artificial intelligence-driven materials discovery.

physics.comp-ph↗

arXiv2024

Deep-Learning Database of Density Functional Theory Hamiltonians for Twisted Materials

Moir\'e-twisted materials have garnered significant research interest due to their distinctive properties and intriguing physics. However, conducting first-principles studies on such materials faces challenges, notably the formidable computational cost associated with simulating ultra-large twisted structures. This obstacle impedes the construction of a twisted materials database crucial for datadriven materials discovery. Here, by using high-throughput calculations and state-of-the-art neural network methods, we construct a Deep-learning Database of density functional theory (DFT) Hamiltonians for Twisted materials named DDHT. The DDHT database comprises trained neural-network models of over a hundred homo-bilayer and hetero-bilayer moir\'e-twisted materials. These models enable accurate prediction of the DFT Hamiltonian for these materials across arbitrary twist angles, with an averaged mean absolute error of approximately 1.0 meV or lower. The database facilitates the exploration of flat bands and correlated materials platforms within ultra-large twisted structures.

cond-mat.mtrl-sci↗

arXiv2023

Transfer learning relaxation, electronic structure and continuum model for twisted bilayer MoTe$_2$

Large-scale moir\'e systems are extraordinarily sensitive, with even minute atomic shifts leading to significant changes in electronic structures. Here, we investigate the lattice relaxation effect on moir\'e band structures in twisted bilayer MoTe$_2$ with two approaches: (a) large-scale plane-wave basis first principle calculation down to $2.88^{\circ}$, (b) transfer learning structure relaxation + local-basis first principles calculation down to $1.1^{\circ}$. We use two types of van der Waals corrections: the D2 method of Grimme and the density-dependent energy correction, and find that the density-dependent energy correction yields a continuous evolution of bandwidth with twist angles. Based on the above results. we develop a more complete continuum model with a single set of parameters for a wide range of twist angles, and perform many-body simulations at $\nu=-1,-2/3, -1/3$.

cond-mat.str-el↗

arXiv2023

Revealing the two-dimensional electronic structure and anisotropic superconductivity in a natural van der Waals superlattice (PbSe)$_{1.14}$NbSe$_2$

Van der Waals superlattices are important for tailoring the electronic structures and properties of layered materials. Here we report the superconducting properties and electronic structure of a natural van der Waals superlattice (PbSe)$_{1.14}$NbSe$_2$. Anisotropic superconductivity with a transition temperature $T_c$ = 5.6 $\pm$ 0.1 K, which is higher than monolayer NbSe$_2$, is revealed by transport measurements on high-quality samples. Angle-resolved photoemission spectroscopy (ARPES) measurements reveal the two-dimensional electronic structure and a charge transfer of 0.43 electrons per NbSe$_2$ unit cell from the blocking PbSe layer. In addition, polarization-dependent ARPES measurements reveal a significant circular dichroism with opposite contrast at K and K' valleys, suggesting a significant spin-orbital coupling and distinct orbital angular momentum. Our work suggests natural van der Waals superlattice as an effective pathway for achieving intriguing properties distinct from both the bulk and monolayer samples.

cond-mat.supr-con↗

arXiv2021

A Bayesian Approach for In-Situ Stress Prediction and Uncertainty Quantification for Subsurface Engineering

Many subsurface engineering applications require accurate knowledge of the in-situ state of stress for their safe design and operation. Existing methods to meet this need primarily include field measurements for estimating one or more of the principal stresses from a borehole, or optimization methods for constructing a 3D geomechanical model in terms of geophysical measurements. These methods, however, often contain considerable uncertainty in estimating the state of stress. In this paper, we build on a Bayesian approach to quantify uncertainty in stress estimations for subsurface engineering applications. This approach can provide an estimate of the 3D distribution of stress throughout the volume of interest and provide an estimate of the uncertainty arising from the stress measurement, the rheology parameters, and a paucity of measurements. The value of this approach is demonstrated using stress measurements from the In Salah carbon storage site, which was one of the first industrial carbon capture and storage projects in the world. This demonstration shows the application of this Bayesian approach for estimating the initial state of stress for In Salah and quantifying the uncertainty in the estimated stress. Also, an assessment of a maximum injection pressure to prevent geomechanical risks from CO2 injection pressures is provided in terms of the probability distribution of the minimum principal stress quantified by the approach. With the In Salah case study, this paper demonstrates that using the Bayesian approach can provide additional insights for site explorations and/or project operations to make informed-site decisions for subsurface engineering applications.

physics.geo-ph↗

arXiv2021

Autonomous Inversion of In Situ Deformation Measurement Data for CO2 Storage Decision Support

Current methods of estimating the change in stress caused by injecting fluid into subsurface formations require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method to perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. The approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.

physics.geo-ph↗