Proposed Quantum Twisting Scanning Probe Microscope over Twisted Bilayer Graphene.

Twisted bilayer graphene (TBG) has the natural merits of tunable flat bands and localized states distributed as a triangular lattice. However, the application of this state remains obscure. By density functional theory (DFT) and pz orbital tight-binding model calculations, we investigate the tip-shaped electrostatic potential of top valence electrons of TBG at half filling. Adsorption energy scanning of molecules above the TBG reveals that this tip efficiently attracts molecules selectively to AA-stacked or AB-stacked regions. Tip shapes can be controlled by their underlying electronic structure, with electrons of low bandwidth exhibiting a more localized feature. Our results indicate that TBG tips offer applications in noninvasive and nonpolluting measurements in scanning probe microscopy and theoretical guidance for 2D material-based probes.

Complex-Valued K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Large-Scale Hybrid Functional Enabled Ab Initio Molecular Dynamics Simulations within Plane Waves.

K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (ISDF) decomposition for hybrid functional electronic structure calculations. Real-valued K-means clustering for accelerating the ISDF decomposition has been demonstrated for large-scale hybrid functional enabled ab initio molecular dynamics (hybrid AIMD) simulations within plane-wave basis sets where the Kohn-Sham orbitals are real-valued. However, it is unclear whether such K-means clustering works for complex-valued Kohn-Sham orbitals. Here, we propose an improved weight function defined as the sum of the square modulus of complex-valued Kohn-Sham orbitals in K-means clustering for hybrid AIMD simulations. Numerical results demonstrate that the K-means algorithm with a new weight function yields smoother and more delocalized interpolation sampling points, resulting in smoother energy potential, smaller energy drift, and longer time steps for hybrid AIMD simulations compared to the previous weight function used in the real-valued K-means algorithm. In particular, we find that this improved algorithm can obtain more accurate oxygen-oxygen radial distribution functions in liquid water molecules and a more accurate power spectrum in crystal silicon dioxide compared to the previous K-means algorithm. Finally, we describe a massively parallel implementation of this ISDF decomposition to accelerate large-scale complex-valued hybrid AIMD simulations containing thousands of atoms (2,744 atoms), which can scale up to 5,504 CPU cores on modern supercomputers.

Designing Two-Dimensional Versatile Room-Temperature Ferromagnets via Assembling Large-Scale Magnetic Quantum Dots.

Two-dimensional (2D) ferromagnets possess astonishing potential in new-concept spintronics. However, most of the reported intrinsic 2D ferromagnets show a low Curie temperature far below room temperature. Here, we propose a series of 2D magnetic covalent and metal organic frameworks (COFs/MOFs) by assembling triangular zigzag graphene quantum dots (TZGDs) with various linkages, involving small-sized TZGDs, nonmetal atoms, magnetic metal atoms, and molecules. Upon first-principles calculations, we demonstrate 2D magnetic semiconductors with an enhanced Curie temperature of up to 472 K can be realized through the strong p(d)-p direct exchange interaction between TZGDs and linkages. Particularly, the TZGD size hardly affects the Curie temperature, whereas linkages can modulate the Curie temperature significantly. The TZGD size and linkages can regulate the electronic and magnetic properties of TZGD-based 2D ferromagnets. Our results confirm the possibility of designing 2D ferromagnets based on TZGDs and motivate the research of 2D ferromagnets on magnetic quantum dots and molecular magnets.

Realizing Effective Cubic-Scaling Coulomb Hole Plus Screened Exchange Approximation in Periodic Systems via Interpolative Separable Density Fitting with a Plane-Wave Basis Set.

The GW approximation is an effective way to accurately describe the single-electron excitations of molecules and the quasiparticle energies of solids. However, a perceived drawback of the GW calculations is their high computational cost and large memory usage, which limit their applications to large systems. Herein, we demonstrate an accurate and effective low-rank approximation to accelerate non-self-consistent GW (G0W0) calculations under the static Coulomb hole plus screened exchange (COHSEX) approximation for periodic systems. Our approach is to adopt the interpolative separable density fitting (ISDF) decomposition and Cauchy's integral to construct low-rank representations of the dielectric matrix ϵ and self-energy matrix Σ. This approach reduces the number of floating point operations from O(Ne4) to O(Ne3) and requires a much smaller memory footprint. Two methods are used to select the interpolation points in ISDF, including the standard QR factorization with column pivoting (QRCP) procedure and the machine learning K-means clustering (K-means) algorithm. We demonstrate that these two methods can yield similar accuracy for both molecules and solids at much lower computational cost. In particular, K-means clustering can significantly reduce the computational cost of selecting the interpolation points by an order of magnitude compared to QRCP, resulting in an overall speedup factor of about ten times ISDF accelerated the static COHSEX calculations compared with conventional COHSEX approximation.

Machine Learning K-Means Clustering Algorithm for Interpolative Separable Density Fitting to Accelerate Hybrid Functional Calculations with Numerical Atomic Orbitals.

The interpolative separable density fitting (ISDF) is an efficient and accurate low-rank decomposition method to reduce the high computational cost and memory usage of the Hartree-Fock exchange (HFX) calculations with numerical atomic orbitals (NAOs). In this work, we present a machine learning K-means clustering algorithm to select the interpolation points in ISDF, which offers a much cheaper alternative to the expensive QR factorization with column pivoting (QRCP) procedure. We implement this K-means-based ISDF decomposition to accelerate hybrid functional calculations with NAOs in the HONPAS package. We demonstrate that this method can yield a similar accuracy for both molecules and solids at a much lower computational cost. In particular, K-means can remarkably reduce the computational cost of selecting the interpolation points by nearly two orders of magnitude compared to QRCP, resulting in a speedup of ∼10 times for ISDF-based HFX calculations.

Interpolative Separable Density Fitting Decomposition for Accelerating Hartree-Fock Exchange Calculations within Numerical Atomic Orbitals.

The high cost associated with the evaluation of Hartree-Fock exchange (HFX) makes hybrid functionals computationally challenging for large systems. In this work, we present an efficient way to accelerate HFX calculations with numerical atomic basis sets. Our approach is based on the recently proposed interpolative separable density fitting (ISDF) decomposition to construct a low-rank approximation of the HFX matrix, which avoids explicit calculations of the electron repulsion integrals (ERIs) and significantly reduces the computational cost. We implement the ISDF method for hybrid functional (PBE0) calculations in the HONPAS package. We take benzene and polycyclic aromatic hydrocarbon molecules as examples and demonstrate that hybrid functionals with ISDF yield quite promising results at a significantly reduced computational cost. Especially, the ISDF approach reduces the total cost of the evaluating HFX matrix by nearly 2 orders of magnitude compared to conventional approaches of direct evaluation of ERIs.

Tunable Schottky and Ohmic contacts in graphene and tellurene van der Waals heterostructures.

We systematically investigate the effects of external electric field and interlayer coupling on the electronic structures and contact characteristics of hybrid graphene and tellurene (G/Te) van der Waals heterostructures (vdWHs) based on first-principles calculations. Our results show that the G/α-Te interface is formed by an n-type Schottky contact with a negligible Schottky barrier height (SBH), while the G/ß-Te interface is formed by a p-type Schottky contact with a SBH of 0.51 eV. By applying external electric fields perpendicular to the G/Te interfaces or changing the interlayer distance between the graphene and tellurene monolayers, both Schottky barriers and contact types (n-type Schottky, p-type Schottky, and Ohmic) at the G/Te interfaces can be effectively modulated. The changes in charge transfer, as well as the corresponding interface dipole and potential step with the external electric field and interlayer coupling, are revealed to account for the reason for tunable Schottky and Ohmic contacts at the G/Te interfaces. Therefore, the G/Te vdWHs show tunable Schottky and Ohmic contacts with promising applications of graphene-based field-effect transistors in future experiments.

Interpolative Separable Density Fitting for Accelerating Two-Electron Integrals: A Theoretical Perspective.

Low-rank approximations have long been considered an efficient way to accelerate electronic structure calculations associated with the evaluation of electron repulsion integrals (ERIs). As an accurate and efficient algorithm for compressing the ERI tensor, the interpolative separable density fitting (ISDF) decomposition has recently attracted great attention in this context. In this perspective, we introduce the ISDF decomposition from the theoretical aspects and technique details. The ISDF decomposition can construct a fully separable low-rank approximation (tensor hypercontraction factorization) of ERIs in real space with a cubic cost, offering great flexibility for accelerating high-scaling electronic structure calculations. We review the typical applications of ISDF in hybrid functionals, time-dependent density functional theory, and GW approximation. Finally, we discuss the promising directions for future development of ISDF.

Efficient implementation of analytical gradients for periodic hybrid functional calculations within fitted numerical atomic orbitals from NAO2GTO.

The NAO2GTO scheme provides an efficient way to evaluate the electron repulsion integrals (ERIs) over numerical atomic orbitals (NAOs) with auxiliary Gaussian-type orbitals (GTOs). However, the NAO2GTO fitting will significantly impact the accuracy and convergence of hybrid functional calculations. To address this issue, here we propose to use the fitted orbitals as a new numerical basis to properly handle the mismatch between NAOs and fitted GTOs. We present an efficient and linear-scaling implementation of analytical gradients of Hartree-Fock exchange (HFX) energy for periodic HSE06 calculations with fitted NAOs in the HONPAS package. In our implementation, the ERIs and their derivatives for HFX matrix and forces are evaluated analytically with the auxiliary GTOs, while other terms are calculated using numerically discretized GTOs. Several integral screening techniques are employed to reduce the number of required ERI derivatives. We benchmark the accuracy and efficiency of our implementation and demonstrate that our results of lattice constants, bulk moduli, and band gaps of several typical semiconductors are in good agreement with the experimental values. We also show that the calculation of HFX forces based on a master-worker dynamic parallel scheme has a very high efficiency and scales linearly with respect to system size. Finally, we study the geometry optimization and polaron formation due to an excess electron in rutile TiO2 by means of HSE06 calculations to further validate the applicability of our implementation.

Ionic liquid functionalized trapezoidal Zn-MOF nanosheets integrated with gold nanoparticles for photoelectrochemical immunosensing alpha-fetoprotein.

An imidazolium based ionic liquid was successfully prepared and used as an organic ligand to coordinate with Zn2+ to prepare trapezoidal metal-organic frameworks (Zn-MOF) nanosheets. Then, gold nanoparticles (AuNPs) were integrated onto Zn-MOF nanosheets surface to produce AuNPs@Zn-MOF nanocomposites by in-situ reduction of chloroauric acid. AuNPs with size less than 5 nm were uniformly dispersed on the entire surface of Zn-MOF nanosheets. AuNPs can significantly promote the photocurrent response of Zn-MOF nanosheets and supply an efficient photoelectrochemical sensing platform for fabricating an immunosensor for alpha-fetoprotein (AFP). For AFP determination, the photocurrent response of the immunosensor was linearly related to the logarithm of AFP concentration in the range of 0.005-15.0 ng/mL. The detection limit was calculated to be 1.88 pg/mL. The PEC immunosensor can be facilely fabricated, and provided some superior analytical characteristics such as excellent selectivity, sensitivity, stability and reproducibility for AFP determination. Practicability of the photoelectrochemical immunosensor was demonstrated by using it in assaying AFP in clinical serum samples.

High thermoelectric and reversible p-n-p conduction type switching integrated in dimetal chalcogenide.

The subject of the involved phase transition in solid materials has formed not only the basis of materials technology but also the central issue of solid-state chemistry for centuries. The ability to design and control the required changes in physical properties within phase transition becomes key prerequisite for the modern functionalized materials. Herein, we have experimentally achieved the high thermoelectric performance (ZT value reaches 1.5 at 700 K) and reversible p-n-p semiconducting switching integrated in a dimetal chalcogenide, AgBiSe(2) during the continuous hexagonal-rhombohedral-cubic phase transition. The clear-cut evidences in temperature-dependent positron annihilation and Raman spectra confirmed that the p-n-p switching is derived from the bimetal atoms exchange within phase transition, whereas the full disordering of bimetal atoms after the bimetal exchange results in the high thermoelectric performance. The combination of p-n-p switching and high thermoelectric performance enables the dimetal chalcogenides perfect candidates for novel multifunctional electronic devices. The discovery of bimetal atoms exchange during the phase transition brings novel phenomena with unusual properties which definitely enrich solid-state chemistry and materials science.

Low-Rank Approximations Accelerated Plane-Wave Hybrid Functional Calculations with k-Point Sampling.

The low-rank approximations of the adaptively compressed exchange (ACE) operator and interpolative separable density fitting (ISDF) algorithms significantly reduce the computational cost and memory usage of hybrid functional calculations in real space, but the lack of k-point sampling hinders their implementation in reciprocal space for periodic systems with the plane-wave basis set. Here, we combine the ACE operator and ISDF decomposition into a new ACE-ISDF algorithm for periodic systems in reciprocal space with k-point sampling. On the basis of the ACE-ISDF algorithm with the improved reciprocal space ACE operator and k-point Fourier convolution, the time complexity of the hybrid functional calculation is reduced from O(Ne4Nk2) to O(Ne3Nklog(Nk)) (Ne and Nk are the number of electrons and k-points, respectively) with a much smaller prefactor and much lower memory consumption compared to the standard method for periodic systems with a plane-wave basis set.

High performance computing of DGDFT for tens of thousands of atoms using millions of cores on Sunway TaihuLight.

High performance computing (HPC) is a powerful tool to accelerate the Kohn-Sham density functional theory (KS-DFT) calculations on modern heterogeneous supercomputers. Here, we describe a massively parallel implementation of discontinuous Galerkin density functional theory (DGDFT) method on the Sunway TaihuLight supercomputer. The DGDFT method uses the adaptive local basis (ALB) functions generated on-the-fly during the self-consistent field (SCF) iteration to solve the KS equations with high precision comparable to plane-wave basis set. In particular, the DGDFT method adopts a two-level parallelization strategy that deals with various types of data distribution, task scheduling, and data communication schemes, and combines with the master-slave multi-thread heterogeneous parallelism of SW26010 processor, resulting in large-scale HPC KS-DFT calculations on the Sunway TaihuLight supercomputer. We show that the DGDFT method can scale up to 8,519,680 processing cores (131,072 core groups) on the Sunway TaihuLight supercomputer for studying the electronic structures of two-dimensional (2D) metallic graphene systems that contain tens of thousands of carbon atoms.

Parallel Implementation of Large-Scale Linear Scaling Density Functional Theory Calculations With Numerical Atomic Orbitals in HONPAS.

Linear-scaling density functional theory (DFT) is an efficient method to describe the electronic structures of molecules, semiconductors, and insulators to avoid the high cubic-scaling cost in conventional DFT calculations. Here, we present a parallel implementation of linear-scaling density matrix trace correcting (TC) purification algorithm to solve the Kohn-Sham (KS) equations with the numerical atomic orbitals in the HONPAS package. Such a linear-scaling density matrix purification algorithm is based on the Kohn's nearsightedness principle, resulting in a sparse Hamiltonian matrix with localized basis sets in the DFT calculations. Therefore, sparse matrix multiplication is the most time-consuming step in the density matrix purification algorithm for linear-scaling DFT calculations. We propose to use the MPI_Allgather function for parallel programming to deal with the sparse matrix multiplication within the compressed sparse row (CSR) format, which can scale up to hundreds of processing cores on modern heterogeneous supercomputers. We demonstrate the computational accuracy and efficiency of this parallel density matrix purification algorithm by performing large-scale DFT calculations on boron nitrogen nanotubes containing tens of thousands of atoms.

Energy level realignment in weakly interacting donor-acceptor binary molecular networks.

Understanding the effect of intermolecular and molecule-substrate interactions on molecular electronic states is key to revealing the energy level alignment mechanism at organic-organic heterojunctions or organic-inorganic interfaces. In this paper, we investigate the energy level alignment mechanism in weakly interacting donor-acceptor binary molecular superstructures, comprising copper hexadecafluorophthalocyanine (F16CuPc) intermixed with copper phthalocyanine (CuPc), or manganese phthalocynine (MnPc) on graphite. The molecular electronic structures have been systematically studied by in situ ultraviolet photoelectron spectroscopy (UPS) and low-temperature scanning tunneling microscopy/spectroscopy (LT-STM/STS) experiments and corroborated by density functional theory (DFT) calculations. As demonstrated by the UPS and LT-STM/STS measurements, the observed unusual energy level realignment (i.e., a large downward shift in donor HOMO level and a corresponding small upward shift in acceptor HOMO level) in the CuPc-F16CuPc binary superstructures originates from the balance between intermolecular and molecule-substrate interactions. The enhanced intermolecular interactions through the hydrogen bonding between neighboring CuPc and F16CuPc can stabilize the binary superstructures and modify the local molecular electronic states. The obvious molecular energy level shift was explained by gap-state-mediated interfacial charge transfer.

Rocking-chair configuration in ultrathin lithium vanadate-graphene hybrid nanosheets for electrical modulation.

The ability to control electronic property of a material by externally applied voltage is greatly anticipated in modern electronics, and graphene provide potential application foreground for this issue on account of its exotic ambipolar transport property. In this study, we proposed that inorganic-graphene intercalated nanosheet is an effective solution to optimize the transport property of graphene. As an example, lithium vanadate-graphene (LiVO-graphene) alternately intercalated nanosheets were designed and successfully synthesized. Theoretical calculation implied that its rocking chair configuration may provide a new pathway to switch the carrier in graphene layer between p-type and n-type while the position of embedded Li ions is controlled by an external field. Thus, a demo transistor was fabricated with layer-by-layer overlapping of LiVO-graphene nanosheets which proved that this inorganic-graphene structure could be used for electrical modulation in electronic devices.