31 Search Results

Heterostructures of topological insulator Bi₂Se₃ on transition metal dichalcogenides (TMDCs) offer a new materials platform for studying novel quantum states by exploiting the interplay among topological orders, charge orders and magnetic orders. Here, the diverse interface attributes, such as material combination, charge re-arrangement, defect and strain, can be utilized to manipulate the quantum properties of this class of materials. Recent experiments of Bi₂Se₃/NbSe₂ heterostructures show signatures of strong Rashba band splitting due to the presence of a BiSe buffer layer, but the atomic level mechanism is not fully understood. We conduct first-principles studies of the Bi₂Se3/BiSe/TMDC heterostructures with five differentmore » TMDC substrates (1T phase VSe₂, MoSe₂, TiSe₂, and 2H phase NbSe₂, MoSe₂). We find significant charge transfer at both BiSe/TMDC and Bi₂Se₃/BiSe interfaces driven by the work function difference, which stabilizes the BiSe layer as an electron donor and creates interface dipole. The electric field of the interface dipole breaks the inversion symmetry in the Bi₂Se₃ layer, leading to the giant Rashba band splitting in two quintuple layers and the recovery of the Dirac point in three quintuple layers, with the latter otherwise only occurring in thicker samples with at least six Bi₂Se₃ quintuple layers. Besides, we find that strain can significantly affect the charge transfer at the interfaces. Our study presents a promising avenue for tuning topological properties in heterostructures of two-dimensional materials, with potential applications in quantum devices.« less

Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) of 41 different chemical motifs on small molecules from simulated C, N, and O K-edge X-ray absorption near-edge structure (XANES) spectra. Our classifiers not only achieve class-balanced accuracies of more than 0.95 but also accurately quantify uncertainty. Here, we also show that including multiple XANES modalities improves predictions notably on average, demonstrating a “multimodal advantage” over any single modality. In addition to structure refinement, ourmore » approach can be generalized to broad applications with molecular design pipelines.« less

X-ray absorption spectroscopy (XAS) is an element-specific materials characterization technique that is sensitive to structural and electronic properties. First-principles simulated XAS has been widely used as a powerful tool to interpret experimental spectra and draw physical insights. Recently, there has also been growing interest in building computational XAS databases to enable data analytics and machine learning applications. However, there are non-trivial differences among commonly used XAS simulation codes, both in underlying theoretical formalism and in technical implementation. Reliable and reproducible computational XAS databases require systematic benchmark studies. In this work, we benchmarked Ti K-edge XAS simulations of ten representative Ti-Omore » binary compounds, which we refer to as the Ti-O-10 dataset, using three state-of-the-art codes: XSPECTRA, OCEAN and exciting. We systematically studied the convergence behavior with respect to the input parameters and developed a workflow to automate and standardize the calculations to ensure converged spectra. Our benchmark comparison considers a 35 eV spectral range starting from the K-edge onset, representative of widely used near-edge spectra. Quantitative comparison over this range is based on Spearman’s rank correlation score (r_sp). Our results show: (1) the two Bethe-Salpeter equation (BSE) codes (OCEAN and exciting) have excellent agreement with an average $$r$$_$sp$ of 0.998; (2) good agreement is obtained between the core-hole potential code (XSPECTRA) and BSE codes (OCEAN and exciting) with an average $$r$$_$sp$ of 0.990, and this smaller $$r$$_$sp$ reflects the noticeable differences in the main edge spectral shape that can be primarily attributed to the difference in the strength of the screened core-hole potential; (3) simulations from both methods overall reproduce well the main experimental spectral features of rutile and anatase, and the different treatments of the screened core-hole potential have visible impact on pre-edge intensities and the peak ratio of the main edge; (4) there exist moderate differences in the relative edge alignment of the three codes with a standard deviation of about 0.2 eV, which arise from multiple contributions including the frozen core approximation, final state effects, and different approximations used for the self-energy correction. In conclusion, our benchmark study provides important standards for first-principles XAS simulations with broad impact in data-driven XAS analysis.« less

Phase transformation via dopant introduction is an emerging strategy for tuning the electronic and catalytic properties of oxides. However, developing a general theory of ternary alloys and their microstructure evolution remains challenging due to the knowledge gaps in how point defects such as dopants lead to the formation of complex or metastable phases. We investigate the influence of Mn concentration and annealing temperature on the microstructure evolution of amorphous Mn-alloyed TiO₂ synthesized via Atomic Layer Deposition. In conclusion, our findings reveal a new crystallization pathway resulting in a direct amorphous TiO₂ to rutile transformation without an intermediate anatase phase andmore » our multi-parameter thermal processing conditions show potential for AI-assisted data acquisition and analysis for studying complex oxide coatings.« less

Two-dimensional (2D) ultrathin (~0.5 nm) aluminosilicate bilayer films, consisting of hexagonal prisms (a.k.a. double 6-membered rings D6R) with acidic bridging hydroxyl groups exposed on the surface, have been previously synthesized on a Ru(0001) surface as a zeolite model system. These structures are helpful for mimicking zeolite catalysts with D6R building blocks, such as chabazite. We performed density functional theory calculations to investigate the monomolecular cracking and dehydrogenation of n-butane molecules over the acidic hydroxyl groups of the 2D model system and compared the reaction energetics with that in bulk chabazite. The intrinsic activation energy barrier is the highest for dehydrogenationmore » and lowest for central C–C bond cracking in bulk chabazite. The trend of intrinsic energy barriers for dehydrogenation and terminal and central C–C bond cracking is reproduced on the 2D aluminosilicate film. Overall, the activation barriers are higher on the 2D film than in bulk chabazite due to the lack of confinement in the former. We further explored the effects of the zeolite channel size on the n-butane adsorption and monomolecular cracking using different bulk nanoporous zeolite frameworks (TON, MEL, MEI, and VFI). We found that as the confinement of channels decreases, n-butane adsorption becomes weaker, and the intrinsic energy barrier of terminal C–C cracking increases. The activation energy barriers (dehydrogenation and terminal and central C–C cracking) on the 2D bilayer film surface, which may be considered as zeolite cages at the infinite cage size limit, are close to that in VFI with a relatively large channel size. Comparing the reaction pathway of n-butane terminal C–C cracking in 3D nanocages and on the surface of the 2D aluminosilicate film revealed that stabilizing the transition states in the 3D nanocages is responsible for the decrease in the intrinsic energy barriers for bulk zeolites.« less

First-principles computational spectroscopy is a critical tool for interpreting experiment, per- forming structure refinement, and developing new physical understanding. Systematically setting up input files for different simulation codes and a diverse class of materials is a challeng- ing task with a very high barrier-to-entry, given the complexities and nuances of each individual simulation package. This task is non-trivial even for experts in the electronic structure field and nearly formidable for non-expert researchers. Lightshow solves this problem by providing a uniform abstraction for writing computational x-ray spectroscopy input files for multiple popular codes, including FEFF, VASP, OCEAN, exciting and XSpectra. Itsmore » extendable framework will also allow the community to easily add new functions and to incorporate new simulation codes.« less

Improved understanding of structural and chemical properties through local experimental probes, such as X-ray absorption near-edge structure (XANES) spectroscopy, is crucial for the understanding and design of functional materials. In recent years, significant advancements have been made in the development of data science approaches for the automated interpretation of XANES structure–spectrum relationships. However, existing studies have primarily focused on crystalline solids and small molecules, while fewer efforts have been devoted to disordered systems. Thus, in this work, we demonstrate the development of neural network models for predicting and interpreting XANES spectra of amorphous carbon (a-C) from local structural descriptors. Comparisonmore » between different structural descriptors expectedly shows that the inclusion of both bond length and bond angle information is necessary for an accurate prediction of the spectra. Among the descriptors considered in this work, we find that the local many-body tensor representation yields the highest accuracy and greatest interpretability so that it can be leveraged to understand the importance of structural motifs in determining XANES spectra. Furthermore, we also discuss performance of neural network models for predicting both local structure features, such as bond lengths and bond angles, and global chemical composition, such as the sp:sp²:sp³ ratio.« less

Layered hybrid halide perovskites gain chirality via the incorporation of chiral organic cations in the spacer layer. These so-called chiral two-dimensional (2D) halide perovskites have attracted considerable interest recently for chiral optoelectronic, spintronic, and ferroelectric applications. However, the effect of temperature on these materials, especially how the structure changes with temperature and its impact on the chiral optoelectronic properties, remains an open question. Here, we study the effect of temperature change on chiral optoelectronic and structural properties through temperature-dependent circularly polarized photoluminescence (CPPL) microscopy and synchrotron powder X-ray diffraction as well as pair distribution function analysis to elucidate the intrinsicmore » chiral optoelectronic and structural variations for R-, S-, and racemic-methylbenzylamine lead iodide. Here our results show that the temperature-induced band gap changes indicate a strong electron–phonon coupling compared to the thermal-induced lattice expansion in chiral 2D perovskites. From powder diffraction measurements, a monotonic lattice expansion is observed on heating with no structural phase transitions over 90–360 K detected for the three samples studied herein. Locally, a strongly anisotropic and even negative expansion in some components of the instantaneous Pb–I pair-distance distribution is suggestive of coupling between dynamical intralayer distortions and lattice expansion. This work provides insights into the fundamental understanding of the temperature effect on chiral optoelectronic and structural properties of chiral 2D perovskites, which can lead to new chiral materials design strategies for future chiral optoelectronic applications.« less

As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important for their robust application. Uncertainty-aware machine learning methods have been used in select applications, but largely for scalar properties. In this work, we showcase an exemplary study in which neural network ensembles are used to predict the x-ray absorption spectra of small molecules, as well as their pointwise uncertainty, from local atomic environments. The performance of the resulting surrogate clearly demonstrates quantitative correlation between errors relative to ground truth and the predicted uncertainty estimates.more » Significantly, the model provides an upper bound on the expected error. Specifically, an important quality of this uncertainty-aware model is that it can indicate when the model is predicting on out-of-sample data. This allows for its integration with large-scale sampling of structures together with active learning or other techniques for structure refinement. Additionally, our models can be generalized to larger molecules than those used for training, and also successfully track uncertainty due to random distortions in test molecules. While we demonstrate this workflow on a specific example, ensemble learning is completely general. We believe it could have significant impact on ML-enabled forward modeling of a broad array of molecular and materials properties.« less

Transition metal dichalcogenides exhibit many fascinating properties including superconductivity, magnetic orders, and charge density wave. The combination of these features with a non-trivial band topology opens the possibility of additional exotic states such as Majorana fermions and quantum anomalous Hall effect. Here, we report on photon-energy and polarization dependent spin-resolved angle-resolved photoemission spectroscopy experiments on single crystal 1T-VSe₂, revealing an unexpected band inversion and emergent Dirac nodal arc with spin-momentum locking. Density functional theory calculations suggest a surface lattice strain could be the driving mechanism for the topologically nontrivial electronic structure of 1T-VSe₂.