npj Computational Materials最新文献

Low-dimensional multiferroics, which simultaneously possess at least two primary ferroic order parameters, hold great promise for post-Moore electronic devices. However, intrinsic one-dimensional (1D) multiferroics with the coexistence of ferroelectricity and ferromagnetism are still yet to be realized, which will be not only crucial for exploring the interplay between low-dimensionality and ferroelectric/ferromagnetic ordering but also significant in rendering application approaches for high density information technologies. Here, we present a theoretical prediction of intrinsic multiferroicity in 1D molybdenum oxytrihalides nanowires, especially focusing on MoOBr₃ nanowires which could be readily extracted from experimentally synthesized van der Waals MoOBr₃ bulk materials. Due to the spatial inversion symmetry spontaneously broken by Mo atoms’ displacements, MoOBr₃ nanowires exhibit 1D ferroelectricity with small coercive electric field and exceptional Curie temperature (~570 K). Additionally, MoOBr₃ nanowires also possess 1D antiferroelectric metastable states. On the other hand, both ferroelectric and antiferroelectric MoOBr₃ nanowires exhibit ferromagnetic ordering on account of the half-filled Mo-d_yz orbitals, a moderate tensile strain (~5%) can greatly boost the spontaneous polarization (~40%) and a mild compress strain (~−2%) may readily switch the magnetic easy axis of ferroelectric MoOBr₃ nanowires. Our work holds potential candidates for developing innovative devices that exploit intrinsic multiferroic properties, enabling advancements in novel electronic and spintronic applications.

Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. While these models exhibit robustness within familiar chemical domains, reliability wanes when applied to new spaces. To address this challenge, we present a multi-tiered multi-task learning framework empowered with advanced machine-crafted polymer fingerprinting algorithms and data fusion techniques. This framework combines scarce “high-fidelity” experimental data with abundant diverse “low-fidelity” simulation or synthetic data, resulting in predictive models that display a high level of generalizability across novel chemical spaces. Additionally, this multi-task scheme capitalizes on known physics and interrelated properties, such as gas diffusivity and solubility, both of which are closely tied to permeability. By amalgamating high throughput generated simulation data with available experimental data for gas permeability, diffusivity, and solubility for various gases, we construct multi-task deep learning models. These models can simultaneously predict all three properties for all gases under consideration, with markedly enhanced predictive accuracy, particularly compared to traditional models reliant solely on experimental data for a singular property. This strategy underscores the potential of coupling high-throughput classical simulations with data fusion methodologies to yield state-of-the-art property predictors, especially when experimental data for targeted properties is scarce.

Point defect qubits in semiconductors have demonstrated their outstanding capabilities for high spatial resolution sensing generating broad multidisciplinary interest. Hexagonal boron nitride (hBN) hosting point defect qubits have recently opened up new horizons for quantum sensing by implementing sensing foils. The sensitivity of point defect sensors in hBN is currently limited by the linewidth of the magnetic resonance signal, which is broadened due to strong hyperfine couplings. Here, we report on a vacancy-related spin qubit with an inherently low symmetry configuration, the VB2 center, giving rise to a reduced magnetic resonance linewidth at zero magnetic fields. The VB2 center is also equipped with a classical memory that can be utilized for storing population information. Using scanning transmission electron microscopy imaging, we confirm the existence of the VB2 configuration in free-standing monolayer hBN.

Recent advancements in machine learning (ML) have revolutionized the field of high-performance materials design. However, developing robust ML models to decipher intricate structure-property relationships in materials remains challenging, primarily due to the limited availability of labeled datasets with well-characterized crystal structures. This is particularly pronounced in materials where functional properties are closely intertwined with their crystallographic symmetry. We introduce a self-supervised probabilistic model (SSPM) that autonomously learns unbiased atomic representations and the likelihood of compounds with given crystal structures, utilizing solely the existing crystal structure data from materials databases. SSPM significantly enhances the performance of downstream ML models by efficient atomic representations and accurately captures the probabilistic relationships between composition and crystal structure. We showcase SSPM’s capability by discovering shape memory alloys (SMAs). Amongst the top 50 predictions, 23 have been confirmed as SMAs either experimentally or theoretically, and a previously unknown SMA candidate, MgAu, has been identified.

It is a time-consuming and costly process to develop affordable and high-performance organic photovoltaic materials. Computational methods are essential for accelerating the material discovery process by predicting power conversion efficiencies (PCE). In this study, we propose a deep learning-based framework (DeepAcceptor) to design and discover highly efficient small molecule acceptor materials. Specifically, an experimental dataset is constructed by collecting acceptor data from publications. Then, a deep learning-based model is customized to predict PCEs by applying graph representation learning to Bidirectional Encoder Representations from Transformers (BERT), with the atom, bond, and connection information in acceptor molecular structures as the input (abcBERT). The computational dataset derived from density functional theory (DFT) calculations and the experimental dataset from literature are used to pre-train and fine-tune the model, respectively. The abcBERT model outperforms other state-of-the-art models for the PCE prediction with MAE = 1.78 and R² = 0.67 on the test set. A molecular generation and screening process is built to find new high-performance acceptors for PM6. Three discovered candidates are further validated by experiment, and the best PCE reaches 14.61%. The released user-friendly interface of DeepAcceptor greatly boosts the accessibility and efficiency of designing and discovering high-performance acceptors. Altogether, the DeepAcceptor framework with abcBERT is promising to predict the PCE and accelerate the discovery of high-performance acceptor materials.

Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities. This necessitates the development of the machine learning methods for discovery of physical and chemical phenomena from the data, such as manifestations of symmetry breaking phenomena in electron and scanning tunneling microscopy images, or variability of the nanoparticles. Variational autoencoders (VAEs) are emerging as a powerful paradigm for the unsupervised data analysis, allowing to disentangle the factors of variability and discover optimal parsimonious representation. Here, we summarize recent developments in VAEs, covering the basic principles and intuition behind the VAEs. The invariant VAEs are introduced as an approach to accommodate scale and translation invariances present in imaging data and separate known factors of variations from the ones to be discovered. We further describe the opportunities enabled by the control over VAE architecture, including conditional, semi-supervised, and joint VAEs. Several case studies of VAE applications for toy models and experimental datasets in Scanning Transmission Electron Microscopy are discussed, emphasizing the deep connection between VAE and basic physical principles. Python codes and datasets discussed in this article are available at https://github.com/saimani5/VAE-tutorials and can be used by researchers as an application guide when applying these to their own datasets.

The multi-tier GW+EDMFT scheme is an ab-initio method for calculating the electronic structure of correlated materials. While the approach is free from ad-hoc parameters, it requires a selection of appropriate energy windows for describing low-energy and strongly correlated physics. In this study, we test the consistency of the multi-tier description by considering different low-energy windows for a series of cubic SrXO₃ (X = V, Cr, Mn) perovskites. Specifically, we compare the 3-orbital t_2g model, the 5-orbital t_2g + e_g model, the 12-orbital t_2g + O_p model, and (in the case of SrVO₃) the 14-orbital t_2g + e_g + O_p model and compare the results to available photoemission and X-ray absorption measurements. The multi-tier method yields consistent results for the t_2g and t_2g + e_g low-energy windows, while the models with O_p states produce stronger correlation effects and mostly agree well with experiment, especially in the unoccupied part of the spectrum. We also discuss the consistency between the fermionic and bosonic spectral functions and the physical origin of satellite features, and present momentum-resolved charge susceptibilities.

Two-dimensional materials possessing intrinsic multiferroic properties have long been sought to harness the magnetoelectric coupling in nanoelectronic devices. Here, we report the achievement of robust type I multiferroic order in single-layer chromium trihalides by decorating transition metal atoms. The out-of-plane ferroelectric polarization exhibits strong atomic selectivity, where 12 of 84 single-layer transition metal-based multiferroic materials possess out-of-plane ferroelectric or antiferroelectric polarization. Group theory reveals that this phenomenon is strongly dependent on p–d coupling and crystal field splitting. Cu decoration enhances the intrinsic ferromagnetism of trihalides and increases the ferromagnetic transition temperature. Moreover, both ferroelectric and antiferroelectric phases are obtained, providing opportunities for electrical control of magnetism and energy storage and conversion applications. Furthermore, the transport properties of Cu(CrBr₃)₂ devices are calculated based on the non-equilibrium Green’s function, and the results demonstrate outstanding spin-filtering properties and a low-bias negative differential resistance (NDR) effect for low power consumption.

An ab-initio computational methodology for interrogating the phonon contribution to polaron formation in real materials is developed that can be directly compared to experiment. Using LiF as an example, we show that the recent ab-initio theory of Sio et al.¹ makes predictions of the momentum- and branch dependent phonon amplitudes in polaron quasiparticles that are testable using ultrafast electron diffuse scattering (UEDS) and related techniques. The large electron polaron in LiF has UEDS signatures that are qualitatively similar to those expected from a simple isotropic strain field model, but the small hole polaron exhibits a profoundly anisotropic UEDS pattern that is in poor agreement with an isotropic strain field. We also show that these polaron diffuse scattering signatures are directly emblematic of the underlying polaron wavefunction. The combination of new time and momentum resolved experimental probes of nonequilibrium phonons with novel computational methods promises to complement the qualitative results obtained via model Hamiltonians with a first principles, material-specific quantitative understanding of polarons and their properties.

Tungsten-bronze-type material Ba_6-3xRE_8+2xTi₁₈O₅₄, (RE = rare earth elements) is an important microwave dielectric that has shown great promises for future miniaturization of microwave devices because of its high dielectric constant, low loss, and tunabilities, and there is still much room for improvement. With their proven predictive power, first-principles calculations may greatly help accelerate materials optimization by reducing or eliminating the expensive and time-consuming experimental trial-and-error process. However, microwave dielectrics such as the tungsten-bronze-type materials are rather complex systems with unit cells containing hundreds or thousands of atoms, making ab initio calculations prohibitively expensive. In this work, we propose an elemental-unit decomposition (EUD) technique that can drastically reduce the computational effort of predicting the properties of complex microwave dielectrics and demonstrate its accuracy and efficiency. Our approach facilitates first-principles prediction and design of complex microwave dielectric materials that would otherwise be extremely difficult.