npj Computational Materials最新文献

Phase-field modeling offers a powerful tool for investigating the electrical control of the domain structure in ferroelectrics. However, its broad application is constrained by demanding computational requirements, limiting its utility in inverse design scenarios. Here, we introduce a machine-learning surrogate to accelerate 3D phase-field modeling of tip-induced electrical switching. By dynamically handling the boundary conditions, the surrogate achieves accurate reproduction of switching trajectories under various tip locations and applied voltages. With stable predictions throughout entire morphological evolution pathways and a relative error inferior to 10% compared to direct solvers, the model efficiently emulates intricate switching sequences. By successfully replicating the boundary conditions, the presented framework strides towards a holistic surrogate for the ferroelectric phase field. With up to 2500-fold speed-ups over classical methods, our approach opens the path for the tractable design of the domain structure and the resolution of realistic inverse problems.

High-entropy materials have been proposed for applications in nuclear systems recently due to their outstanding properties in extreme environments. Chemical complexity in these materials plays an important role in irradiation tolerance since it significantly affects energy dissipation and defect behaviors under particle bombardment. Indeed, better resistance to irradiation-induced amorphization was observed in the high-entropy MAX (HE-MAX) phase (Ti, M)₂SnC (M = V, Nb, Zr, Hf). However, in this work, we report an opposite trend in another series of HE-MAX phases (Ti, M)₂AlC (M = Nb, Ta, V, Zr). It is demonstrated that the amorphization resistance is sequentially reduced as the number of components increases from single-component Ti₂AlC to (TiNbTa)₂AlC and (TiNbTaVZr)₂AlC. These phenomena are verified through AIMD simulations and interpreted by analyzing the underlying properties combining lattice distortion and bonding characteristics through the first-principle calculation. By developing a machine-learning (ML) model, we can directly predict lattice distortion to screen HE-MAX phases with excellent resistance to irradiation-induced amorphization. We highlight that the elemental species plays a more crucial role in the irradiation tolerance of these MAX phases than the number of constituent elements. Knowledge gained from this study will enable an improved understanding of the irradiation tolerance in HE-MAX phases and other multi-elemental ceramics.

In modern materials discovery, materials are now efficiently screened using machine learning (ML) techniques with target-specific properties for meeting various engineering applications. However, a major challenge that persists with deep generative ML approach is the issue related to lattice reconstruction at the decoding phase, leading to the generation of materials with low symmetry, unfeasible atomic coordination, and triclinic behavioral properties in the crystal lattice. To address this concern, the present research makes a contribution by proposing a Lattice-Constrained Materials Generative Model (LCMGM) for designing new and polymorphic perovskite materials with crystal conformities that are consistent with predefined geometrical and thermodynamic stability constraints at the encoding phase. A comparison with baseline models such as Physics Guided Crystal Generative Model (PGCGM) and Fourier-Transformed Crystal Property (FTCP), confirms the potential of the LCMGM for improved training stability, better chemical learning effect and higher geometrical conformity. The new materials emerging from this research are Density Functional Theory (DFT) validated and openly made available in the Mendeley data repository: https://doi.org/10.17632/m262xxpgn2.1.

The development of non-noble metal electrocatalysts for the Oxygen Evolution Reaction (OER) is advancing towards the use of multi-element materials. To reveal the complex correlations of multi-element OER electrocatalysts, we developed an iterative workflow combining high-throughput experiments and AI-generated content (AIGC) processes. An increased number of 909 (compared to 145 in previous literature) universal descriptors for inorganic materials science were constructed and used as Artificial Neural Network (ANN) input. A large number of statistical ensembles with each ANN individual ensemble having a reduced number of descriptors were integrated with a new Hierarchical Neural Network (HNN) algorithm. This algorithm addresses the longstanding challenge of balancing overwhelming descriptor numbers with insufficient datasets in traditional ANN approaches to materials science problems. As a result, the combination of AIGC and experimental validation significantly enhanced prediction accuracy, increase the R² values from 0.7 to 0.98 for Tafel slopes.

A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis. It addresses signal variability caused by stretching, proving beneficial for analyzing data such as powder diffraction at varying temperatures. This approach provides a more meaningful decomposition, particularly when the component signals resemble those from chemical components in the sample. The stretchedNMF model introduces a stretching factor to accommodate signal expansion, solved using discretization and Block Coordinate Descent algorithms. Initial experimental results indicate that the stretchedNMF model outperforms conventional NMF for datasets exhibiting such expansion. An enhanced version, sparse-stretchedNMF, optimized for powder diffraction data from crystalline materials, leverages signal sparsity for accurate extraction, especially with small stretches. Experimental results showcase its effectiveness in analyzing diffraction data, including success in real-time chemical reaction experiments.

By dispensing with all the atoms and only focusing on dislocation lines, the computational method of Discrete Dislocation Dynamics (DDD) gains greatly over Molecular Dynamics (MD) in simulation efficiency of metal plasticity. But whereas in MD dislocations follow natural dynamics of atomic motion, DDD must rely on a dislocation mobility function to prescribe how a dislocation line should respond to the driving force exerted on it. However, reflecting our still incomplete understanding of ways in which dislocations move, mobility functions presently employed in DDD simulations entail simplifications and approximations of limited or, worse still, unknown accuracy and applicability. Here we introduce a data-driven approach in which the dislocation mobility function is modeled as a graph neural network (GNN) trained on large-scale MD simulations of crystal plasticity. We apply our proposed approach to predicting plastic strength of body-centered-cubic (BCC) metal tungsten and show that, once implemented in a DDD model, our GNN dislocation mobility function accurately reproduces the challenging tension/compression asymmetry of plastic flow observed both in ground-truth MD simulations and in experiment. Furthermore, subsequently validated by MD simulations, the same function accurately predicts plastic response of tungsten under conditions not previously seen in training. By demonstrating its ability to learn relevant physics of dislocation motion, our DDD+ML approach opens a promising avenue to bringing fidelity of DDD models closer in line with direct MD simulations at a much reduced computational cost.

Cavity electromagnonic system, which simultaneously consists of cavities for photons, magnons (quanta of spin waves), and acoustic phonons, provides an exciting platform to achieve coherent energy transduction among different physical systems down to single quantum level. Here we report a dynamical phase-field model that allows simulating the coupled dynamics of the electromagnetic waves, magnetization, and strain in 3D multiphase systems. As examples of application, we computationally demonstrate the excitation of hybrid magnon-photon modes (magnon polaritons), Floquet-induced magnonic Aulter-Townes splitting, dynamical energy exchange (Rabi oscillation) and relative phase control (Ramsey interference) between the two magnon polariton modes. The simulation results are consistent with analytical calculations based on Floquet Hamiltonian theory. Simulations are also performed to design a cavity electro-magno-mechanical system that enables the triple phonon-magnon-photon resonance, where the resonant excitation of a chiral, fundamental (n = 1) transverse acoustic phonon mode by magnon polaritons is demonstrated. With the capability to predict coupling strength, dissipation rates, and temporal evolution of photon/magnon/phonon mode profiles using fundamental materials parameters as the inputs, the present dynamical phase-field model represents a valuable computational tool to guide the fabrication of the cavity electromagnonic system and the design of operating conditions for applications in quantum sensing, transduction, and communication.

The interlayer twist angle endows a new degree of freedom to manipulate the spatially separated interlayer excitons in van der Waals (vdWs) heterostructures. Herein, we find that the band-edge Γ-Γ interlayer excitation directly forms interlayer exciton in InSe/Sb heterostructure, different from that of transition metal dichalcogenides (TMDs) heterostructures in two-step processes by intralayer excitation and transfer. By tuning the interlayer coupling and breathing vibrational modes associated with the Γ-Γ photoexcitation, the interlayer twist can significantly adjust the excitation peak position and lifetime of recombination. The interlayer excitation peak in InSe/Sb heterostructure can shift ~400 meV, and the interlayer exciton lifetime varies in hundreds of nanoseconds as a periodic function of the twist angle (0°–60°). This work enriches the understanding of interlayer exciton formation and facilitates the artificial excitonic engineering of vdWs heterostructures.

Chalcogenide phase-change materials (PCMs) are showing versatile possibilities in cutting-edge applications, including non-volatile memory, neuromorphic computing, and nano-photonics. However, for embedded phase-change memory applications, conventional PCMs suffer from insufficient thermal stability because of their relatively low crystallization temperatures (T_x). Although doping with additional alloying elements could improve the amorphous stability, it also increases the tendency towards compositional partitioning and phase separation. Recently, a two-dimensional (2D) layered compound CrGeTe₃ (CrGT) was developed as a PCM, showing a high T_x ~ 276 °C with an inverse change in resistive-switching character upon phase transition. Here, we report a high-throughput materials screening for 2D layered phase-change chalcogenides. We aim to clarify whether the high T_x and the inverse electrical resistance contrast are intrinsic features of 2D PCMs. In total, twenty-five 2D chalcogenides with CrGT trilayer structures have been identified from a large database. We then focused on selected layered tellurides by performing thorough ab initio simulations and experimental investigations and confirming that their amorphous phase indeed has a much higher T_x than conventional PCMs. We attribute this enhanced amorphous stability to the structurally complex nuclei required to render crystallization possible. Overall, we regard InGeTe₃ as a balanced 2D PCM with both high thermal stability and large electrical contrast for embedded memory applications.

Since the first report of ferroelectricity in nanoscale HfO₂-based thin films in 2011, this silicon-compatible binary oxide has quickly garnered intense interest in academia and industry, and continues to do so. Despite its deceivingly simple chemical composition, the ferroelectric physics supported by HfO₂ is remarkably complex, arguably rivaling that of perovskite ferroelectrics. Computational investigations, especially those utilizing first-principles density functional theory (DFT), have significantly advanced our understanding of the nature of ferroelectricity in these thin films. In this review, we provide an in-depth discussion of the computational efforts to understand ferroelectric hafnia, comparing various metastable polar phases and examining the critical factors necessary for their stabilization. The intricate nature of HfO₂ is intimately related to the complex interplay among diverse structural polymorphs, dopants and their charge-compensating oxygen vacancies, and unconventional switching mechanisms of domains and domain walls, which can sometimes yield conflicting theoretical predictions and theoretical-experimental discrepancies. We also discuss opportunities enabled by machine-learning-assisted molecular dynamics and phase-field simulations to go beyond DFT modeling, probing the dynamical properties of ferroelectric HfO₂ and tackling pressing issues such as high coercive fields.