npj Computational Materials最新文献

In this work, we set out to elucidate the light-harvesting properties of various random and ordered photocatalyst supports (PSs) with different macropore sizes. To accomplish this, we propose two studies of increasing relevance, enabled by computed tomography (CT) reconstructions and ray-tracing COMSOL Multiphysics simulations: (a) a 360-degree light release study approximating a PS situated within a compound parabolic concentrator (CPC) or cylindrical LED reactor with open ends; and (b) the same system as before but with closed ends. The ordered geometry is of interest, as it can be 3D printed at scale with a tailored morphology and porosity, and it can potentially be refined using machine learning models to optimize its light-harvesting properties. As will be shown, the local volumetric light absorption (LVLA) data suggests that an ordered PS with a more open pore interior and a smaller pore exterior would begin to approach the more isophotonic light-harvesting properties of random PSs.

Recent advancements in artificial intelligence (AI)-based design strategies for metamaterials have revolutionized the creation of customizable architectures spanning nano- to macro-scale dimensions. However, their increasing complexity poses challenges in generating diverse metamaterials, hindering widespread adoption. Here, we introduce an innovative design strategy for three-dimensional graph metamaterials through simple arithmetic operations within the latent space. By leveraging carefully designed hidden representations of disentangled latent space and diffusion processes, our method unravels design space complexity, generating diverse graph metamaterials with comprehensive understanding. This versatile methodology facilitates the creation of graph metamaterials ranging from repetitive lattices to functionally graded materials. We believe that this methodology represents a foundational step in advancing our comprehension of the intricate latent design space, offering the potential to establish a unified model for various traditional generative models in the realm of graph metamaterials.

Understanding structural flexibility of metal-organic frameworks (MOFs) via molecular dynamics simulations is crucial to design better MOFs. Density functional theory (DFT) and quantum-chemistry methods provide highly accurate molecular dynamics, but the computational overheads limit their use in long time-dependent simulations. In contrast, classical force fields struggle with the description of coordination bonds. Here we develop a DFT-accurate machine-learning spectral neighbor analysis potentials for two representative MOFs. Their structural and vibrational properties are then studied and tightly compared with available experimental data. Most importantly, we demonstrate an active-learning algorithm, based on mapping the relevant internal coordinates, which drastically reduces the number of training data to be computed at the DFT level. Thus, the workflow presented here appears as an efficient strategy for the study of flexible MOFs with DFT accuracy, but at a fraction of the DFT computational cost.

The Kampmann–Wagner Numerical (KWN) model of precipitation is a powerful tool to simulate the precipitation of the second phase considering the nucleation, growth, and coarsening. Some quantities such as interfacial energy and nucleation site number density are required to accomplish the simulation. Practically, those quantities are hard to measure in the experiment directly, and the derivation of those quantities through modeling can also be costly. In this work, we hereby adopt the minimization algorithm implemented in the open-source Scipy Python package to derive that important information in terms of very limited experimental data. The convergence and robustness of different algorithms are discussed. Among those algorithms, the Nelder–Mead and Powell algorithms are successfully applied to optimize multiple parameters during KWN modeling. This work will shed light on the design of experiments/processes and facilitate integrated computational materials engineering (ICME).

Magnetic skyrmions are potential candidates for high-density storage and logic devices because of their inherent topological stability and nanoscale size. Two-dimensional (2D) Janus transition metal chalcogenides (TMDs) are widely used to induce skyrmions due to the breaking of inversion symmetry. However, the experimental synthesis of Janus TMDs is rare, which indicates that the Janus configuration might not be the most stable MXY structure. Here, through machine-learning-assisted high-throughput first-principles calculations, we demonstrate that not all MXY compounds can be stabilized in Janus layered structure and a large proportion prefer to form other configurations with lower energy than the Janus configuration. Interestingly, these new configurations exhibit a strong Dzyaloshinskii–Moriya interaction (DMI), which can generate and stabilize skyrmions even under a strong magnetic field. This work provides not only an efficient method for obtaining ferromagnetic materials with strong DMI but also a theoretical guidance for the synthesis of TMDs via experiments.

Ultrafast manipulation of the Néel vector in metallic antiferromagnets most commonly occurs by generation of spin-orbit (SOT) or spin-transfer (STT) torques. Here, we predict another possibility for antiferromagnetic domain switching by using novel laser optical torques (LOTs). We present results of atomistic spin dynamics simulations from the application of LOTs for all-optical switching of the Néel vector in the antiferromagnet Mn₂Au. The driving mechanism takes advantage of the sizeable exchange enhancement, characteristic of antiferromagnetic dynamics, allowing for picosecond 90 and 180-degree precessional toggle switching of the Néel vector with laser fluences on the order of mJ/cm². A special symmetry of these novel torques greatly minimises the over-shooting effect common to precessional spin switching by SOT and STT methods. We demonstrate the opportunity for LOTs to produce deterministic, non-toggle switching of single antiferromagnetic domains. Lastly, we show that even with sizeable ultrafast heating by laser in metallic systems, there exist a large interval of laser parameters where the LOT-assisted toggle and preferential switchings in magnetic grains have probabilities close to one. The proposed protocol could be used on its own for all-optical control of antiferromagnets for computing or memory storage, or in combination with other switching methods to lower energy barriers and/or to prevent over-shooting of the Néel vector.

2D semiconductors offer a promising pathway to replace silicon in next-generation electronics. Among their many advantages, 2D materials possess atomically-sharp surfaces and enable scaling the channel thickness down to the monolayer limit. However, these materials exhibit comparatively lower charge carrier mobility and higher contact resistance than 3D semiconductors, making it challenging to realize high-performance devices at scale. In this work, we search for high-mobility 2D materials by combining a high-throughput screening strategy with state-of-the-art calculations based on the ab initio Boltzmann transport equation. Our analysis singles out a known transition metal dichalcogenide, monolayer WS₂, as the most promising 2D semiconductor, with the potential to reach ultra-high room-temperature hole mobilities in excess of 1300 cm²/Vs should Ohmic contacts and low defect densities be achieved. Our work also highlights the importance of performing full-blown ab initio transport calculations to achieve predictive accuracy, including spin–orbital couplings, quasiparticle corrections, dipole and quadrupole long-range electron–phonon interactions, as well as scattering by point defects and extended defects.

A critical issue in laser powder bed fusion (LPBF) additive manufacturing is the selective vaporization of alloying elements resulting in poor mechanical properties and corrosion resistance of parts. The process also alters the part’s chemical composition compared to the feedstock. Here we present a novel multi-physics modeling framework, integrating heat and fluid flow simulations, thermodynamic calculations, and evaporation modeling to estimate and control the composition change during LPBF of nickel-based superalloys. Experimental validation confirms the accuracy of our model. Moreover, we quantify the relative vulnerabilities of different nickel-based superalloys to composition change quantitatively and we examine the effect of remelting due to the layer-by-layer deposition during the LPBF process. Spatial variations in evaporative flux and compositions for each element were determined, providing valuable insights into the LPBF process and product attributes. The results of this study can be used to optimize the LPBF process parameters such as laser power, scanning speed, and powder layer thickness to ensure the production of high-quality components with desired chemical compositions.

Fullerenes, as characteristic carbon nanomaterials, offer significant potential for diverse applications due to their structural diversity and tunable properties. Numerous isomers can exist for a specific fullerene size, yet a comprehensive understanding of their fundamental properties remains elusive. In this study, we construct an up-to-date computational database for C₂₀–C₆₀ fullerenes, consisting of 5770 structures, and calculate 12 fundamental properties using DFT, including stability (binding energy), electronic properties (HOMO-LUMO gap), and solubility (partition coefficient logP). Our findings reveal that the HOMO-LUMO gap weakly correlates with both binding energy and logP, indicating that electronic properties can be tailored for specific uses without affecting stability or solubility. In addition, we introduce a set of topological features and geometric measures to investigate structure-property relationships. We apply atom, bond, and hexagon features to effectively predict the stability of C₂₀–C₆₀ fullerenes, surpassing the conventional qualitative isolated pentagon rule, and demonstrating their robust transferability to larger-size fullerenes beyond C₆₀. Our work offers guidance for optimizing fullerenes as electron acceptors in organic solar cells and lays a foundational understanding of their functionalization and applications in energy conversion and nanomaterial sciences.