npj Computational Materials最新文献

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.

Computational investigations of biological and soft-matter systems governed by strongly anisotropic interactions typically require resource-demanding methods such as atomistic simulations. However, these techniques frequently prove to be prohibitively expensive for accessing the long-time and large-length scales inherent to such systems. Conversely, coarse-grained models offer a computationally efficient alternative. Nonetheless, models of this type have seldom been developed to accurately represent anisotropic or directional interactions. In this work, we introduce a straightforward bottom-up, data-driven approach for constructing single-site coarse-grained potentials suitable for particles with arbitrary shapes and highly directional interactions. Our method for constructing these coarse-grained potentials relies on particle-centered descriptors of local structure that effectively encode dependencies on rotational degrees of freedom in the interactions. By using these descriptors as regressors in a linear model and employing a simple feature selection scheme, we construct single-site coarse-grained potentials for particles with anisotropic interactions, including surface-patterned particles and colloidal superballs in the presence of non-adsorbing polymers. We validate the efficacy of our models by accurately capturing the intricacies of the potential-energy surfaces from the underlying fine-grained models. Additionally, we demonstrate that this simple approach can accurately represent the contact function (shape) of non-spherical particles, which may be leveraged to construct continuous potentials suitable for large-scale simulations.

All-solid-state Na-ion batteries have emerged as alternatives to all-solid-state Li-ion batteries owing to the global abundance of Na element. However, finding a commercially viable Na-ion solid-state electrolyte (SSE) remains challenging due to the relatively poor understanding of the structures effective for conduction compared to those for Li-ion SSE. In this study, we develop a screening framework based on an unsupervised machine learning technique to characterize Na-ion SSEs according to their lattice structures. Specifically, we evaluate feature vectors encoding 180 structural properties for 12,670 materials containing Na ions. Subsequently, the resulting feature vectors are clustered using hierarchical density-based spatial clustering of applications with noise (HDBSCAN), leading to the discovery of 12 groups including those with experimentally proven Na-ion superionic conductors such as NASICONs and sodium chalcogenides. Post hoc analysis of these clusters reveals that the groups with high conductivity share similar characteristics, including the existence of ion channels for Na ions and the weak interactions between Na ions and the proximate atoms. Ab initio molecular dynamics simulations confirm that the promising groups exhibit exceptional ion diffusivity compared to other groups. By employing decision tree classifiers trained to screen promising groups, we demonstrate the rapid assessment of the potential of a given material. Finally, we offer perspectives and insights for the development of novel Na-ion SSEs for all-solid-state Na-ion batteries.

Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.

Four-dimensional scanning transmission electron microscopy, coupled with a wide array of data analytics, has unveiled new insights into complex materials. Here, we introduce a straightforward unsupervised machine learning approach that entails dimensionality reduction and clustering with minimal hyperparameter tuning to semi-automatically identify unique coexisting structures in metallic alloys. Applying cepstral transformation to the original diffraction dataset improves this process by effectively isolating phase information from potential signal ambiguity caused by sample tilt and thickness variations, commonly observed in electron diffraction patterns. In a case study of a NiTiHfAl shape memory alloy, conventional scanning transmission electron microscopy imaging struggles to accurately identify a low-contrast precipitate at lower magnifications, posing challenges for microscale analyses. We find that our method efficiently separates multiple coherent structures while using objective means of determining hyperparameters. Furthermore, we demonstrate how the clustering result facilitates more robust strain mapping to provide immediate and quantitative structural insights.

Accurate computation of the gas adsorption properties of MOFs is usually bottlenecked by the DFT calculations required to generate partial atomic charges. Therefore, large virtual screenings of MOFs often use the QEq method which is rapid, but of limited accuracy. Recently, machine learning (ML) models have been trained to generate charges in much better agreement with DFT-derived charges compared to the QEq models. Previous ML charge models for MOFs have all used training sets with less than 3000 MOFs obtained from the CoRE MOF database, which has recently been shown to have high structural error rates. In this work, we developed a graph attention network model for predicting DFT-derived charges in MOFs where the model was developed with the ARC-MOF database that contains 279,632 MOFs and over 40 million charges. This model, which we call MEPO-ML, predicts charges with a mean absolute error of 0.025e on our test set of over 27 K MOFs. Other ML models reported in the literature were also trained using the same dataset and descriptors, and MEPO-ML was shown to give the lowest errors. The gas adsorption properties evaluated using MEPO-ML charges are found to be in significantly better agreement with the reference DFT-derived charges compared to the empirical charges, for both polar and non-polar gases. Using only a single CPU core on our benchmark computer, MEPO-ML charges can be generated in less than two seconds on average (including all computations required to apply the model) for MOFs in the test set of 27 K MOFs.

Plasmonic-driven photocatalysis is one of the most vibrant and promising field in nanoscience. Out of the various mechanisms known to activate chemical reactions in molecules interacting with optically excited nanostructures, the one involving production and transfer of Hot Carriers (HCs) is among the most relevant. Over the past decade, along with stunning advances on HCs control and manipulation, a variety of theoretical and computational strategies have been developed to model this phenomenon and explore its underlying physics. These techniques have provided comprehensive understandings of HCs life stages and dynamics, and allowed valuable insights on their role in photocatalysis. However, to date it is hard to extricate within the plethora of methods developed and the growing number of applications they found. The purpose of this review is to survey the approaches employed so far to model HCs photophysics, rationalizing and classifying the different studies in terms of modelization, theoretical approaches, and approximations.