npj Computational Materials最新文献

In the realm of lithium superionic conductors, pursuing higher ionic conductivity is imperative, with the variance in lithium-ion concentration playing a determining role. Due to the permanent and temporary site-blocking effects, especially at non-dilute concentrations, not all Li-ions contribute to ionic conductivity. Here, we propose a strategy to directly calculate effective mobile ion concentration in which multiple-ion correlated migration is considered in the percolation analysis with the input of Li-ion distributions and hopping behavior based on kinetic Monte Carlo simulation, termed P-KMC. We provide examples of two representative lithium superionic conductors, cubic garnet-type Li_xA₃B₂O₁₂ (0 ≤ x ≤ 9; A and B represent different cations) and perovskite-type Li_xLa_2/3−x/3TiO₃ (0 ≤ x ≤ 0.5), to demonstrate the direct dependence of the ionic conductivity on the effective mobile ion concentration. This methodology provides a robust tool to identify the optimal compositions for the highest ionic conductivity in superionic conductors.

Doping is critical for inducing ferroelectricity in hafnia films, yet the underlying mechanisms remain debated. Here, through first-principles studies, we elucidate the pivotal role played by the complex phase transition mechanisms under carrier doping in understanding the origin of hafnia ferroelectricity. Specifically, electron doping orchestrates a metastable polar phase to stable antipolar phase transformation, driven by strong screening effects and weakened nonpolar covalent bonds, making n-type dopants rare. Conversely, weak screening effect and enhanced polar covalent bonding strengthen robust ferroelectricity, enabling significant ground-state phase transitions from the monoclinic to the polar orthorhombic phase and finally to the cubic phase under hole doping, a phenomenon prevalent in hafnia-based films doped with p-type dopants. Furthermore, this hole-enhanced polar distortion also results in an inverse size effect in hafnia ferroelectric films, unlike perovskite ferroelectrics. Our findings offer new insights into the preparation of robust hafnia-based ferroelectric films through doping or interface engineering.

Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods–Ensembles, deep evidential regression (DER), and Gaussian Mixture Models (GMM)—were applied to the H-transfer reaction between syn-Criegee and vinyl hydroxyperoxide. The results indicate that ensemble models provide the best results for detecting outliers, followed by GMM. For example, from a pool of 1000 structures with the largest uncertainty, the detection quality for outliers is ~90% and ~50%, respectively, if 25 or 1000 structures with large errors are sought. On the contrary, the limitations of the statistical assumptions of DER greatly impact its prediction capabilities. Finally, a structure-based indicator was found to be correlated with large average error, which may help to rapidly classify new structures into those that provide an advantage for refining the neural network.

We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.

This paper describes a novel physics-informed Bayesian optimization approach that leverages prior physics knowledge, specifically Vegard’s law and the linear relationship between gas flow rate and composition in compound semiconductors. The methodology was applied to metal-organic chemical vapor deposition for III–V semiconductor growth. It resulted in the successful synthesis of III–V semiconductors with tailored band gap wavelengths and lattice constants in the region of growth conditions not included in the training data. Furthermore, it predicted hidden trends that Ga composition would be smaller than In composition in As-rich growth regions. This trend is not described by prior physics, demonstrating that statistical machine learning is effective not only for optimization but also for gaining a physical understanding of crystal growth mechanisms. The study demonstrates the potential to develop extrapolable machine learning models by integrating robust physics knowledge, which significantly enhances the efficiency of high-throughput and autonomous material synthesis.

Exceptional room-temperature plastic deformability has been recently uncovered in a series of two-dimensional (2D) van der Waals (vdW) crystals, adding a new facet to these materials alongside the rich physical properties. Although several mechanisms have been proposed to interpret the deformation of specific materials, a deep and systematic understanding is still missing to rationalize and compare the deformability for a variety of vdW materials. In this work, focusing on typical hexagonal vdW crystals such as graphite, h-BN, transition metal dichalcogenides (TMDCs), and IIIA-VIA compounds, the deformation parameters (slip barrier energy, cleavage energy, elastic modulus) and bond features are calculated, and their correlations are systematically studied. Noticeably, there is a strong positive relation between cross-layer slip/cleavage energy, in-plane modulus, and the intralayer bond strength. The IIIA-VIA compounds (GaS, GaSe, InSe) are predicted to show a larger deformability factor, probably due to their weaker and softer chemical bonds. Moreover, it is anticipated that the deformability can be further modulated by constructing superlattice structures. These findings will facilitate the understanding and development of a variety of deformable 2D inorganic semiconductors as both few-layers and bulks.

The emerged wurtzite (wz) Al_1−xB_xN alloy has drawn increasing attention due to its superior ferroelectricity and excellent compatibility with microelectronics. We find that the stability and ferroelectric switching pathways of wz-Al_1−xB_xN alloys are affected by the orbital contribution, covalent bond strength, and elastic constant C₁₄. As the concentration of B increases, the internal parameter u decreases while the elastic constant C₁₄ increases, leading to an increase in spontaneous polarization and a decrease in the polarization switching barrier. The spontaneous polarization, polarization switching barrier, and band gap of wz-Al_1−xB_xN alloy can be further improved through the application of strain in a specific direction, resulting in a giant ferroelectricity. Additionally, the phase transformation of the wz-Al_1−xB_xN alloy induced by the increasing B composition can be regarded as a sequential process involving shrinkage, rotation, and deformation of tetrahedron. These findings give a deep understanding of the ferroelectric wz-Al_1−xB_xN alloy, and provide a guideline for designing a high-performance ferroelectric wz-Al_1−xB_xN alloy.

Understanding heat transport in organic semiconductors is of fundamental and practical relevance. Therefore, we study the lattice thermal conductivities of a series of (oligo)acenes, where an increasing number of rings per molecule leads to a systematic increase of the crystals’ complexity. Temperature-dependent thermal conductivity experiments in these systems disagree with predictions based on the traditional Peierls–Boltzmann framework, which describes heat transport in terms of particle-like phonon propagation. We demonstrate that accounting for additional phonon-tunneling conduction mechanisms through the Wigner Transport Equation resolves this disagreement and quantitatively rationalizes experiments. The pronounced increase of tunneling transport with temperature explains several unusual experimental observations, such as a weak temperature dependence in naphthalene’s thermal conductivity and an essentially temperature-invariant conductivity in pentacene. While the anisotropic thermal conductivities within the acene planes are essentially material-independent, the tunneling contributions (and hence the total conductivities) significantly increase with molecular length in the molecular backbone direction. This, for pentacene results in a surprising minimum of the thermal conductivity at 300 K.

The limited understanding of the microstructure and dynamic evolution associated with the non-stoichiometric characteristics of wustite has constrained the comprehension of iron oxide properties, diffusion, and phase transformation behaviors. This study employs deep learning methods to train interatomic potential parameters for the Fe–O system, achieving precise atomic-scale simulations of the wustite phase structure and internal lattice defects. This approach addresses the shortcomings of large-scale molecular dynamics simulations in accurately describing the solid-phase structure of the Fe–O system. Utilizing these potential parameters, this research is the first to reveal the complex mechanisms underlying the non-stoichiometric nature of wustite (Fe_1−xO). The study found that cation vacancy defects in wustite tend to aggregate, forming stable cluster structures. It also elucidated the formation mechanisms of interstitial iron atoms and typical defect clusters in wustite, establishing the formation preference for Koch–Cohen defect clusters. These potential parameters and research methods can be further applied in future studies on iron oxide reduction, phase transformation mechanisms, and related material development, thereby advancing fundamental research in metallurgy and related industries.

The excellent ability of dye-sensitized solar cells (DSSCs) to capture ambient light and convert it into electric current makes them attractive power sources for indoor applications, including powering Internet of Things (IoT) devices. In this context, substantial research efforts have been devoted to the discovery of novel organic dyes able to harvest energy from a wide range of indoor light sources at different intensities. However, such activities are often based on trial-and-error procedures which are frequently expensive and time-consuming. Here, Machine Learning (ML) techniques and Density Functional Theory (DFT) methods have been combined in a two-stage approach, with the aim to accelerate the design of new, synthetically accessible organic dyes for indoor DSSC applications. By predicting the power conversion efficiency (PCE) under different indoor light sources and intensities, potentially high-performance organic dyes have been identified.