npj Computational Materials最新文献

We describe an integrated modelling approach to accelerate the search for novel, single-phase, multicomponent materials with high magnetocrystalline anisotropy (MCA). For a given system we predict the nature of atomic ordering, its dependence on the magnetic state, and then proceed to describe the consequent MCA, magnetisation, and magnetic critical temperature (Curie temperature). Crucially, within our modelling framework, the same ab initio description of a material’s electronic structure determines all aspects. We demonstrate this holistic method by studying the effects of alloying additions in FeNi, examining systems with the general stoichiometries Fe₄Ni₃X and Fe₃Ni₄X, for additives including X = Pt, Pd, Al, and Co. The atomic ordering behaviour predicted on adding these elements, fundamental for determining a material’s MCA, is rich and varied. Equiatomic FeNi has been reported to require ferromagnetic order to establish the tetragonal L1₀ order suited for significant MCA. Our results show that when alloying additions are included in this material, annealing in an applied magnetic field and/or below a material’s Curie temperature may also promote tetragonal order, along with an appreciable effect on the predicted hard magnetic properties.

Melting properties are critical for designing novel materials, especially for discovering high-performance, high-melting refractory materials. Experimental measurements of these properties are extremely challenging due to their high melting temperatures. Complementary theoretical predictions are, therefore, indispensable. One of the most accurate approaches for this purpose is the ab initio free-energy approach based on density functional theory (DFT). However, it generally involves expensive thermodynamic integration using ab initio molecular dynamic simulations. The high computational cost makes high-throughput calculations infeasible. Here, we propose a highly efficient DFT-based method aided by a specially designed machine learning potential. As the machine learning potential can closely reproduce the ab initio phase-space distribution, even for multi-component alloys, the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation calculations. The method achieves overall savings of computational resources by 80% compared to current alternatives. We apply the method to the high-entropy alloy TaVCrW and calculate its melting properties, including the melting temperature, entropy and enthalpy of fusion, and volume change at the melting point. Additionally, the heat capacities of solid and liquid TaVCrW are calculated. The results agree reasonably with the CALPHAD extrapolated values.

We study the phase behaviour of the Al_xCrFeCoNi high-entropy alloy. Our approach is based on a perturbative analysis of the internal energy of the paramagnetic solid solution as evaluated within the Korringa-Kohn-Rostoker formulation of density functional theory, using the coherent potential approximation to average over disorder. Via application of a Landau-type linear response theory, we infer preferential chemical orderings directly. In addition, we recover a pairwise form of the alloy internal energy suitable for study via atomistic simulations, which in this work are performed using the nested sampling algorithm, which is well-suited for studying complex potential energy surfaces. When the underlying lattice is fcc, at low concentrations of Al, depending on the value of x, we predict either an L1₂ or D0₂₂ ordering emerging below approximately 1000 K. On the other hand, when the underlying lattice is bcc, consistent with experimental observations, we predict B2 ordering temperatures higher than the melting temperature of the alloy, confirming that this ordered phase forms directly from the melt. For both fcc and bcc systems, chemical orderings are dominated by Al moving to one sublattice, Ni and Co the other, while Cr and Fe remain comparatively disordered. On the bcc lattice, our atomistic modelling suggests eventual decomposition into B2 NiAl and Cr-rich phases. These results shed light on the fundamental physical origins of atomic ordering tendencies in these intriguing materials.

A model for studying displacement damage in irradiated HfO₂ ferroelectric thin films was developed using deep learning and a repulsive table, combining the accuracy of density functional theory with the efficiency of molecular dynamics. This model accurately predicts the properties of various HfO₂ phases, such as PO (Pca2₁), T (P4₂/nmc), AO (Pbca), and M (P2₁/c), and describes the atom collision-separation process during irradiation. The displacement threshold energies for the Hf atoms, three-coordinated O atoms, and four-coordinated O atoms are 57.72, 41.93, and 32.89 eV, respectively. The defect formation probabilities (DFPs) for the O primary knock-on atoms (PKAs) and Hf PKAs increase with energy, reaching 1. Below 80.27 eV, the O PKAs are more likely to form point defects than the Hf PKAs. Above this energy, the Hf PKAs have a higher DFP because the O PKAs form replacement loops more easily, inhibiting the generation of point defects. This study provides a comprehensive understanding of defect formation, which is crucial for increasing the reliability of HfO₂ ferroelectric devices under irradiation.

The quasiharmonic approximation (QHA) in combination with density-functional theory is the main computational method used to calculate thermodynamic properties under arbitrary temperature and pressure conditions. QHA can predict thermodynamic phase diagrams, elastic properties and temperature- and pressure-dependent equilibrium geometries, all of which are important in various fields of knowledge. The main drawbacks of QHA are that it makes spurious predictions for the volume and other properties in the high temperature limit due to its approximate treatment of anharmonicity, and that it is unable to model dynamically stabilized structures. In this work, we propose an extension to QHA that fixes these problems. Our approach is based on four ingredients: (i) the calculation of the n-th order force constants using randomly displaced configurations and regularized regression, (ii) the calculation of temperature-dependent effective harmonic frequencies ω(V, T) within the self-consistent harmonic approximation (SCHA), (iii) Allen’s quasiparticle (QP) theory, which allows the calculation of the anharmonic entropy from the effective frequencies, and (iv) a simple Debye-like numerical model that enables the calculation of all other thermodynamic properties from the QP entropies. The proposed method is conceptually simple, with a computational complexity similar to QHA but requiring more supercell calculations. It allows incorporating anharmonic effects to any order. The predictions of the new method coincide with QHA in the low-temperature limit and eliminate the QHA blowout at high temperature, recovering the experimentally observed behavior of all thermodynamic properties tested. The performance of the new method is demonstrated by calculating the thermodynamic properties of geologically relevant minerals MgO and CaO. In addition, using cubic SrTiO₃ as an example, we show that, unlike QHA, our method can also predict thermodynamic properties of dynamically stabilized phases. We expect this new method to be an important tool in geochemistry and materials discovery.

We present an algorithm for the high-throughput computational discovery of intermetallic compounds in systems with a large number of components. It is particularly important for high entropy alloys (HEAs), where multiple principal elements can form numerous potential intermetallic compounds during the condensation process, making it challenging to predict the dominant phase. Our algorithm is based on a brute-force evaluation of candidate structures with a fixed underlying lattice (FCC or BCC) accelerated by machine-learning interatomic potentials. The algorithm takes a set of chemical elements and a crystal lattice type as inputs and produces structures on and near the convex hull of thermodynamically stable structures. The candidate structures are evaluated using the low-rank potential (LRP), trained to reproduce energies of structures equilibrated with density functional theory (DFT). Thanks to extreme computational effectiveness of the LRP, it is feasible to evaluate hundreds of thousands of structures per second, per CPU core. Thus, our algorithm screens a complete set of candidate structures for a given system without missing any configurations. We validated our method on systems with BCC (Nb-W, Nb-Mo-W, V-Nb-Mo-Ta-W) and FCC (Cu-Pt, Cu-Pd-Pt, Cu-Pd-Ag-Pt-Au) lattices and discovered 268 new alloys not reported in the AFLOW database¹, which we used as a benchmark.

Dislocations in ceramics are increasingly recognized for their promising potential in applications such as toughening intrinsically brittle ceramics and tailoring functional properties. However, the atomistic simulation of dislocation plasticity in ceramics remains challenging due to the complex interatomic interactions characteristic of ceramics, which include a mix of ionic and covalent bonds, and highly distorted and extensive dislocation core structures within complex crystal structures. These complexities exceed the capabilities of empirical interatomic potentials. Therefore, constructing neural network potentials (NNPs) emerges as the optimal solution. Yet, creating a training dataset that includes dislocation structures proves difficult due to the complexity of their core configurations in ceramics and the computational demands of density functional theory for large atomic models containing dislocation cores. In this work, we propose a training dataset from properties that are easier to compute via high-throughput calculation. Using this dataset, we have successfully developed NNPs for dislocation plasticity in ceramics, specifically for three typical functional ceramics: ZnO, GaN, and SrTiO₃. These NNPs effectively capture the nonstoichiometric and charged core structures and slip barriers of dislocations, as well as the long-range electrostatic interactions between charged dislocations. The effectiveness of this dataset was further validated by measuring the similarity and uncertainty across snapshots derived from large-scale simulations, alongside extensive validation across various properties. Utilizing the constructed NNPs, we examined dislocation plasticity in ceramics through nanopillar compression and nanoindentation, which demonstrated excellent agreement with experimental observations. This study provides an effective framework for constructing NNPs that enable the detailed atomistic modeling of dislocation plasticity, opening new avenues for exploring the plastic behavior of ceramics.

Predicting the properties of non-fullerene acceptors (NFAs), complex organic molecules used in organic solar cells (OSCs), poses a significant challenge. Some existing approaches primarily focus on atom-level information and may overlook high-level molecular features, including the subunits of NFAs. While other methods that effectively represent subunit information show improved prediction performance, they require labor-intensive data labeling. In this paper, we introduce an efficient molecular description method that automatically extracts molecular information at both the atom and subunit levels without any labor-intensive data labeling. Inspired by the Word2Vec method, our Ring2Vec method treats the “rings” in organic molecules as analogous to “words” in sentences. We achieve fast and accurate predictions of the energy levels of NFA molecules, with a minimal prediction error of merely 0.06 eV. Furthermore, our method can potentially have broad applicability across various domains of molecular description and property prediction, owing to the efficiency of the Ring2Vec model.

Dielectrics are crucial for technologies like flash memory, CPUs, photovoltaics, and capacitors, but public data on these materials are scarce, restricting research and development. Existing machine learning models have focused on predicting scalar polycrystalline dielectric constants, neglecting the directional nature of dielectric tensors essential for material design. This study leverages multi-rank equivariant structural embeddings from a universal neural network potential to enhance predictions of dielectric tensors. We develop an equivariant readout decoder to predict total, electronic, and ionic dielectric tensors while preserving O(3) equivariance, and benchmark its performance against state-of-the-art algorithms. Virtual screening of thermodynamically stable materials from Materials Project for two discovery tasks, high-dielectric and highly anisotropic materials, identifies promising candidates including Cs₂Ti(WO₄)₃ (band gap E_g = 2.93eV, dielectric constant ε = 180.90) and CsZrCuSe₃ (anisotropic ratio α_r = 121.89). The results demonstrate our model’s accuracy in predicting dielectric tensors and its potential for discovering novel dielectric materials.

First principles approaches have revolutionized our ability in using computers to predict, explore, and design materials. A major advantage commonly associated with these approaches is that they are fully parameter-free. However, numerically solving the underlying equations requires to choose a set of convergence parameters. With the advent of high-throughput calculations, it becomes exceedingly important to achieve a truly parameter-free approach. Utilizing uncertainty quantification (UQ) and linear decomposition we derive a numerically highly efficient representation of the statistical and systematic error in the multidimensional space of the convergence parameters for plane wave density functional theory (DFT) calculations. Based on this formalism we implement a fully automated approach that requires as input the target precision rather than convergence parameters. The performance and robustness of the approach are shown by applying it to a large set of elements crystallizing in a cubic fcc lattice.