npj Computational Materials最新文献

Large deformations of soft materials are customarily associated with strong constitutive and geometrical nonlinearities that originate new modes of fracture. Some isotropic materials can develop strong fracture anisotropy, which manifests as modifications of the crack path. Sideways cracking occurs when the crack deviates to propagate in the loading direction, rather than perpendicular to it. This fracture mode results from higher resistance to propagation perpendicular to the principal stretch direction. It has been argued that such fracture anisotropy is related to deformation-induced anisotropy resulting from the microstructural stretching of polymer chains and, in strain-crystallizing elastomers, strain-induced crystallization mechanisms. However, the precise variation of the fracture behavior with the degree of crosslinking remains to be understood. Leveraging experiments and computational simulations, here we show that the tendency of a crack to propagate sideways in the two component Elastosil P7670 increases with the degree of crosslinking. We explore the mixing ratio for the synthesis of the elastomer that establishes the transition from forward to sideways fracturing. To assist the investigations, we construct a novel phase-field model for fracture where the critical energy release rate is directly related to the crosslinking degree. Our results demonstrate that fracture anisotropy can be modulated during the synthesis of the polymer. Then, we propose a roadmap with composite soft structures with low and highly crosslinked phases that allow for control over fracture, arresting and/or directing the fracture. The smart combination of the phases enables soft structures with enhanced fracture tolerance and reduced stiffness. By extending our computational framework as a virtual testbed, we capture the fracture performance of the composite samples and enable predictions based on more intricate composite unit cells. Overall, our work offers promising avenues for enhancing the fracture toughness of soft polymers.

The d-transition kagome metals provide a novel platform for exploring correlated superconducting state intertwined with charge ordering. However, the force of charge-density-wave (CDW) and superconductivity (SC) formation, and the mechanism underlying electron pairing remain elusive. Here, utilizing our newly developed methodology based on electride states as fingerprints, we propose a novel class of hydrogen-kagome superconductors AH₃Li₅ (A = C, Si, P) with ideal kagome band characteristics and elucidate the electron-phonon coupling (EPC) mechanism responsible for electron pairing. The representative compressed PH₃Li₅ and CH₃Li₅ demonstrates impressive superconducting transition temperatures (T_cs) of 120.09 K and 57.18 K, respectively. Importantly, the CDW competes with SC thus resulting in a pressure-driven dome-shaped SC in CH₃Li₅, where the CDW order was induced by both EPC and Fermi surface nesting. Our study presents a scientific method for identifying high-T_c hydrogen-kagome metals and provides new avenues to fundamentally understand the underlying mechanism of CDW and SC, thereby guiding future experimental investigations.

The physical/chemical properties undergo significant transformations in the different states arising from phase transition. However, due to the lack of a dynamic perspective, transitional mesophases are largely underexamined, constrained by the high resource burden of first principles. Here, using molecular dynamics (MD) simulations empowered by the machine-learning potential, we proffer an innovative paradigm for phase transition: regulating the thermal transport properties via the transitional mesophase triggered by a uniaxial force field. We investigate the mechanical, electrical, and thermal transport properties of the two-dimensional carbon allotrope of Janus-graphene with strain-engineered phase transition. Notably, we found that the transitional mesophase significantly suppresses the thermal conductivity and induces strong anisotropy near the phase transition point. Through machine-learning-driven MD simulations, we achieved high-precision atomic-level simulations of Janus-graphene. The results show that thermal vibration-induced intermediate amorphous or interfacial phases induce strong and anisotropic interfacial thermal resistance. The investigation not only endows us with a novel perspective on mesophases during phase transitions but also enhances our holistic comprehension of the evolution of material properties.

Critical to the growth of digital manufacturing is the development of rapid yet accurate quality control technologies to assess the microstructure of each metal part produced. Typical surface analysis methods are limited in measurement throughput and impose constraints on maximum area size and surface quality, which enforce the tedious practice of extracting and preparing flat, small-scale samples for microstructure analysis. Here, we propose a new approach based on directional reflectance microscopy (DRM) which can yield part-scale microstructure information nondestructively and on curved, complex surfaces. We demonstrate our approach on the airfoil of a turbine blade and carry out a rigorous error analysis using other samples with variable surface geometry. Our results highlight the potential for part-specific quality control in the context of digital manufacturing.

Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic geometry, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned interatomic potentials, is conceptually at odds with learning complex intrinsic properties of materials, often driven by spectral properties commonly represented in reciprocal space (e.g., band gaps or mobilities) which cannot be readily partitioned in real space. For such applications, methods that represent the electronic rather than the atomic structure could be more promising. In this work, we present a general framework focused on electronic-structure descriptors that take advantage of the natural symmetries and inherent interpretability of physical models. We apply this framework first to material similarity and then to accelerated screening, where a model trained on 217 materials correctly labels 75% of entries in the Materials Cloud 3D database, which meet common screening criteria for promising transparent-conducting materials.

The fracture toughness (K_IC) of high-entropy oxides (HEOs) is critically important for several applications, but identification and quantification of the toughening mechanisms resulting from lattice-engineering/distortion in HEOs is challenging. Here, based on the classic Griffith criteria, a physics-driven theoretical equation combined with a knowledge-enabled data-driven machine-learning algorithm is proposed to predict the K_IC and elucidate the toughening mechanisms of A₂Zr₂O₇-type HEOs. Together with experimental verification, our proposed model is applied to a dataset comprising 41208 (nRE_1/n)₂Zr₂O₇ (n = 2~7) HEOs, considering the contributions of the intrinsic brittleness and increased toughness due to the local lattice distortion (LLD), thereby addressing the challenge of accurate estimating K_IC in complex HEOs using the rule of mixtures. During crack tip propagation, the interaction mechanism of cations induces stress fields and charge variations of LLD and dissipates crack energy, thus, to yield the crack tip softening and the elastic shielding and to enhance the toughness of HEOs.

A novel approach is presented by integrating images-driven deep learning (DL) with high entropy oxides (HEOs) analysis. A fully convolutional neural network (FCN) is used to interpret experimental scanning transmission electron microscopy (STEM) images of HEO of various sizes. The FCN model is designed to predict column heights (CHs) and elemental distributions from single, experimentally acquired STEM images of complex (Mn, Fe, Ni, Cu, Zn)₃O₄ HEO nanoparticles (NPs) at atomic resolution. The model’s ability to predict elemental distributions was tested across various crystallographic zones. It was found that the model could effectively adapt to different atomic configurations and operational conditions. One of the significant outcomes was the identification of substantial elemental inhomogeneities in all experimental NPs, which highlighted the random and complex nature of element distribution within HEOs. The developed FCN DL method can be applied to assist experimental HEO and beyond NP analysis in various operating conditions.

The origin of resistivity peak and sign reversal of Hall resistivity in ZrTe₅ has long been debated. Despite various theories proposed to explain these unique transport properties, there’s a lack of comprehensive first principles studies. In this work, we employ first principles calculations and Boltzmann transport theory to explore transport properties of narrow-gap semiconductors across varying temperatures and doping levels within the relaxation time approximation. We simulate the temperature-sensitive chemical potential and relaxation time in semiconductors through proper approximations, then extensively analyze ZrTe₅’s transport behaviors with and without an applied magnetic field. Our results reproduce crucial experimental observations such as the zero-field resistivity anomaly, nonlinear Hall resistivity with sign reversal, and non-saturating magnetoresistance at high temperatures, without introducing topological phases and/or correlation interactions. Our approach provides a systematic understanding based on multi-carrier contributions and Fermi surface geometry, and could be extended to other narrow-gap semiconductors to explore novel transport properties.

The forward screening and reverse design of drug molecules, inorganic molecules, and polymers with enhanced properties are vital for accelerating the transition from laboratory research to market application. Specifically, due to the scarcity of large-scale datasets, the discovery of polymers via materials informatics is particularly challenging. Nonetheless, scientists have developed various machine learning models for polymer structure-property relationships using only small polymer datasets, thereby advancing the forward screening process of polymers. However, the success of this approach ultimately depends on the diversity of the candidate pool, and exhaustively enumerating all possible polymer structures through human imagination is impractical. Consequently, achieving on-demand reverse design of polymers is essential. In this work, we curate an immense polymer dataset containing nearly one million polymeric structure-property pairs based on expert knowledge. Leveraging this dataset, we propose a Transformer-Assisted Oriented pretrained model for on-demand polymer generation (PolyTAO). This model generates polymers with 99.27% chemical validity in top-1 generation mode (approximately 200k generated polymers), representing the highest reported success rate among polymer generative models, and this was achieved on the largest test set. Importantly, the average R² between the properties of the generated polymers and their expected values across 15 predefined properties is 0.96, which underscores PolyTAO’s powerful on-demand polymer generation capabilities. To further evaluate the pretrained model’s performance in generating polymers with additional user-defined properties for downstream tasks, we conduct fine-tuning experiments on three publicly available small polymer datasets using both semi-template and template-free generation paradigms. Through these extensive experiments, we demonstrate that our pretrained model and its fine-tuned versions are capable of achieving the on-demand reverse design of polymers with specified properties, whether in a semi-template generation or the more challenging template-free generation scenarios, showcasing its potential as a unified pretrained foundation model for polymer generation.

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.