npj Computational Materials最新文献

Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.

The substitution of heteroatoms and the functionalisation of molecules are established strategies in chemical synthesis. They target the precise tuning of the electronic properties of hydrocarbon molecules to improve their performance in various applications and increase their versatility. Modifications to the molecular structure often lead to simultaneous changes in the morphology such as different crystal structures. These changes can have a stronger and unpredictable impact on the targeted property. The complex relationships between substitution/functionalization in chemical synthesis and the resulting modifications of properties in thin films or crystals are difficult to predict and remain elusive. Here we address these effects for charge carrier transport in organic crystals by combining simulations of carrier mobilities with crystal structure prediction based on density functional theory and density functional tight binding theory. This enables the prediction of carrier mobilities based solely on the molecular structure and allows for the investigation of chemical modifications prior to synthesis and characterisation. Studying nine specific molecules with tetracene and rubrene as reference compounds along with their combined modifications of the molecular cores and additional functionalisations, we unveil systematic trends for the carrier mobilities of their polymorphs. The positive effect of phenyl groups that is responsible for the marked differences between tetracene and rubrene can be transferred to other small molecules such as NDT and NBT leading to a mobility increase by large factors of about five.

Near-infrared (NIR) phosphors based on Cr³⁺ doped garnets present great potential in the next generation of NIR light sources. Nevertheless, the huge searching space for the garnet composition makes the rapid discovery of NIR phosphors with high performance remain a great challenge for the scientific community. Herein, a generalizable machine learning (ML) strategy is designed to accelerate the exploration of innovative NIR phosphors via establishing the relationship between key parameters and emission peak wavelength (EPW). We propose a semi-supervised co-training model based on kernel ridge regression (KRR) and support vector regression (SVR), which successfully establishes an expanded dataset with unlabeled dataset (previously unidentified garnets), addressing the overfitting issue resulted from a small dataset and greatly improving the model generalization capability. The model is then interpreted to extract valuable insights into the contribution originated from different features. And a new type NIR luminescent material of Lu₃Y₂Ga₃O₁₂: Cr³⁺ (EPW~750 nm) is efficiently screened, which demonstrates a high internal (external) quantum efficiency of 97.1% (38.8%) and good thermal stability, particularly exhibiting promising application in the NIR phosphor-converted LEDs (pc-LED). These results suggest the strategy proposed in this work could provide new viewpoint and direction for developing NIR luminescence materials.

To realize room temperature ferromagnetic (FM) semiconductors is still a challenge in spintronics. Many antiferromagnetic (AFM) insulators and semiconductors with high Neel temperature T_N are obtained in experiments, such as LaFeO₃, BiFeO₃, etc. High concentrations of magnetic impurities can be doped into these AFM materials, but AFM state with very tiny net magnetic moments was obtained in experiments because the magnetic impurities were equally doped into the spin up and down sublattices of the AFM materials. Here, we propose that the effective magnetic field provided by a FM substrate could guarantee the spin-dependent doping in AFM materials, where the doped magnetic impurities prefer one sublattice of spins, and the ferrimagnetic (FIM) materials are obtained. To demonstrate this proposal, we study the Mn-doped AFM insulator LaFeO₃ with FM substrate of Fe metal by the density functional theory (DFT) calculations. It is shown that the doped magnetic Mn impurities prefer to occupy one sublattice of the AFM insulator and introduce large magnetic moments in La(Fe, Mn)O₃. For the AFM insulator LaFeO₃ with high T_N = 740 K, several FIM semiconductors with high Curie temperature T_C > 300 K and the band gap less than 2 eV are obtained by DFT calculations when 1/8 or 1/4 Fe atoms in LaFeO₃ are replaced by the other 3d, 4d transition metal elements. The large magneto-optical Kerr effect (MOKE) is obtained in these LaFeO₃-based FIM semiconductors. In addition, the FIM semiconductors with high T_C are also obtained by spin-dependent doping in some other AFM materials with high T_N, including BiFeO₃, SrTcO₃, CaTcO₃, etc. Our theoretical results propose a way to obtain high T_C FIM semiconductors by spin-dependent doping in high T_N AFM insulators and semiconductors.

Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.

In this study, we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate physicochemical information, such as lattice parameters and crystallite sizes. Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness. It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic, eliminating the need for prior knowledge of the sample’s chemical composition. We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data, acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery.

High-harmonic generation (HHG) has emerged as a central technique in attosecond science and strong-field physics, providing a tool for investigating ultrafast dynamics. However, the microscopic mechanism of HHG in solids is still under debate, and it is unclear how it is modified in the ubiquitous presence of phonons. Here we theoretically investigate the role of collectively coherent vibrations in HHG in a wide range of solids (e.g., hBN, graphite, 2H-MoS₂, and diamond). We predict that phonon-assisted high harmonic yields can be significantly enhanced, compared to the phonon-free case – up to a factor of ~20 for a transverse optical phonon in bulk hBN. We also show that the emitted harmonics strongly depend on the character of the pumped vibrational modes. Through state-of-the-art ab initio calculations, we elucidate the physical origin of the HHG yield enhancement – phonon-assisted photoinduced carrier doping, which plays a paramount role in both intraband and interband electron dynamics. Our research illuminates a clear pathway toward comprehending phonon-mediated nonlinear optical processes within materials, offering a powerful tool to deliberately engineer and govern solid-state high harmonics.

First-principles simulations were conducted to explore various electronic properties of crystalline SiO₂ (α-quartz) under ultrafast laser irradiation. Employing Density Functional Perturbation Theory and the many-body (GW) approximation, we calculated the impact of thermally excited electrons on the electronic specific heat, electron pressure, effective mass, deformation potential, electron-phonon coupling and electron relaxation time of quartz, covering a wide range of electron temperatures, up to 100,000 K. We show that the electron-phonon relaxation time of highly-excited quartz becomes twice faster compared to low-excited states. The deformation potential, which dictates atomic displacement, has a non-monotonic behavior with a well-pronounced minimum at around 16,000 K (2.7 × 10²¹ cm⁻³ of excited electrons) where the bond ionicity of the Si-O starts decreasing followed by a cohesion loss at 35,000 K due to the pressure exerted by the excited electrons on the lattice. Consequently, our calculated data, illustrating the evolution of physical parameters, can facilitate simulations of laser-matter interactions and provide predictive insights into the behavior of quartz under experimental conditions.

Despite numerous efforts from numerical approaches to complement experimental measurements, several fundamental challenges have still hindered one’s ability to truly provide an atomistic picture of the nucleation process in nanocrystals. Among them, our study resolves three obstacles: (1) Machine-learning force fields including long-range interactions able to capture the finesse of the underlying atomic interactions, (2) Data-driven characterization of the local ordering in a complex structural landscape associated with several crystal polymorphs and (3) Comparing results from a large range of temperatures using both brute-force and rare-event sampling. Altogether, our simulation strategy has allowed us to study zinc oxide crystallization from nano-droplet melt. Remarkably, our results show that different nucleation pathways compete depending on the investigated degree of supercooling.

We present the Python package CELL, which provides a modular approach to the cluster expansion (CE) method. CELL can treat a wide variety of substitutional systems, including one-, two-, and three-dimensional alloys, in a general multi-component and multi-sublattice framework. It is capable of dealing with complex materials comprising several atoms in their parent lattice. CELL uses state-of-the-art techniques for the construction of training data sets, model selection, and finite-temperature simulations. The user interface consists of well-documented Python classes and modules (http://sol.physik.hu-berlin.de/cell/). CELL also provides visualization utilities and can be interfaced with virtually any ab initio package, total-energy codes based on interatomic potentials, and more. The usage and capabilities of CELL are illustrated by a number of examples, comprising a Cu-Pt surface alloy with oxygen adsorption, featuring two coupled binary sublattices, and the thermodynamic analysis of its order-disorder transition; the demixing transition and lattice-constant bowing of the Si-Ge alloy; and an iterative CE approach for a complex clathrate compound with a parent lattice consisting of 54 atoms.