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Register a new userOn the detection and classification of material defects in crystalline solids after energetic particle impact simulations
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We present a fingerprint-like method to analyze material defects after energetic particle irradiation by computing a rotation invariant descriptor vector for each atom of a given sample. For ordered solids this new method is easy to use, does not require extreme computational resources, and is largely independent of the sample material and sample temperature. As illustration we applied the method to molecular dynamics simulations of deuterated and pristine tungsten lattices at 300 K using a primary knock-on atom (PKA) of 1 keV with different velocity directions to emulate a neutron bombardment process. The number of W atoms, that are affected after the collision cascade, have been quantified with the presented approach. At first atoms at regular lattice positions as well as common defect types like interstitials and vacancies have been identified using precomputed descriptor vectors. A principal component analysis (PCA) is used to identify previously overlooked defect types and to derive the corresponding local atomic structure. A comparison of the irradiation effects for deuterated and pristine tungsten samples revealed that deuterated samples exhibit consistently more defects than pristine ones.
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We propose a method to decompose the total energy of a supercell containing defects into contributions of individual atoms, using the energy density formalism within density functional theory. The spatial energy density is unique up to a gauge transformation, and we show that unique atomic energies can be calculated by integrating over Bader and charge-neutral volumes for each atom. Numerically, we implement the energy density method in the framework of the Vienna ab initio simulation package (VASP) for both norm-conserving and ultrasoft pseudopotentials and the projector augmented wave method, and use a weighted integration algorithm to integrate the volumes. The surface energies and point defect energies can be calculated by integrating the energy density over the surface region and the defect region, respectively. We compute energies for several surfaces and defects: the (110) surface energy of GaAs, the mono-vacancy formation energies of Si, the (100) surface energy of Au, and the interstitial formation energy of O in the hexagonal close-packed Ti crystal. The surface and defect energies calculated using our method agree with size-converged calculations of the difference between the total energies of the system with and without the defect. Moreover, the convergence of the defect energies with size can be found from a single calculation.
Research on topological physics of phonons has attracted enormous interest but demands appropriate model materials. Our {it ab initio} calculations identify silicon as an ideal candidate material containing extraordinarily rich topological phonon states. In silicon, we identify various topological nodal lines protected by glide mirror or mirror symmetries and characterized by quantized Berry phase $pi$, which gives drumhead surface states observable from any surface orientations. Remarkably, a novel type of topological nexus phonon is discovered, which is featured by double Fermi-arc-like surface states and distinguished from Weyl phonons by requiring neither inversion nor time-reversal symmetry breaking. Versatile topological states can be created from the nexus phonons, such as Hopf nodal link by strain. Furthermore, we generalize the symmetry analysis to other centrosymmetric systems and find numerous candidate materials, demonstrating the ubiquitous existence of topological phonons in solids. These findings open up new opportunities for studying topological phonons in realistic materials and their influence on surface physics.
Defects influence the properties and functionality of all crystalline materials. For instance, point defects participate in electronic (e.g. carrier generation and recombination) and optical (e.g. absorption and emission) processes critical to solar energy conversion. Solid-state diffusion, mediated by the transport of charged defects, is used for electrochemical energy storage. First-principles calculations of defects based on density functional theory have been widely used to complement, and even validate, experimental observations. In this `quick-start guide, we discuss the best practice in how to calculate the formation energy of point defects in crystalline materials and analysis techniques appropriate to probe changes in structure and properties relevant across energy technologies.
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure. Further, analysis is applied to individual molecules in order to explore the contribution of bonding motifs to these properties. Feature descriptors evaluated include Morgan fingerprints, E-state vectors, a custom sum over bonds descriptor, and coulomb matrices. Algorithms discussed include kernel ridge regression, least absolute shrinkage and selection operator (LASSO) regression, Gaussian process regression, and the multi-layer perceptron (a neural network). Effects of regularization, kernel selection, network parameters, and dimensionality reduction are discussed. We determine that even when using a small training set, non-linear regression methods may create models within a useful error tolerance for screening of materials.
This work studies the influence of microstructures and crystalline defects on the superconductivity of MgB2, with the objective to improve its flux pinning. A MgB2 sample pellet that was hot isostatic pressed (HIPed) was found to have significantly increased critical current density (Jc) at high fields than its un-HIPed counterpart. The HIPed sample had a Jc of 10000 A/cm2 in 50000 Oe (5 T) at 5K. This was 20 times higher than that of the un-HIPed sample, and the same as the best Jc reported by other research groups. Microstructures observed in scanning and transmission electron microscopy indicate that the HIP process eliminated porosity present in the MgB2 pellet resulting in an improved intergrain connectivity. Such improvement in intergrain connectivity was believed to prevent the steep Jc drop with magnetic field H that occurred in the un-HIPed MgB2 pellet at H > 45000 Oe (4.5 T) and T = 5 K. The HIP process was also found to disperse the MgO that existed at the grain boundaries of the un-HIPed MgB2 pellet and to generate more dislocations in the HIPed the pellets. These dispersed MgO particles and dislocations improved flux pinning also at H<45000 Oe. The HIPing process was also found to lower the resistivity at room temperature.
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