Complex Oxides under Simulated Electric Field: Determinants of Defect Polarization in ABO3 Perovskites.

Polarization of ionic and electronic defects in response to high electric fields plays an essential role in determining properties of materials in applications such as memristive devices. However, isolating the polarization response of individual defects has been challenging for both models and measurements. Here the authors quantify the nonlinear dielectric response of neutral oxygen vacancies, comprised of strongly localized electrons at an oxygen vacancy site, in perovskite oxides of the form ABO3 . Their approach implements a computationally efficient local Hubbard U correction in density functional theory simulations. These calculations indicate that the electric dipole moment of this defect is correlated positively with the lattice volume, which they varied by elastic strain and by A-site cation species. In addition, the dipole of the neutral oxygen vacancy under electric field increases with increasing reducibility of the B-site cation. The predicted relationship among point defect polarization, mechanical strain, and transition metal chemistry provides insights for the properties of memristive materials and devices under high electric fields.

Direct Prediction of Phonon Density of States With Euclidean Neural Networks.

Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density-of-states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high-quality prediction using a small training set of ≈103 examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high-performance thermal storage materials and phonon-mediated superconductors.

Corrigendum to "Design of CGMP Production of 18F- and 68Ga-Radiopharmaceuticals".

[This corrects the article DOI: 10.1155/2014/680195.].

Design of CGMP production of 18F- and 68Ga-radiopharmaceuticals.

OBJECTIVE: Radiopharmaceutical production process must adhere to current good manufacturing process (CGMP) compliance to ensure the quality of precursor, prodrug (active pharmaceutical ingredient, API), and the final drug product that meet acceptance criteria. We aimed to develop an automated system for production of CGMP grade of PET radiopharmaceuticals. METHODS: The hardware and software of the automated synthesizer that fit in the hot cell under cGMP requirement were developed. Examples of production yield and purity for (68)Ga-DOTATATE and (18)F-FDG at CGMP facility were optimized. Analytical assays and acceptance criteria for cGMP grade of (68)Ga-DOTATATE and (18)F-FDG were established. RESULTS: CGMP facility for the production of PET radiopharmaceuticals has been established. Radio-TLC and HPLC analyses of (68)Ga-DOTATATE and (18)F-FDG showed that the radiochemical purity was 92% and 96%, respectively. The products were sterile and pyrogenic-free. CONCLUSION: CGMP compliance of radiopharmaceuticals has been reviewed. (68)Ga-DOTATATE and (18)F-FDG were synthesized with high radiochemical yield under CGMP process.