We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO 2) dataset generated from a "melt-quench" ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO 2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies.

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Complex microstructure changes occur in nuclear fuel and structural materials due to the extreme environments of intense irradiation and high temperature. This paper evaluates the role of the phase field method in predicting the microstructure evolution of irradiated nuclear materials and the impact on their mechanical, thermal, and magnetic properties. The paper starts with an overview of the important physical mechanisms of defect evolution and the significant gaps in simulating microstructure evolution in irradiated nuclear materials. Then, the phase field method is introduced as a powerful and predictive tool and its applications to microstructure and property evolution in irradiated nuclear materials are reviewed. The review shows that (1) Phase field models can correctly describe important phenomena such as spatial-dependent generation, migration, and recombination of defects, radiation-induced dissolution, the Soret effect, strong interfacial energy anisotropy, and elastic interaction; (2) The phase field method can qualitatively and quantitatively simulate two-dimensional and three-dimensional microstructure evolution, including radiation-induced segregation, second phase nucleation, void migration, void and gas bubble superlattice formation, interstitial loop evolution, hydrate formation, and grain growth, and (3) The Phase field method correctly predicts the relationships between microstructures and properties. The final section is dedicated to a discussion of the strengths and limitations of the phase field method, as applied to irradiation effects in nuclear materials. npj Computational Materials (2017) 3:16 ; doi:10.1038/s41524-017-0018-y INTRODUCTIONHigh energy particle (such as neutron, ion, and electron) radiation can create major changes in the shape and thermo-mechanical properties of nuclear fuels and structural components of nuclear reactors. These changes are caused by radiation-induced evolution of compositions and microstructures. The main effects of radiation on reactor materials are: (1) dimensional change associated with gas bubble swelling, void swelling, grain growth, and creep; 1-5 (2) loss of ductility and increase in ductile-brittle transition temperature (DBTT) due to the formation of secondphase precipitates, self-interstitial atomic (SIA) loops, and dislocation networks; 6, 7 (3) oxidation and corrosion accelerated by high temperature, fission products, and radiation damage; 8-10 and (4) local and bulk changes in chemical composition, including irradiation-enhanced segregation of alloy components and phase separation.11-17 Figure 1 shows typical microstructures observed in irradiated materials.9, 18-21 The radiation-induced heterogeneity of the microstructures depends on the initial phase and defect structure of the fresh (non-irradiated) materials and on the type and severity of the radiation environment. Fundamental understanding of heterogeneous three-dimensional microstructure evolution is crucial to the development of advanced radiation tolerant materials that can significa...

OverviewIn this article, the semi-empirical modifi ed embedded atom method is used to develop a model of Pu-Ga alloys. Employing classical calculations, the model is used to predict thermodynamic properties of these alloys as well as the complex Pu-Ga phase diagram.

Understanding the atomic structure of amorphous solids is important in predicting and tuning their macroscopic behavior. Here, we use a combination of high-energy X-ray diffraction, neutron diffraction, and molecular dynamics simulations to benchmark the atomic interactions in the high temperature stable liquid and low-density amorphous solid states of hafnia. The diffraction results reveal an average Hf–O coordination number of ~7 exists in both the liquid and amorphous nanoparticle forms studied. The measured pair distribution functions are compared to those generated from several simulation models in the literature. We have also performed ab initio and classical molecular dynamics simulations that show density has a strong effect on the polyhedral connectivity. The liquid shows a broad distribution of Hf–Hf interactions, while the formation of low-density amorphous nanoclusters can reproduce the sharp split peak in the Hf–Hf partial pair distribution function observed in experiment. The agglomeration of amorphous nanoparticles condensed from the gas phase is associated with the formation of both edge-sharing and corner-sharing HfO6,7 polyhedra resembling that observed in the monoclinic phase.