IC W20_phadiagurox Highlight: Systematics of the ambient melting points of stoichiometric uranium oxides [Slides]
Abstract not provided.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Engineering topics
Burakovsky, Leonid
Publications and source records attributed to Burakovsky, Leonid.
Automated discovery of a robust interatomic potential for aluminum
Machine learning, trained on quantum mechanics (QM) calculations, is a powerful tool for modeling potential energy surfaces. A critical factor is the quality and diversity of the training dataset. Here we present a highly automated approach to dataset construction and demonstrate the method by building a potential for elemental aluminum (ANI-Al). In our active learning scheme, the ML potential under development is used to drive non-equilibrium molecular dynamics simulations with time-varying applied temperatures. Whenever a configuration is reached for which the ML uncertainty is large, new QM data is collected. The ML model is periodically retrained on all available QM data. The final ANI-Al potential makes very accurate predictions of radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers. We perform a 1.3M atom shock simulation and show that ANI-Al force predictions shine in their agreement with new reference DFT calculations.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
IC W20_thermoelasticity Highlight: Dynamic strength - shear modulus scaling for tantalum at extreme pressures [Poster]
Two figures are shown: Pressure dependence of the shear modulus of Ta for two fundamental curves: the 300 K isotherm and the Hugoniot; and, Scaling of the high pressure strength (Y) of Ta with shear modulus (G). The dashed curve represents a hypothetical G which is fit such that the linear scaling approximation matches the data.
36 MATERIALS SCIENCE↗