Publications

Wood, Mitchell A.; Nikolov, Svetoslav V.; Rohskopf, Andrew D.; Desjarlais, Michael P.; Cangi, Attila C.; Tranchida, Julien T.

Fully characterizing high energy density (HED) phenomena using pulsed power facilities (Z machine) and coherent light sources is possible only with complementary numerical modeling for design, diagnostic development, and data interpretation. The exercise of creating numerical tests, that match experimental conditions, builds critical insight that is crucial for the development of a strong fundamental understanding of the physics behind HED phenomena and for the design of next generation pulsed power facilities. The persistence of electron correlation in HED ma- terials arising from Coulomb interactions and the Pauli exclusion principle is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. An exemplar is a fer- romagnetic material like iron, while familiar and widely used, we lack a simulation capability to characterize the interplay of structure and magnetic effects that govern material strength, ki- netics of phase transitions and other transport properties. Herein we construct and demonstrate the Molecular-Spin Dynamics (MSD) simulation capability for iron from ambient to earth core conditions, all software advances are open source and presently available for broad usage. These methods are multi-scale in nature, direct comparisons between high fidelity density functional the- ory (DFT) and linear-scaling MSD simulations is done throughout this work, with advancements made to MSD allowing for electronic structure changes being reflected in classical dynamics. Main takeaways for the project include insight into the role of magnetic spins on mechanical properties and thermal conductivity, development of accurate interatomic potentials paired with spin Hamil- tonians, and characterization of the high pressure melt boundary that is of critical importance to planetary modeling efforts.

npj Computational Materials

Nikolov, Svetoslav V.; Wood, Mitchell A.; Cangi, Attila; Maillet, Jean B.; Marinica, Mihai C.; Thompson, Aidan P.; Desjarlais, Michael P.; Tranchida, Julien G.

A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP. Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed. Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations. We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP for α-iron. The combined potential energy surface yields excellent agreement with first-principles magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus, magnetization, and specific heat across the ferromagnetic–paramagnetic phase transition.

Nikolov, Svetoslav V.; Peterson, Kara J.; Bolintineanu, Dan S.; Clemmer, Joel T.

Abstract not provided.

Peterson, Kara J.; Nikolov, Svetoslav V.; Bolintineanu, Dan S.; Clemmer, Joel T.; Turner, Adrian T.

Abstract not provided.

Nikolov, Svetoslav V.; Rodgers, Theron R.

Abstract not provided.