James Hickman
Recent Research and Publication Summary
Development of a general-purpose machine-learning interatomic potential for aluminum by the physically informed neural network method
https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.4.113807 (new window)
Interatomic potentials constitute the key component of large-scale atomistic simulations of materials. The recently proposed physically informed neural network (PINN) method combines a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential applicable to both metals and nonmetals. In this paper, we present a modified version of the PINN method that accelerates the potential training process and further improves the transferability of PINN potentials to unknown atomic environments. As an application, a modified PINN potential for Al has been developed by training on a large database of electronic structure calculations. The potential reproduces the reference first-principles energies within 2.6 meV per atom and accurately predicts a wide spectrum of physical properties of Al. Such properties include, but are not limited to, lattice dynamics, thermal expansion, energies of point and extended defects, the melting temperature, the structure and dynamic properties of liquid Al, the surface tensions of the liquid surface and the solid-liquid interface, and the nucleation and growth of a grain boundary crack. Computational efficiency of PINN potentials is also discussed.
Temperature fluctuations in canonical systems: Insights from molecular dynamics simulations
https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.184311
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Molecular dynamics simulations of a quasiharmonic solid are conducted to elucidate the meaning of temperature fluctuations in canonical systems and validate a well-known but frequently contested equation predicting the mean square of such fluctuations. The simulations implement two virtual and one physical (natural) thermostat and examine the kinetic, potential, and total energy correlation functions in the time and frequency domains. The results clearly demonstrate the existence of quasiequilibrium states in which the system can be characterized by a well-defined temperature that follows the mentioned fluctuation equation. The emergence of such states is due to the wide separation of time scales between thermal relaxation by phonon scattering and slow energy exchanges with the thermostat. The quasiequilibrium states exist between these two time scales when the system behaves as virtually isolated and equilibrium.
Nickel nanoparticles set a new record of strength
https://www.nature.com/articles/s41467-018-06575-6
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Material objects with micrometer or nanometer dimensions can exhibit much higher strength than macroscopic objects, but this strength rarely approaches the maximum theoretical strength of the material. Here, we demonstrate that faceted single-crystalline nickel (Ni) nanoparticles exhibit an ultrahigh compressive strength (up to 34 GPa) unprecedented for metallic materials. This strength matches the available estimates of Ni theoretical strength. Three factors are responsible for this record-high strength: the large Ni shear modulus, the smooth edges and corners of the nanoparticles, and the thin oxide layer on the particle surface. This finding is supported by molecular dynamics simulations that closely mimic the experimental conditions, which show that the mechanical failure of the strongest particles is triggered by homogeneous nucleation of dislocation loops inside the particle. The nucleation of a stable loop is preceded by multiple nucleation attempts accompanied by unusually large local atomic displacements caused by thermal fluctuations.
Thermal conductivity and its relation to atomic structure for symmetrical tilt grain boundaries in silicon
https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.4.033405
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We perform a systematic study of thermal resistance and conductance of tilt grain boundaries (GBs) in Si using classical molecular dynamics. The GBs studied are naturally divided into three groups according to the structural units forming the GB core. We find that, within each group, the GB thermal conductivity strongly correlates with the excess GB energy. All three groups predict nearly the same GB conductivity extrapolated to the high-energy limit. This limiting value is close to the thermal conductivity of amorphous Si, suggesting similar heat transport mechanisms. While the lattice thermal conductivity decreases with temperature, the GB conductivity slightly increases. However, at high temperatures it turns over and starts decreasing if the GB structure undergoes a premelting transformation. Analysis of vibrational spectra of GBs resolved along different directions sheds light on the mechanisms of their thermal resistance. The existence of alternating tensile and compressive atomic environments in the GB core gives rise to localized vibrational modes, frequency gaps creating acoustic mismatch with lattice phonons, and anharmonic vibrations of loosely bound atoms residing in open atomic environments.