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Rigid bodies, made of smaller composite beads, are commonly used to simulate anisotropic particles with molecular dynamics or Monte Carlo methods. To accurately represent the particle shape and to obtain smooth and realistic effective pair interactions between two rigid bodies, each body may need to contain hundreds of spherical beads. Given an interacting pair of particles, traditional molecular dynamics methods calculate all the inter-body distances between the beads of the rigid bodies within a certain distance. For a system containing many anisotropic particles, these distance calculations are computationally costly and limit the attainable system size and simulation time. However, the effective interaction between two rigid particles should only depend on the distance between their center of masses and their relative orientation. Therefore, a function capable of directly mapping the center of mass distance and orientation to the interaction energy between the two rigid bodies would completely bypass inter-bead distance calculations. It is challenging to derive such a general function analytically for almost any non-spherical rigid body. In this study, we have trained neural nets, powerful tools to fit nonlinear functions to complex datasets, to achieve this task. The pair configuration (center of mass distance and relative orientation) is taken as an input, and the energy, forces, and torques between two rigid particles are predicted directly. We show that molecular dynamics simulations of cubes and cylinders performed with forces and torques obtained from the gradients of the energy neural-nets quantitatively match traditional simulations that use composite rigid bodies. Both structural quantities and dynamic measures are in agreement, while achieving up to 23 times speedup over traditional molecular dynamics, depending on hardware and system size. The method presented here can, in principle, be applied to any irregular concave or convex shape with any pair interaction, provided that sufficient training data can be obtained.
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Orientation effects on near-field radiative heat transfer between complex-shaped dielectric particles
The effect of orientation on near-field radiative heat transfer between two complex-shaped superellipsoid particles of SiO 2 is presented. The particles under study are 50 nm in radius and of variable concavity. Orientation is characterized by the degree of rotational symmetry in the two-particle systems, and the radiative conductance is calculated using the discrete system Green's function approach to account for all electromagnetic interactions. The results reveal that the total conductance in some orientations can be up to twice that of other orientations when particles are at a center-of-mass separation distance of 110 nm. Orientation effects are not significantly correlated with system rotational symmetries but are strongly correlated with the minimum vacuum gap distance between particles. As such, orientation effects on near-field radiative heat transfer are a consequence of particle topology, with more extreme topologies leading to a continuation of orientation effects at larger particle center-of-mass separation distances. The concave superellipsoid particles display significant orientation effects up to a center-of-mass separation distance approximately equal to 3.9 times the particle radius, while the convex superellipsoid particles display significant orientation effects up to a center-of-mass separation distance approximately equal to 3.2 times the particle radius. In contrast to previous anisotropic, spheroidal dipole studies, these results of complex-shaped superellipsoid particles suggest that orientation effects become negligible when heat transfer is a volumetric process for all orientations. This work is essential for understanding radiative transport between particles that have non-regular geometries or that may have geometrical defects or abnormalities.
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- Award ID(s):
- 1952210
- PAR ID:
- 10414056
- Date Published:
- Journal Name:
- Applied Physics Letters
- Volume:
- 121
- Issue:
- 18
- ISSN:
- 0003-6951
- Page Range / eLocation ID:
- 182206
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0116828
