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Title: Evolutionary strategy for inverse charge measurements of dielectric particles

Here, we report a computational strategy to obtain the charges of individual dielectric particles from experimental observation of their interactions as a function of time. This strategy uses evolutionary optimization to minimize the difference between trajectories extracted from the experiment and simulated trajectories based on many-particle force fields. The force fields include both Coulombic interactions and dielectric polarization effects that arise due to particle-particle charge mismatch and particle-environment dielectric contrast. The strategy was applied to systems of free falling charged granular particles in a vacuum, where electrostatic interactions are the only driving forces that influence the particles' motion. We show that when the particles' initial positions and velocities are known, the optimizer requires only an initial and final particle configuration of a short trajectory in order to accurately infer the particles' charges; when the initial velocities are unknown and only the initial positions are given, the optimizer can learn from multiple frames along the trajectory to determine the particles' initial velocities and charges. While the results presented here offer a proof-of-concept demonstration of the proposed ideas, the proposed strategy could be extended to more complex systems of electrostatically charged granular matter.
Authors:
 [1] ;  [1] ;  [2] ;  [2] ; ORCiD logo [3] ;  [4]
  1. Univ. of Chicago, IL (United States). Inst. for Molecular Engineering
  2. Univ. of Chicago, IL (United States). James Franck Inst. and Dept. of Physics
  3. Argonne National Lab. (ANL), Argonne, IL (United States). Materials Science Division; Northwestern Univ. and Argonne National Lab. (ANL), Evanston, IL (United States). Northwestern Argonne Inst. of Science and Engineering (NAISE)
  4. Univ. of Chicago, IL (United States). Inst. for Molecular Engineering; Argonne National Lab. (ANL), Argonne, IL (United States). Materials Science Division
Publication Date:
Grant/Contract Number:
AC02-06CH11357; DMR-1420709; DMR-1309611
Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 148; Journal Issue: 23; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Research Org:
Argonne National Laboratory (ANL), Argonne, IL (United States). Midwest Integrated Center for Computational Materials (MICCoM); Univ. of Chicago, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; National Science Foundation (NSF)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; computer science and technology; dielectrics; vacuum tubes; electrostatics
OSTI Identifier:
1460679
Alternate Identifier(s):
OSTI ID: 1454651

Jiang, Xikai, Li, Jiyuan, Lee, Victor, Jaeger, Heinrich M., Heinonen, Olle G., and de Pablo, Juan J.. Evolutionary strategy for inverse charge measurements of dielectric particles. United States: N. p., Web. doi:10.1063/1.5027435.
Jiang, Xikai, Li, Jiyuan, Lee, Victor, Jaeger, Heinrich M., Heinonen, Olle G., & de Pablo, Juan J.. Evolutionary strategy for inverse charge measurements of dielectric particles. United States. doi:10.1063/1.5027435.
Jiang, Xikai, Li, Jiyuan, Lee, Victor, Jaeger, Heinrich M., Heinonen, Olle G., and de Pablo, Juan J.. 2018. "Evolutionary strategy for inverse charge measurements of dielectric particles". United States. doi:10.1063/1.5027435.
@article{osti_1460679,
title = {Evolutionary strategy for inverse charge measurements of dielectric particles},
author = {Jiang, Xikai and Li, Jiyuan and Lee, Victor and Jaeger, Heinrich M. and Heinonen, Olle G. and de Pablo, Juan J.},
abstractNote = {Here, we report a computational strategy to obtain the charges of individual dielectric particles from experimental observation of their interactions as a function of time. This strategy uses evolutionary optimization to minimize the difference between trajectories extracted from the experiment and simulated trajectories based on many-particle force fields. The force fields include both Coulombic interactions and dielectric polarization effects that arise due to particle-particle charge mismatch and particle-environment dielectric contrast. The strategy was applied to systems of free falling charged granular particles in a vacuum, where electrostatic interactions are the only driving forces that influence the particles' motion. We show that when the particles' initial positions and velocities are known, the optimizer requires only an initial and final particle configuration of a short trajectory in order to accurately infer the particles' charges; when the initial velocities are unknown and only the initial positions are given, the optimizer can learn from multiple frames along the trajectory to determine the particles' initial velocities and charges. While the results presented here offer a proof-of-concept demonstration of the proposed ideas, the proposed strategy could be extended to more complex systems of electrostatically charged granular matter.},
doi = {10.1063/1.5027435},
journal = {Journal of Chemical Physics},
number = 23,
volume = 148,
place = {United States},
year = {2018},
month = {6}
}