Increasing efficiency and accuracy of magnetic interaction calculations in colloidal simulation through machine learning

• Published in: Journal of colloid and interface science Vol. 611; pp. 29 - 38
• Main Authors: Pan, Chunzhou, Mahmoudabadbozchelou, Mohammadamin, Duan, Xiaoli, Benneyan, James C., Jamali, Safa, Erb, Randall M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2022
• Subjects: Algorithms; Colloidal forces; Computer Simulation; Dipole model; Machine Learning; Magnetic particle interactions; Magnetic Phenomena; Multi-fidelity neural network; Neural Networks, Computer; Dipole model; Magnetic particle interactions; Multi-fidelity neural network; Machine Learning; Colloidal forces
• ISSN: 0021-9797; 1095-7103
• DOI: 10.1016/j.jcis.2021.11.195

[Display omitted] Calculating the magnetic interaction between magnetic particles that are positioned in close proximity to one another is a surprisingly challenging task. Exact solutions for this interaction exist either through numerical expansion of multipolar interactions or through solving Maxwell’s equations with a finite element solver. These approaches can take hours for simple configurations of three particles. Meanwhile, across a range of scientific and engineering problems, machine learning approaches have been developed as fast computational platforms for solving complex systems of interest when large data sets are available. In this paper, we bring the touted benefits of recent advances in science-based machine learning algorithms to bear on the problem of modeling the magnetic interaction between three particles. We investigate this approach using diverse machine learning systems including physics informed neural networks. We find that once the training data has been collected and the model has been initiated, simulation times are reduced from hours to mere seconds while maintaining remarkable accuracy. Despite this promise, we also try to lay bare the current challenges of applying machine learning to these and more complex colloidal systems.