Machine learning active-nematic hydrodynamics
J. Colen*, M. Han*, R. Zhang, S.A. Redford, L.M. Lenna, L. Morgan, P.V. Ruijgrok, R. Adkins, Z. Bryant, Z. Dogic, M.L. Gardel, J. J. de Pablo, and V. Vitelli
Published in Proceedings of the National Academy of Sciences, 2021
Artificial intelligence holds considerable promise for transforming quantitative modeling in materials science. We illustrate this potential by developing machine-learning models of a paradigmatic class of biomaterials called active nematics. These hybrid materials can be viewed as artificial muscles composed of biological fibers and molecular motors. Here, the macroscopic coefficients characterizing energy injection by motors and material elasticity are not constant. They are unknown functions of space and time that we extract directly from experiments using neural networks. Our physics-inspired machine-learning algorithms can also forecast the evolution of these complex materials simply using image sequences from their past, without any knowledge of the governing dynamics.
Collaboration with Ming Han, Vincenzo Vitelli, and the Gardel, Dogic, de Pablo, and Bryant labs.