Learning viscosity from videos for high throughput characterization

We present a computer vision (CV) viscometer that infers fluid viscosity from visual flow features during vial inversion. Instead of relying on velocity fields, our approach uses a neural network to approximate the inverse mapping directly from video recordings. This demonstrates that simple optical patterns, not explicit velocity data, can encode sufficient information to infer fluid properties. The CV viscometer uses one camera and one motor to simultaneously record the flow of six inverted vials and estimate their viscosity. Parallel testing, together with no sample preparation, enables over 100 measurements per hour. Despite the uncontrolled flow - driven by gravity, surface tension, inertia, and initial conditions - the method achieves relative errors below 25% across nearly five orders of magnitude (0.01–1000 Pa.s), improving to 15% above 0.1 Pa.s. Notably, the dynamic range matches, and sometimes exceeds, that of traditional viscometers due to the multiscale flow of the inverted vial. The system reliably estimates viscosity of Newtonian fluids, with high deviation as a potential indicator of non-Newtonian behavior. It is low-cost, contactless, and easily integrates into both manual and automated workflows, enabling accessible, scalable viscosity inference for data-driven material discovery.

[1] Arretche, I., Hossain, M.T., Tiwari, R., Kim, A., Mills, M.G., Armstrong, C.D., Lessard, J.J., Tawfick, S.H. and Ewoldt, R.H., 2025. arXiv preprint arXiv:2506.02034.