  Since their invention in the 1980s [1], optical tweezers have found a wide
range of applications, from biophotonics and mechanobiology to microscopy and
optomechanics [2, 3, 4, 5]. Simulations of the motion of microscopic particles
held by optical tweezers are often required to explore complex phenomena and to
interpret experimental data [6, 7, 8, 9]. For the sake of computational
efficiency, these simulations usually model the optical tweezers as an harmonic
potential [10, 11]. However, more physically-accurate optical-scattering models
[12, 13, 14, 15] are required to accurately model more onerous systems; this is
especially true for optical traps generated with complex fields [16, 17, 18,
19]. Although accurate, these models tend to be prohibitively slow for problems
with more than one or two degrees of freedom (DoF) [20], which has limited
their broad adoption. Here, we demonstrate that machine learning permits one to
combine the speed of the harmonic model with the accuracy of optical-scattering
models. Specifically, we show that a neural network can be trained to rapidly
and accurately predict the optical forces acting on a microscopic particle. We
demonstrate the utility of this approach on two phenomena that are
prohibitively slow to accurately simulate otherwise: the escape dynamics of
swelling microparticles in an optical trap, and the rotation rates of particles
in a superposition of beams with opposite orbital angular momenta. Thanks to
its high speed and accuracy, this method can greatly enhance the range of
phenomena that can be efficiently simulated and studied.
