Using molecular dynamics methods, we have simulated several model graphite nanometer-scale laser driven motors. To our knowledge, this is the first study of laser excitation of a nanometer-scale device. The motors consisted of two concentric graphite cylinders (shaft and sleeve) with one positive and one negative electric charge attached to the shaft; rotational motion of the shaft was induced by applying one or sometimes two oscillating laser fields. The shaft cycled between periods of rotational pendulum-like behavior and unidirectional rotation (motor-like behavior). The motor on and off times strongly depended on the motor size, field strength and frequency, and relative location of the attached positive and negative charges. In addition, the two-laser simulations showed much larger motor on times and more stable rotation than one-laser simulations. A mathematical model of the overall process was obtained by employing computational neural networks (CNNs). A CNN was able to 'learn' the mapping from size, charge position, frequency, and strength of the electric field to the motor on and off times. This multidimensional, nonlinear mapping was determined to within an average accuracy of 2 and could be used to determine initial parameters that would lead to better overall performance of the nanomotor.
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