Compressed sensing is a method that allows a significant reduction in the number of samples required for accurate measurements in many applications in experimental sciences and engineering. In this work, we show that compressed sensing can also be used to speed up numerical simulations. We apply compressed sensing to extract information from the real-time simulation of atomic and molecular systems, including electronic and nuclear dynamics. We find that, compared to the standard discrete Fourier transform approach, for the calculation of vibrational and optical spectra the total propagation time, and hence the computational cost, can be reduced by approximately a factor of five.sparse signal reconstruction | molecular dynamics | electron dynamics A recent development in the field of data analysis is the compressed sensing (CS) (or compressive sampling) method (1, 2). The foundation of the method is the concept of sparsity: A signal expanded in a certain basis is said to be sparse when most of the expansion coefficients are zero. This extra information can be used by the CS method to significantly reduce the number of measurements needed to reconstruct a signal. CS has been successfully applied to data acquisition in many different areas (3), including the improvement of the resolution of medical magnetic resonance imaging (4) and the experimental study of atomic and quantum systems (5-7).In this article we show that CS can also be an invaluable tool for some numerical simulations with a considerable reduction of the computational cost. We focus on atomistic simulations of nanoscopic systems by using CS to extract frequency-resolved information from real-time methods such as molecular dynamics (MD) and real-time electron dynamics.MD (8, 9) is one of the most widely used methods to study atomistic systems computationally as it can be used to compute many static and dynamical properties. In MD the trajectory of the atomic nuclei is obtained by integrating their equations of motion either with parametrized force fields or else by explicitly modeling the electrons (10). Given the importance of MD, developing methods that can improve the precision and reduce the computational cost of this method, especially for ab initio MD, can have a large impact in the field of atomistic simulation.Real-time electron dynamics, in particular real-time timedependent density functional theory (TDDFT) (11), plays a similarly important role in the study of linear and nonlinear electronic properties (12-15). Because of its scalability and parallelizability, real-time TDDFT is particularly efficient for large electronic systems (16), so an additional reduction in the computational cost can extend the boundaries of the system sizes that can be studied.Many physical properties are represented by frequency-dependent quantities. To obtain these from real-time information, usually a discrete Fourier transform (FT) is used. Our approach is to replace this FT by a calculation of the Fourier coefficients based on the CS method. To obtain a given ...