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Programmable active matter (PAM) combines information processing and energy transduction. The physical embodiment of information could be the direction of magnetic spins, a sequence of molecules, the concentrations of ions, or the shape of materials. Energy transduction involves the transformation of chemical, magnetic, or electrical energies into mechanical energy. A major class of PAM consists of material systems with many interacting units. These units could be molecules, colloids, microorganisms, droplets, or robots. Because the interaction among units determines the properties and functions of PAMs, the programmability of PAMs is largely due to the programmable interactions. Here, we review PAMs across scales, from supramolecular systems to macroscopic robotic swarms. We focus on the interactions at different scales and describe how these (often local) interactions give rise to global properties and functions. The research on PAMs will contribute to the pursuit of generalised crystallography and the study of complexity and emergence. Finally, we ponder on the opportunities and challenges in using PAM to build a soft-matter brain.

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In order to investigate the multistage oxidation behavior of aluminum silicon carbide (Al 4 SiC 4 ), back propagation artificial neural network (BP-ANN) has been trained and employed considering the oxidation temperature, time, and aspect ratio. The results denote that the BP-ANN model can accurately and efficiently simulate the oxidation behavior of Al 4 SiC 4 powders with different reaction laws. In addition, the extrapolation ability suggests that the BP-ANN model can maintain a high accuracy with the coefficient of determination ≥0.801 to expand the experimental data to 1.2 times the original range. By incorporating a real physical picture model developed by the research group, the experimental data can be further expanded to 2.2 times. This study can provide a new path to recognize the oxidation behavior of materials with multistage oxidation.

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