**Abstract:** The long-term stability of magnetic nanoparticle (MNP) dispersions within polymer coatings remains a significant challenge for the widespread adoption of magneto-rheological (MR) and other MNP-enabled applications. This research introduces a novel prediction framework leveraging multi-modal data fusion and a recurrent neural network (RNN) architecture to accurately forecast MNP agglomeration and subsequent loss of function within polymer matrices. By integrating microscopic particle tracking data, macroscopic rheological measurements, and thermodynamic parameters, our system achieves a 5x improvement in dispersion stability prediction accuracy compared to existing empirical models, opening avenues for accelerated coating formulation and optimization.

Developing artificial synapses capable of optical signal recognition is crucial for advancing neuromorphic computing. However, achieving high accuracy of synapse-based optical signal recognition, which avoids complex procedures of electrode fabrication and follows a contactless pathway, remains a significant challenge. In this study, we utilize a photochromic film of chemically synthesized WO3 with a transmission modulation of 77% to construct all-optical artificial synapses. Unlike optoelectronic approaches, the synapses leverage light for stimulation and contactless response measurement. Typical synaptic behaviors, including paired-pulse facilitation, learning experience, short-term memory, and long-term memory, can be demonstrated through transmittance responses under UV beam stimulation. Furthermore, the WO3 thin film can simulate the memory behavior of human skin’s UV detection when transitioning from outdoor to indoor environments and back to outdoor conditions. Next, a recurrent neural network processes the synaptic transmittance responses to recognize optical signals, achieving 100% accuracy for preset light exposure durations and powers, 95% accuracy for closely spaced durations with a 1 s difference, and 100% accuracy for power differences of 14.5 mW. Moreover, 26 English alphabet letters encoded to different optical pulse trains can be recognized using an all-optical artificial synapse integrated with a recurrent neural network, achieving 100% accuracy. This work highlights the potential of photochromic materials in enabling high-performance neuromorphic computing and provides a new pathway for integrating optical signal processing with artificial intelligence.