ML models, like CNNs, classify and characterize nanoparticles using SEM or TEM images

ML-driven design tailors nanoparticles for specific applications, such as fuel cell catalysts

ML predicts essential nanoparticle properties, including conductivity, stability, and toxicity

ML-driven synthesis of hardware improves accuracy and efficiency in nanoparticle production

ML optimizes nanoparticle synthesis conditions, such as temperature, pH, and reaction time, to achieve tailored results