In supervised learning, the model is trained using a dataset that contains input-output pairs. The input could be characteristics like particle size, shape, or chemical composition, while the output could be properties like thermal stability or toxicity. The algorithm learns to map the input to the output, enabling it to make accurate predictions on new, unseen data.
