Autoencoders offer several advantages in the study of catalysis:
Unsupervised Learning: They can learn from unlabeled data, which is common in catalysis research where labeled data is often scarce. Noise Reduction: Autoencoders can filter out noise from experimental data, leading to more accurate analyses. Efficiency: They can handle large datasets efficiently, making them suitable for high-throughput screening of catalytic materials.