1. Comparison with Experimental Data: Experimental data provides a benchmark for model predictions. For example, scanning tunneling microscopy (STM) and atomic force microscopy (AFM) can provide atomic-scale images and measurements that can be directly compared to model outputs.
2. Benchmarking Against High-Level Calculations: Models can be validated against more accurate, albeit computationally expensive, methods. For example, results from Quantum Monte Carlo (QMC) calculations can serve as a benchmark for validating DFT models.
3. Cross-Validation: Using multiple independent models to predict the same property and comparing their results can help identify discrepancies and improve accuracy.
