<p>High-throughput materials characterization is essential for accelerating materials discovery. To enable high-throughput characterization, machine learning (ML) has been a powerful tool. However, the broader application of ML in experimental settings is limited by key challenges, i.e., the scarcity of labeled experimental data and the lack of uncertainty estimation in model predictions. In this work, we present Uncertainty-aware Simulation-to-Experiment Modeling (USEM), a novel approach that enables ML models trained on labeled simulation data to be adapted for analyzing unlabeled or sparsely labeled experimental data. The method uses adversarial domain adaptation in the latent space to reduce the distance between simulation and experiment data, while incorporating predictive uncertainty through spectral-normalized neural Gaussian processes (SNGP). We demonstrate the effectiveness of USEM on both synthetic and experimental X-ray diffraction (XRD) data, showing improved predictive accuracy and the ability to identify out-of-distribution samples. USEM offers a scalable and trustworthy solution for high-throughput characterization in ML-driven materials discovery.</p>