Presentation
Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments
DescriptionArtificial Intelligence, combined with simulations and experiments, has great potential in accelerating scientific discovery, yet bridging the gap between simulations and experiments remains challenging due to time and scale disparities. Our research addresses this issue by developing a deep kernel-based surrogate model that learns from microscopic images to map structural features to energy differences from defect formation. We begin with full training using simulated images to establish optimal settings and create a baseline for active learning. Active learning is then employed to predict structures along simulation trajectories based on uncertainty and stability, reducing data requirements and computational costs. The model shows a low average error margin of approximately 0.03 meV. A autoencoder-decoder was developed as additional surrogate to enhance feature extraction and reconstruction, achieving a reconstruction loss of around 0.2 and facilitating precise comparisons between simulations and experiments. This approach advances real-time experimental guidance through computational simulations.