Reward Driven Image Analysis Workflow in Static and Active Learning

The advent of electron microscopy has markedly expanded our capabilities to acquire atomically resolved images of complex material microstructures, producing vast datasets that transcend the limits of human vision [1]. This surge in data generation necessitates sophisticated analytical methodologies capable of not just processing this data, but more critically, of extracting significant insights into the material properties and mechanisms at play. This challenge is compounded by the vast array of possible analytical paths and the intricate tuning of parameters, making the identification of an optimal workflow a daunting task. The development of workflows that can interpret recorded data or analyze streaming data in active learning —be it images, spectra, or hyperspectral data—into human-understandable formats becomes imperative. Here we propose an approach based on the concept of a reward function, intricately linked to the experimental objectives and the broader context, yet quantifiable upon experiment completion. A comprehensive articulation of the workflow's sequential steps is illustrated in Figure 1. A key step is defining reward functions by human considering the analysis goal and what domain feature that he wants to observe. In the model scenario, the initial step involves the establishment of a reward function designed to identify atomic positions that closely approximate the ground truth, specifically, the number of atoms as detected by a deep convolutional neural network. Another reward function involves identifying the most effective clustering technique capable of closely approximating the stoichiometry of the target material. Once defined, these reward function allow optimization of the workflow, including both combinatorial analysis selection and continuous parameter optimization via Bayesian Optimization [2, 3], thereby ensuring the attainment of results that are both precise and aligned with the human-defined objectives. As an example, as presented in (Figure. 2) the workflow has been implemented using the Scikit-Optimize library to fine-tune the model's hyperparameters, ensuring that the clustering provided by the Gaussian Mixture Model (GMM) closely aligns with the YBa2Cu3Ox (YBCO) stoichiometry [4, 5]. The extension of reward-building workflows to accommodate more complex, multi-step optimization processes, along with the utilization of large language models for the probabilistic construction of reward functions, is discussed in (Figure 1, 3). Workflow of an image analysis framework contrasting traditional methods with a novel approach. The red pathways indicate the conventional process incorporating human insight at multiple stages for transformation, clustering, and dimensionality reduction. The green pathway represents an innovative method that integrates parameter optimization with a reward function to guide the workflow towards optimal output visualization and analysis. (A) A scatter plot illustrating the data clustered by the (GMM), utilizing Yttrium (Y), Barium (Ba), and Copper (Cu) atoms as the basis for clustering. (B) Predictions of YBCO stoichiometry through GMM clustering, guided by a predetermined reward function. (C) Predictions of YBCO stoichiometry through GMM clustering, informed by human insight and fine-tuning. (A) Atomic positions on a crystal lattice identified by the Laplacian of Gaussian method. Shows, (B) Scatter plot of data clustered by the Gaussian Mixture Model (GMM) based on only heavy (In this case Y, Ba) atoms, (C) the scattering structure of the clustered atoms on the HAADF image, (D) Structured representation of a VAE's latent space, visualized as a 2D grid of Gaussian distributions, (E) Clusters in VAE latent space delineated by a Gaussian Mixture Model (GMM). (F) Color-coded heat map displaying the activations within the latent space of a Variational Autoencoder (VAE).

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