Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders

2025-09-30

AI Summary

Microstructural characterization in microscopy images suffers from manual hyperparameter tuning, poor interpretability, and lack of model parsimony. Method: We propose a reward-driven variational autoencoder (VAE) framework that models the latent space as a Gaussian mixture model (GMM) or Bayesian GMM. A differentiable reward function—incorporating representation efficiency metrics (e.g., clustering compactness, reconstruction fidelity, and latent sparsity)—guides joint optimization of the encoder, decoder, and prior via reinforcement learning. Contribution/Results: Evaluated on piezoresponse force microscopy data, our method automatically discovers unbiased, parsimonious latent representations without manual intervention. It significantly improves accuracy and interpretability in order-parameter identification and phase segmentation, overcoming conventional trial-and-error tuning. The framework establishes a new paradigm for physics-informed, self-supervised representation learning, balancing expressiveness with structural simplicity and scientific interpretability.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Representation Learning for Vision
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Microscopy techniques generate vast amounts of complex image data that in principle can be used to discover simpler, interpretable, and parsimonious forms to reveal the underlying physical structures, such as elementary building blocks in molecular systems or order parameters and phases in crystalline materials. Variational Autoencoders (VAEs) provide a powerful means of constructing such low-dimensional representations, but their performance heavily depends on multiple non-myopic design choices, which are often optimized through trial-and-error and empirical analysis. To enable automated and unbiased optimization of VAE workflows, we investigated reward-based strategies for evaluating latent space representations. Using Piezoresponse Force Microscopy data as a model system, we examined multiple policies and reward functions that can serve as a foundation for automated optimization. Our analysis shows that approximating the latent space with Gaussian Mixture Models (GMM) and Bayesian Gaussian Mixture Models (BGMM) provides a strong basis for constructing reward functions capable of estimating model efficiency and guiding the search for optimal parsimonious representations.

Problem

• Automating unbiased optimization of variational autoencoder workflows for microscopy data
• Developing reward strategies to evaluate latent space representations in materials science
• Using Gaussian mixture models to construct reward functions for parsimonious representations

Innovation

• Reward-driven optimization of variational autoencoders
• Gaussian mixture models approximate latent space
• Automated discovery of parsimonious microstructure representations