Multimodal Deep Learning for Scientific Imaging Interpretation

In the domain of scientific imaging, interpreting visual data often demands an intricate combination of human expertise and deep comprehension of the subject materials. This study presents a novel methodology to linguistically emulate and subsequently evaluate human-like interactions with Scanning Electron Microscopy (SEM) images, specifically of glass materials. Leveraging a multimodal deep learning framework, our approach distills insights from both textual and visual data harvested from peer-reviewed articles, further augmented by the capabilities of GPT-4 for refined data synthesis and evaluation. Despite inherent challenges--such as nuanced interpretations and the limited availability of specialized datasets--our model (GlassLLaVA) excels in crafting accurate interpretations, identifying key features, and detecting defects in previously unseen SEM images. Moreover, we introduce versatile evaluation metrics, suitable for an array of scientific imaging applications, which allows for benchmarking against research-grounded answers. Benefiting from the robustness of contemporary Large Language Models, our model adeptly aligns with insights from research papers. This advancement not only underscores considerable progress in bridging the gap between human and machine interpretation in scientific imaging, but also hints at expansive avenues for future research and broader application.

Deep Learning Phase Segregation

Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems. In this work, we present a data-driven approach for the learning, modeling, and prediction of phase segregation. A direct mapping between an initially dispersed, immiscible binary fluid and the equilibrium concentration field is learned by conditional generative convolutional neural networks. Concentration field predictions by the deep learning model conserve phase fraction, correctly predict phase transition, and reproduce area, perimeter, and total free energy distributions up to 98% accuracy.