Functional Material Systems Enabled by Automated Data Extraction and Machine Learning

The development of new functional materials is crucial for addressing global challenges such as clean energy or the discovery drugs and antibiotics. Functional material systems are typically composed molecular building blocks, organized across multiple length scales in a hierarchical order. large design space allows precise tuning properties to specific applications, but also makes it time-consuming expensive screen optimal structures using traditional experimental methods. Machine learning (ML) models can potentially revolutionize field science by predicting chemical syntheses with high accuracy. However, ML require data be trained validated. Methods automatically extract from scientific literature make possible build diverse datasets models. In this article, opportunities extraction machine methods discussed accelerate high-performing systems, while ensuring that predicted stable, synthesizable, scalable, sustainable. potential impact language (LLMs) on process discussed. Additionally, importance research management tools overcome intrinsic limitations approaches.