Machine learning in nanoscience and beyond: Workflows, data processing, XAI and ITAP metrics, language-based models.

Area of Science:

• * Nanoscience, encompassing colloids, interfaces, and material science.
• * Integration of artificial intelligence (AI) and machine learning (ML) into scientific discovery.

Background:

• * Previous work (Adv. Colloids Interface Sci. 2025, 343, 103546) covered ML in nanoarchitectonics.
• * This review extends the analysis to modern workflows, data processing, and language models.

Purpose of the Study:

• * To review AI applications in colloidal and interfacial science.
• * To explore data processing, model development, and AI-assisted material science.

Main Methods:

• * Organized around data acquisition, model development (shallow to deep learning), and AI-assisted fabrication.
• * Comparison of supervised, unsupervised, semi-supervised, and reinforcement learning models.
• * Examination of explainable AI (XAI) using the ITAP framework.

Main Results:

• * AI models applied across the materials innovation pipeline: design, synthesis, property prediction, and performance evaluation.
• * XAI framework enhances model transparency, reliability, and interpretability.
• * Discussion on autonomous laboratories and future directions in AI-driven nanoarchitectonics.

Conclusions:

• * AI and ML are pivotal for advancing nanoarchitectonics.
• * Language models are expected to play a significant role in future AI applications.
• * Emphasis on transparent, reproducible, and sustainable AI-driven research.