Materials Informatics Across the Length Scales

TL;DR

This paper reviews data-driven materials informatics methods across different length scales, emphasizing current capabilities, challenges, and the importance of data standards for reliable multiscale modeling.

Contribution

It provides a comprehensive survey of multiscale materials informatics approaches, highlighting key challenges and future directions for cross-scale integration.

Findings

Machine-learning interatomic potentials are advancing nanoscale modeling.

Data quality and uncertainty are critical for reliable micro-to-continuum predictions.

Emerging tools like autonomous laboratories are shaping future multiscale workflows.

Abstract

Materials informatics is increasingly used to support modelling, analysis and design across the length scales of materials science, from atomistic simulations to microstructural characterisation and continuum descriptions. Despite rapid progress, the reliability and transferability of these approaches vary strongly with scale. Here we survey data-driven methods at the nanoscale, mesoscale, and micro-to-continuum levels, highlighting established capabilities as well as unresolved challenges. Machine-learning interatomic potentials, mesoscale surrogate and operator-learning models, and learning-based analysis of experimental microstructures are discussed, with emphasis on data quality, uncertainty, interpretability, and cross-scale consistency. We further examine the role of data standards, ontologies, and emerging tools, such as autonomous laboratories, where they directly affect multiscale workflows. This perspective clarifies what can be considered reliable today and identifies key obstacles to the broader integration of materials informatics across scales.