Datamaterials

Datamaterials is a field at the intersection of materials science and data science that emphasizes using data as a core resource for designing, discovering, and understanding materials. In this view, datasets describing crystal structures, properties, synthesis conditions, and experimental results are treated as a material itself, manipulated with models and algorithms to uncover patterns and guide decision making. The term is closely associated with materials informatics and the broader Materials Genome Initiative, which promotes data sharing, standardization, and computational screening to accelerate discovery.

Key methods include high-throughput computation and experiments, data curation, standardized representations (descriptors), machine learning, and active learning. Datamaterials relies on large, interoperable databases and reproducible workflows to enable scalable predictions of material behavior and rapid screening of candidates before synthesis.

Applications span energy materials such as batteries and photovoltaics, catalysts, ceramics, polymers, and metamaterials, as well as data storage materials where information is encoded in physical states. The approach supports property prediction, optimization of synthesis routes, and the design of materials with targeted performance and reliability.

Challenges include data quality and interoperability, uneven data coverage across material classes, biases in datasets, reproducibility of experiments, and the need for common ontologies and benchmarks to enable cross-lab comparisons. Ensuring data provenance, privacy in industry contexts, and integrating experimental and computational results remain active areas of development.

Datamaterials is an evolving field that aims to shorten discovery cycles and build more robust design pipelines by integrating data science with experimental and computational material science.