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Review Article

Data-Driven Discovery of Sustainable Materials

Ozaki, Moeko*1ORCID

* Corresponding author

*1 Chiba University [email protected]
Sustainable Materials Materials Informatics Machine Learning Green Chemistry Life Cycle Assessment Artificial Intelligence Circular Economy

Abstract

The increasing environmental challenges associated with climate change, resource depletion, and industrial pollution have intensified the demand for sustainable materials that support a circular economy sustainable material. Traditional materials discovery methods are often labor-intensive, time-consuming, and dependent on costly experimental trial-and-error approaches, limiting the rapid development of environmentally friendly materials. In recent years, materials informatics (MI), combined with advances in artificial intelligence (AI) and machine learning (ML), has emerged as a transformative paradigm capable of accelerating the discovery and optimization of sustainable materials. This article presents a comprehensive data-driven framework for sustainable material discovery that integrates large-scale materials databases, sustainability indicators, and predictive machine learning models. Open scientific repositories such as Materials Project, NOMAD, and PubChem are discussed as essential data infrastructures supporting computational materials science. The proposed framework incorporates sustainability metrics including life cycle assessment (LCA), carbon footprint analysis, recyclability, biodegradability, and energy-efficient synthesis pathways. Furthermore, the study examines the application of supervised learning techniques for property prediction, unsupervised learning for material classification, and generative deep learning models for designing novel eco-friendly compounds. The article also addresses major challenges including data quality, model interpretability, and experimental validation. Finally, the work highlights the alignment of sustainable materials informatics with the United Nations Sustainable Development Goals (SDGs), emphasizing the importance of interdisciplinary collaboration among materials scientists, chemists, data scientists, and policymakers.

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Article Info
  • Received: 2026-04-01
  • Accepted: 2026-05-10
  • Published: 2026-05-11
  • Pages: 81-139
  • Citations: 0
  • Type: Review Article
  • Volume: 2
  • Version: 2026-05-11 (1)
  • License: CC BY 4.0