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July 31, 2026Open Access

Data-Driven Materials Science for Energy-Sustainable Applications

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Authors

JCJacqueline M. Cole

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Overview

Review illustrates AI-driven data capture methods for materials science in energy sustainability applications.

Key Points

  • The review aims to address the integration of AI in materials science to enhance energy sustainability.
  • Describes algorithms for encoding structure-property relationships in materials science.
  • Illustrates six case studies on applications of AI in materials discovery and optimization.
  • Discusses challenges and opportunities of accessing experimental data using modern technologies.
  • Identifies pathways for utilizing existing dark data in materials science.
  • Highlights the role of electronic lab notebooks in data collection and sharing.
  • Envisions future AI workflows for decision-making in energy-sustainable materials.

Cite This Study

Jacqueline M. Cole (2026) studied this question.

synapsesocial.com/papers/6a6c46f4747664a1aa73bf2dhttps://doi.org/10.17863/cam.132797
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