AI & Environment Resource HubMining and material supply chains through hardware reuse and electronic waste.
Browse all 84 resources →Counts reflect provisional metadata classification, not measured environmental impact. A record may belong to multiple topics.
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Research papers and scholarly literature on impacts, applications, and methods.
This paper presents SOLID-MAP, an AI-driven material acceleration platform that integrates active learning, thermodynamic modeling, and high-throughput experimentation to rapidly discover and evaluate high-entropy alloys for energy-relevant applications.
Matbench Discovery is a benchmarking framework for machine learning models used in materials discovery, highlighting the importance of task-specific metrics and demonstrating that universal interatomic potentials can effectively pre-screen stable inorganic materials in high-throughput workflows.
This research explores how artificial intelligence (AI) can be leveraged to optimize packaging design, reduce operational costs, and enhance sustainability in e-commerce. As packaging waste and shipping inefficiencies grow alongside global online retail demand, traditional methods for determining box size, material use, and logistics planning have become economically and environmentally inadequate.
Missing metadata is a curation task, not evidence of absent research or activity. The Resource Hub does not currently contain a validated incident dataset, expert survey, or measurements of net environmental benefit.