Computational Innovation in Critical Minerals: Integrating Geological Data, AI and Sustainable Resource Planning

Isabelle Claire Beaumont

Computational Innovation in Critical Minerals: Integrating Geological Data, AI and Sustainable Resource Planning

Author(s)	Dr. Isabelle Claire Beaumont
Country	United States
Abstract	Critical minerals are essential to renewable-energy technologies, energy storage, digital infrastructure, advanced manufacturing, transportation electrification, and defense-related supply chains. Their strategic importance has increased demand for more reliable geological intelligence, efficient exploration, transparent resource assessment, and environmentally responsible planning. Conventional mineral exploration frequently relies on fragmented geological maps, geochemical surveys, geophysical observations, remote-sensing products, drilling records, and expert interpretations that differ considerably in scale, quality, accessibility, and uncertainty. Artificial intelligence offers opportunities to integrate these heterogeneous evidence sources, identify spatial relationships, improve mineral-prospectivity mapping, prioritize field investigation, and support scenario-based resource planning. However, predictive performance alone cannot establish whether an AI-supported mineral-development strategy is environmentally sustainable, socially legitimate, economically defensible, or operationally feasible. This conceptual and simulation-based study develops an integrated framework connecting geological data infrastructures, explainable artificial intelligence, uncertainty-aware mineral-prospectivity assessment, lifecycle information, environmental constraints, community considerations, and strategic resource planning. The study applies a structured conceptual synthesis and an illustrative portfolio-analysis procedure. It does not report newly discovered mineral deposits, measured reserves, confidential exploration data, or actual project-development decisions. Ten hypothetical planning zones are assessed using synthetic geological-confidence, sustainability-readiness, and integrated-priority scores. The analysis demonstrates that areas with strong geological evidence do not necessarily possess adequate sustainability readiness. AI-supported resource planning should therefore separate geological potential from development suitability and disclose the uncertainty associated with both dimensions. The proposed framework treats artificial intelligence as a decision-support capability operating within geological reasoning, field validation, lifecycle assessment, environmental governance, and human oversight. Computational innovation can contribute most effectively when it reduces informational uncertainty, makes assumptions auditable, directs costly field investigation efficiently, and prevents geological prospectivity from being interpreted as automatic authorization for extraction.
Keywords	Critical minerals; artificial intelligence; geological data integration; mineral-prospectivity mapping; sustainable mining; explainable AI; resource planning; uncertainty analysis.
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-15

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American Journal of Advanced Multidisciplinary Research and Innovation

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Computational Innovation in Critical Minerals: Integrating Geological Data, AI and Sustainable Resource Planning

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