Geochemical Fingerprints and Deep Learning in Critical Metal Enrichment: A Global Perspective
A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".
Deadline for manuscript submissions: 31 January 2026
Special Issue Editors
Interests: mineral deposits; application of high-purity quartz
Special Issues, Collections and Topics in MDPI journals
Interests: hydrothermal geochemistry; mineral deposits
Special Issue Information
Dear Colleagues,
The global transition toward low-carbon energy systems, advanced electronics, and digital technologies has driven an unprecedented demand for critical metals such as lithium, rare earth elements, cobalt, and tungsten. Understanding the geological and geochemical processes that lead to their enrichment has therefore become a central pursuit in modern mineralogy and geochemistry. Over the past decade, rapid progress in analytical techniques—ranging from in situ isotope microanalysis to trace element mapping—has unveiled new “geochemical fingerprints” that record the complex interplay between magmatic, hydrothermal, and supergene processes responsible for critical metal concentrations. These fingerprints not only provide key insights into ore-forming systems but also form the foundation for quantitative models of mineralization and resource prediction.
At the same time, the integration of deep learning and other data-driven methods has revolutionized our ability to interpret multi-dimensional geological datasets. Machine learning algorithms, when coupled with geochemical and mineralogical data, can discern subtle patterns and predictive signatures that are often overlooked by traditional approaches. From regional metallogenic zoning to mineral chemistry classification and ore potential mapping, artificial intelligence has emerged as a transformative tool in critical metal exploration and modeling.
This Special Issue, “Geochemical Fingerprints and Deep Learning in Critical Metal Enrichment: A Global Perspective,” brings together cutting-edge research and reviews from diverse geological settings across the world. The contributions highlight innovative applications of geochemical proxies, isotopic tracers, and computational techniques to unravel the mechanisms of critical metal enrichment in magmatic, metamorphic, and sedimentary environments. By bridging geochemistry and artificial intelligence, this collection aims to foster a new paradigm in mineral exploration—one that integrates process-based understanding with predictive analytics—to guide sustainable resource discovery in the era of green and intelligent mining.
Prof. Dr. Xiaoyong Yang
Prof. Dr. Xinsong Wang
Dr. Huishan Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- geochemical fingerprints
- deep learning
- critical metals
- mineralization processes
- global perspective
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