Mineral Evolution
Mineral Informatics: A Key to Deep-Time Discovery in Earth and Planetary Sciences
Co-Conveners
Robert M. Hazen, Carnegie Institution for Science
Sergey Krivovichev, Kola Science Center, Russian Academy of Sciences
Yan Li, School of Earth and Space Sciences, Peking University
Shaunna M. Morrison, Department of Earth and Planetary Sciences, Rutgers University
Dietmar Muller, School of Geosciences, University of Sydney
Anirudh Prabhu, Earth and Planets Laboratory, Carnegie Institution for Science
Hans-Peter Schertl, Institute of Geosciences, Ruhr University Bochum
Jun Tsuchiya, Ehime University, Ehime 790-0825, Japan
Minerals and rocks are information-rich, multi-dimensional, and multi-scale natural systems that preserve hundreds of compositional, structural, morphological, and other environmental signatures of planetary history, serving as time capsules. The convergence of mineralogy with data science and informatics is revolutionizing our ability to decode these records during planetary evolution. Progress in mineral informatics rests on four pillars: (1) development of large and rapidly expanding open-access data resources, (2) application of powerful data analytical and visualization methods, (3) development of artificial intelligent algorithm for complex data distribution, and (4) interdisciplinary interpretation and quantitative rules extraction of results after applying these data and methods. Accordingly, we solicit contributions to a dynamic and forward-looking session on mineral informatics, including research related to the mineralogy, petrology, and/or geochemistry of Earth and other planets and moons. Topics include:
•Development and expansion of open and reliable data resources that conform to FAIR practices (Findable, Accessible, Interoperable, and Reusable).
•Co-evolutionary trends in planetary mineralogy, petrology, geochemistry, tectonics, and their links to biosphere.
•Applications of artificial intelligence and machine learning in mineral resource exploration, including predictive modeling for discovering new economic deposits (e.g., Li, REEs), optimizing resource extraction, and reducing environmental impacts .
•Community detection, unsupervised learning, network analysis, and clustering algorithms applied to mineral/rock identification, classification, and planetary evolution staging.
•Search for large-scale evolutionary trends in structural and chemical complexity of deep-time minerals, exploring emergent properties from atomic to planetary-scale interactions.
•Correlations between microbial communities (including metalloprotein), and mineralogy, petrology, geochemistry, and other environmental parameters, exploring how biomineralization, redox-sensitive metal cofactor adaptation, and geochemical niche construction drive co-evolutionary feedbacks across deep time.
*IMA sessions on Earth and planetary materials will be sponsored in part by the Deep-Time Digital Earth Program, and they have been endorsed by the IMA Working Group on Mineral Informatics.
