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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium Circular Metallurgy: Design, Technology, Application
Presentation Title Predicting Mineral Phase Diagrams from First-Principles Modeling
Author(s) Brian Donovan, Alan C. West, Alexander Urban
On-Site Speaker (Planned) Alexander Urban
Abstract Scope Developing cleaner metallurgical processes requires establishing relationships between mineral phases and leaching products. Mineral stability is governed by coupled electronic, magnetic, and entropic effects that are challenging to characterize experimentally. Here, we present recent advances in first-principles modeling of transition-metal sulfides, which are central to critical metals such as copper and nickel. We show that empirically corrected density-functional theory enables accurate predictions of formation energies and phase equilibria across a broad range of sulfide systems, in close agreement with experiment, and finite-temperature effects are essential to reconcile zero-Kelvin predictions with observed mineralogy. Using iron sulfides as a model system, we construct temperature- and pressure-dependent phase diagrams that incorporate magnetic ordering, vibrational free energies, and configurational entropy, explaining the stability of pyrrhotite relative to troilite under geological conditions. This demonstrates predictive modeling of mineral equilibria under realistic conditions, with direct implications for sulfide leaching, separation, and refining.
Proceedings Inclusion? Undecided
Keywords Hydrometallurgy, Computational Materials Science & Engineering, Modeling and Simulation

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Additive Manufacturing of Metals: From Complex Alloys to Simple Alloys
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Controlling Iron-Bearing Intermetallics for Impurity-Tolerant Recycled 6xxx Aluminum Sheet Alloys
Direct Atmospheric Sulfuric Acid Leaching of Low Nickel Matte: A New Hydrometallurgical Route for Selective Metal Recovery
Electrolyte Design for Sustainable Ironmaking: Electrowinning of High-Purity Iron from Low-Grade Oxide Ores
Evaluation of Recycled Ti-6Al-4V Powder Feedstock for use in Additive Manufacturing using In-situ X-Ray Diffraction
Fusing In-Situ Monitoring Data with Physics-Based Simulations for Real-Time Process-to-Structure Prediction in Metal AM Using Conditional Generative AI Model
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Life Cycle Analysis-based Multi-Objective Optimization of H2-DRI charging in Scrap-Based EAF Steelmaking
Mechanism-Guided Upcycling of Downgraded Aluminum Scrap through Iron-Rich Intermetallic Trapping
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Predicting Melting Temperature of Molten Salt Electrolytes with Universal Machine Learning Interatomic Potentials
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Production of Low-Fe Aluminum Alloys through Efficient Iron Removal from Casting Scrap
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Textured Metals for Energy Storage: From Findings via Characterization to New Materials Design

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