Machine Learning Identifies an Information-Sufficiency Regime for Critical-Mineral Recovery
Year: 2026
Type: preprint
Abstract: The recovery of critical minerals from spent lithium-ion batteries is governed by coupled feedstock chemistry, solvent composition, and processing conditions, yet heterogeneous experimental reporting makes it difficult to determine which information is essential for reliable prediction across heterogeneous experimental systems. Here, using deep eutectic solvent-based recovery as a representative resource recovery platform, we curate 814 experimental observations from 38 publications and develop an interpretable machine learning framework to identify an informationsufficiency regime. Five descriptors, including temperature, electrode material type, leaching time, target metal, and hydrogen-bond donor identity, retain 98.3% of the predictive performance of the full 13-descriptor model while reducing the descriptor space by 61.5%. Reducing the representation from five to four descriptors causes a pronounced performance loss, whereas adding a sixth provides negligible additional benefit. Descriptor removal and domain ablation analyses further show that temperature and target metal identity contain the least replaceable predictive information, while material identity, solvent chemistry, and processing conditions provide complementary information. These results identify a compact, chemically structured information core for critical-mineral recovery and provide a quantitative basis for prioritizing experimental measurements, improving cross-study data consistency, and developing more transferable datadriven frameworks for battery resource recovery.
Source: ChemRxiv
Authors: Tengxiao Liu, Ta-Wei Wang, Hongtao Sun
Institution: Pennsylvania State University
Language: English
Subfield: Metals and Alloys
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7222668128