Tasuku Sugiura | Searcharxiv

arXiv2026

Intrinsic Defect Energetics and Fluorine Doping Effects in Li2CO3 and Li2O2: A First-Principles Study

Lithium carbonate, Li2CO3, is a thermodynamically stable carbonate phase whose defect energetics are closely related to its stability and decomposition behavior in various lithium-based electrochemical systems. These properties of Li2CO3 are particularly important in lithium-oxygen battery environments. In these systems, Li2CO3 can form as a parasitic discharge product alongside Li2O2, the primary discharge product, leading to performance degradation. However, compared with Li2O2, the intrinsic defect thermodynamics of Li2CO3 and how chemical doping modifies its defect energetics remain insufficiently understood. In this study, first-principles calculations were performed to systematically analyze the intrinsic point-defect energetics of Li2CO3 and to evaluate the effects of fluorine doping on vacancy formation energies in Li2CO3 and Li2O2. Intrinsic defect analysis reveals that defect behavior is predominantly governed by lithium-related defects. Upon fluorine doping, lithium and carbon vacancy formation energies decrease selectively in Li2CO3, partially destabilizing the carbonate framework, while a reduction in lithium vacancy formation energy is also observed in Li2O2. These results suggest that fluorine doping modulates the defect energetics of both discharge products, potentially providing a thermodynamic basis for controlling the stability of Li2CO3 and Li2O2 under thermodynamic conditions representative of lithium-oxygen batteries.

cond-mat.mtrl-sci↗

arXiv2026

Achieving Robust Extrapolation in Materials Property Prediction via Decoupled Transfer Learning

Machine learning has revolutionized materials property prediction, yet fails catastrophically when extrapolating beyond training distributions-precisely the capability required for discovering unprecedented materials. Graph neural networks (GNNs) exhibit this collapse because end-to-end training fundamentally couples learned representations to target property distributions, preventing genuine extrapolation. We demonstrate that decoupled transfer learning-separating pretrained GNN feature extractors from simple regressors-overcomes this barrier. Pretrained features provide transferable structural knowledge, while simple regressors enable smooth extrapolation by maintaining learned trends beyond training boundaries. Benchmarked on layered intercalation compounds through four rigorous extrapolation scenarios and a temporal Materials Project split, our framework achieves 68% error reduction (RMSE: 0.881 vs. 2.778 eV/atom) versus end-to-end GNNs for extrapolation. Failure analysis reveals extrapolation succeeds for continuous chemical space but fails for discontinuous space, establishing clear design principles. Validated on Fermi energy prediction, this framework is immediately deployable using existing pretrained models, requiring no architectural innovations-transforming ML-driven materials discovery.

cond-mat.mtrl-sci↗