arXiv subjects

Eric Jianfeng Cheng

Publications and source records attributed to Eric Jianfeng Cheng.

5 recordsLinked to original sources

arXiv2026

Solid and Quasi-Solid Electrolytes for Zinc Batteries: Balancing Water Activity, Ion Transport, and Interfaces

Zinc batteries offer a compelling route to safe and low-cost energy storage, yet their reliance on aqueous electrolytes promotes hydrogen evolution, corrosion, cathode dissolution, and non-uniform zinc deposition. Replacing the liquid with a solid or quasi-solid electrolyte can suppress these processes, but it also removes the medium that enables rapid Zn2+ transport and conformal electrode contact. This tension, the price of removing water, has been obscured by inconsistent use of the term solid state and by comparisons based largely on bulk ionic conductivity. Here we critically examine zinc electrolytes across a continuum from water-rich hydrogels to dry polymers, solvated crystals, and inorganic conductors. We distinguish water content from thermodynamic water activity and classify these materials according to phase state, mobile-solvent fraction, and dominant transport mechanism. We show that neither high conductivity nor nominally water-free composition reliably predicts cell performance: electrolyte thickness, Zn2+ transference, interfacial resistance, and evolving contact often determine the practical outcome. Controlled-solvation and hybrid electrolytes therefore provide the most credible near-term path, whereas genuinely solvent-free Zn2+ conductors remain a longer-term scientific target. Progress will require transparent reporting of solvent state and validation using thin electrolytes, realistic electrode loadings, limited zinc excess, and calendar-life testing.

cond-mat.mtrl-sci↗

arXiv2026

Why Ammoniated Lithium Borohydrides Liquefy and Resolidify?

Ammonia ($\mathrm{NH_3}$) absorption drives $\mathrm{LiBH_4\!\cdot\!xNH_3}$ through a re-entrant ``solid--liquid--solid'' transition: $\mathrm{LiBH_4\!\cdot\!NH_3}$ is a well-defined solid ammoniate, compositions near $\mathrm{LiBH_4\!\cdot\!2NH_3}$ are liquid-like or partially liquefied, whereas $\mathrm{LiBH_4\!\cdot\!3NH_3}$ returns to a more rigid non-liquid ammoniate state. However, the microscopic origin of this unintuitive response remains a long-lasting mystery. Here, we uncover its mechanism. Cross-database analysis identifies borohydrides as a particularly state-diverse and composition-responsive material family. Structure prediction and ab initio molecular simulations reveal that $\mathrm{NH_3}$ progressively replaces $\mathrm{BH_4^-}$ in the Li coordination shell. The liquid-like state emerges not at the highest $\mathrm{NH_3}$ loading but near $x\approx2$, where Li--N and Li--B coordination modes are strongly mixed, coordination memory is weakest, and the sampled Li--N/N$\cdots$B coordination landscape is broadest. Further ammoniation produces Li--N-dominant coordination and slows $\mathrm{BH_4^-}/\mathrm{NH_3}$ contact renewal, with the resulting increase in network persistence and accompanying recovery of a rigid ammoniate state. Pressure--composition isotherm, $^{1}\mathrm{H}$ and $^{11}\mathrm{B}$ nuclear magnetic resonance, and Raman measurements support this non-monotonic state evolution and associated $\mathrm{BH_4^-}/\mathrm{NH_3}$ reorganization. These findings transform ammonia-induced liquefaction from an empirical phase anomaly into a competition between native-network disruption, mixed-coordination frustration, and ligand-built network reconstruction, providing a framework for chemically switching between transport-favouring fluidity and stability-favouring rigidity in hydrogen-rich materials.

cond-mat.mtrl-sci↗

arXiv2026

When Literature Data Mislead Artificial Intelligence in Materials Discovery

Artificial intelligence (AI) increasingly treats scientific literature as a data source for building databases, training predictive models, and guiding discovery. Yet literature-derived datasets often assume that reported experimental values are internally consistent and directly reusable. Here, we analyze this assumption using solid electrolyte (SE) conductivity data as a representative materials-science case. By tracing values from source articles to curated datasets, we identify recurrent text-figure mismatches, ambiguous axis annotations, unit inconsistencies, and missing measurement context. These discrepancies are often numerically plausible and therefore difficult to detect through routine preprocessing, but they can propagate as structured label noise during database construction and machine-learning reuse. A cross-database example shows how ambiguous reporting can create a 100-fold conductivity error. Our analysis reframes data accuracy as an infrastructure requirement for artificial-intelligence-driven discovery and motivates traceable reporting, curation, and validation practices for reusable scientific data. Keywords: AI for science; Data reliability; Scientific databases; Structured label noise; Literature-derived data; Materials informatics; Solid electrolytes

cs.IR↗

arXiv2026

Breaking Bottlenecks in Solid Electrolyte Discovery with Large Artificial Intelligence Models

Solid electrolytes (SEs) are central to next-generation metal batteries, yet their discovery remains constrained by fragmented data, limited transferability of simulations, and slow experimental iteration. Unlike catalysis, where surface reactivity dominates, SEs require simultaneous optimization of bulk ion transport, defect chemistry, mechanical integrity, and interfacial stability. Here, we outline a framework for autonomous SE discovery enabled by large artificial intelligence (AI) models, including machine learning interatomic potentials (MLIPs) and large language models (LLMs). We discuss the evolution from static materials databases to dynamic, self-updating knowledge systems, the role of MLIPs in bridging density functional theory (DFT) and long-timescale ion migration, and the emergence of LLMs as engines for literature mining, hypothesis generation, and scientific reasoning. We further describe a closed-loop architecture integrating AI-driven candidate design, multiscale simulation, uncertainty-aware selection, and experimental validation. Such systems shift SE research from intuition-guided exploration to data-informed, self-improving cycles. We conclude by highlighting challenges in data standardization, interfacial complexity, and reproducibility, and we propose design principles for building autonomous laboratories for solid-state battery materials.

cond-mat.mtrl-sci↗

arXiv2026

Building a physics-aware AI ecosystem for solid-state hydrogen storage materials

Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evolution of hydrogen storage materials (HSMs). Although artificial intelligence (AI) has accelerated materials discovery, current approaches remain constrained by fragmented data, limited physical consistency, and weak integration with experimental validation. Here, we propose a unified framework that integrates coherent data infrastructure, physics-grounded modeling, and AI-driven inverse design within a closed-loop discovery paradigm. By embedding physical constraints and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs.

cond-mat.mtrl-sci↗