arXiv subjects

Kunik Jang

Publications and source records attributed to Kunik Jang.

3 recordsLinked to original sources

arXiv2026

Pressure-regulated mechanochemistry at lithium metal-sulfide electrolyte interfaces

Stack pressure is commonly treated as a means of maintaining physical contact in all-solid-state lithium-metal batteries, but it can also alter the chemistry of reactive solid-solid interfaces. Here, using pressure-aware, charge-resolved machine-learning molecular dynamics validated against DFT, we determine how pressure magnitude and loading geometry regulate interphase formation at Li||Li6PS5Cl interfaces. The response is nonmonotonic: compression at 1 kbar accelerates PS4 decomposition and Li2S-like ordering, whereas 10-100 kbar compression restricts structural rearrangement and long-range crystallization. Charge-resolved dynamics further identify sulfur-centered, lithium-rich early-interphase environments associated with subsequent Li2S-like ordering. Uniaxial loading accelerates interfacial reaction relative to isostatic loading at the same nominal pressure. Pressure also changes void closure and dead-lithium spreading in a defect-location-dependent manner. These results establish applied pressure as a mechanochemical process variable coupling interphase chemistry, ion transport and defect evolution, providing a mechanistic framework for interpreting pressure effects in sulfide solid-state batteries.

cond-mat.mtrl-sci↗

arXiv2026

Compiling Chemical Knowledge into Executable Descriptors for Materials Prediction

Materials prediction depends critically on how scientific knowledge is represented, yet many governing considerations exist only as natural-language heuristics that conventional learners cannot use. We introduce CRISP, a large language model-assisted framework that treats representation construction as a rule-space exploration and compilation problem: it repeatedly samples target-relevant chemical rules without access to structures, labels or data splits, consolidates related concepts, and compiles each into an executable scalar descriptor supplied to a conventional learner. For positive-unlabeled inorganic-crystal synthesizability, CRISP outperformed expert-curated and generic structural representations under a shared learner and surpassed purpose-built synthesizability models, with its advantage most pronounced under structural-size and chemical-family shifts. Infrequently generated rules contributed complementary predictive information, showing that generation frequency does not determine utility. The same workflow yielded competitive representations for formation energy and ionic conductivity while revealing task-dependent limits for shear modulus, establishing a dataset-blind, auditable route from broad chemical knowledge to transferable computational representations.

cond-mat.mtrl-sci↗

arXiv2026

Materealize: a multi-agent deliberation system for end-to-end material design and synthesis

We propose Materealize, a multi-agent system for end-to-end inorganic materials design and synthesis that orchestrates core domain tools spanning structure generation, property prediction, synthesizability prediction, and synthesis planning within a single unified framework. Through a natural-language interface, Materealize enables non-experts to access computational materials workflows and obtain experimentally actionable outputs for material realization. Materealize provides two complementary modes. In instant mode, the system rapidly composes connected tools to solve diverse inorganic tasks-including property-conditioned synthesizable candidate design with synthesis recipes, diagnosis, and redesign of unsynthesizable structures, and synthesizable data augmentation-within a few minutes. In thinking mode, Materealize applies multi-agent debate to deliver more refined and information-rich synthesis recommendations, including reasoning- and model-driven synthesis routes and mechanistic hypotheses. The mechanistic hypotheses are validated by direct comparison with the literature for known mechanisms and further supported by physics-grounded simulations for novel synthesis pathways. By combining tool-level accuracy with reasoning-level integration, Materealize can bridge the gap between computational discovery and practical experimental realization.

cond-mat.mtrl-sci↗