arXiv subjects

Abhishek Tewari

Publications and source records attributed to Abhishek Tewari.

4 recordsLinked to original sources

arXiv2026

An Interfacial Balance Rule Governs Binder-Electrolyte Coupling in Lead-Free Perovskite Energy Storage

Electrode binders are conventionally regarded as inert structural components. Here, we show that in lead-free perovskite supercapacitors, the binder defines the optimal electrolyte composition. Across a factorial matrix of poly(vinylidene fluoride) (PVDF) loadings and LiTFSI concentrations in CsSnCl$_3$ electrodes, the capacitance optimum shifts systematically with binder content along a single linear relationship, described by the Interfacial Balance Rule ($λ+θ=1$), where $λ$ and $θ$ are the normalized lithium-supply and polymer contributions at the optimized interfacial state. The same relationship holds for hybrid MASnCl$_3$, showing that the optimum is governed by the polymer-electrolyte interface rather than the perovskite lattice chemistry. Simulations using a pre-trained MACE machine-learned interatomic potential show that PVDF adopts a planar configuration on CsSnCl$_3$ and simultaneously interacts with cationic and anionic sites. This configuration homogenizes lithium adsorption energetics, introduces fluorine-mediated coordination, and confines lithium to a two-dimensional interfacial region while preserving lateral mobility. Tuning polymer coverage through surface density and chain length reveals a finite interfacial lithium accommodation capacity that marks the onset of out-of-plane aggregation. The Interfacial Balance Rule provides a macroscopic descriptor of this finite interfacial resource, balancing polymer-mediated lithium stabilization against limited accommodation space. Binder loading is therefore an active design parameter for polymer-regulated energy-storage interfaces.

cond-mat.mtrl-sci↗

arXiv2026

Chemically Resolved Topological Coordinates Link Structural Dynamics and Configurational Thermodynamics

Atomic coordinates specify a structure, but they do not reveal how chemical connectivity across several length scales relates to atomic motion and configurational energy ordering. We formulate chemically directed persistent homology at four resolutions---complete networks, individual sites, spatial fields, and substitutional arrangements---while retaining the chemical identity and length scale of each connectivity feature. In \textit{ab initio} molecular dynamics (AIMD) trajectories of $δ$- and $γ$-CsPbI$_3$ at five temperatures spanning 500--700 K, the corner-sharing $γ$ network has a lower Pb--I restoring stiffness and permits larger iodide excursions, yet iodide positional correlations decay 2.27 times more slowly and Pb-network topology retains memory 1.63 times longer than in the edge-sharing $δ$ phase. Local softness and loss of network memory are therefore distinct. At individual sites, rare $γ$-phase Pb environments with a $δ$-like Cs-cage connectivity precede 0.17 $\mathring{\mathrm{A}}$ greater Pb displacement over the subsequent 0.5 ps. The same Pb-network coordinate resolves disruption of corner-sharing connectivity across a 2560-atom $δ|γ$ boundary. In substituted CsPbI$_3$, compact dopant arrangements undergo greater cooperative host relaxation and lie lower in density-functional-theory (DFT) energy than dispersed arrangements of the same composition. SchNet and Allegro model families with comparable energy errors encode opposite ordering along this coordinate, and 61 of 247 supplied models with errors below 1 meV atom$^{-1}$ on separate test structures reverse the DFT relation. Because relative configurational energies set Boltzmann populations, chemical network topology links structure to physical response and tests whether learned energy models preserve DFT configurational ordering even when their average errors are small.

cond-mat.mtrl-sci↗

arXiv2026

MatMMExtract: An Open-Source Pipeline for Panel-Level Extraction of Grounded Image-Text Pairs from Materials Science Literature

The materials science literature encodes decades of experimental knowledge in figures, yet this visual record remains locked away and inaccessible to AI at scale. The core difficulty is structural: most scientific figures are compound, with a single caption describing multiple sub-panels simultaneously, making direct image-text pairing unreliable. We present MatMMExtract, an end-to-end open-source pipeline that resolves this by decomposing compound figures into individual sub-panels and generating structured, grounded annotations using a large language model guided by a curated materials science taxonomy. Applied to 14,810 open-access articles, MatMMExtract produces MatSciFig; 391,606 panel-level image-text pairs from 180,571 figures, each annotated with a sub-caption, a two-level visualisation category spanning 19 classes and over 100 subtypes, and a scientific summary. To enable accurate panel localisation, we introduce MaterialScope, a domain-specific detection dataset of 2,811 manually annotated materials science figures, on which a fine-tuned YOLO12-m detector achieves mAP_50 of 0.9227. Among six benchmarked language models, Gemini 3.1 Flash Lite delivers the best cost-quality trade-off for annotation generation, with 82% of outputs rated good and a hallucination rate of 4.8%. A dual-encoder retrieval baseline on MatSciFig achieves a 4.4 times improvement in R@1 over zero-shot CLIP, demonstrating the dataset's immediate utility for vision-language learning. All resources are released openly to the community.

cs.CV↗

arXiv2025

Automated Extraction of Material Properties using LLM-based AI Agents

The rapid discovery of materials is constrained by the lack of large, machine-readable datasets that couple performance metrics with structural context. Existing databases are either small, manually curated, or biased toward first principles results, leaving experimental literature underexploited. We present an agentic, large language model (LLM)-driven workflow that autonomously extracts thermoelectric and structural-properties from about 10,000 full-text scientific articles. The pipeline integrates dynamic token allocation, zeroshot multi-agent extraction, and conditional table parsing to balance accuracy against computational cost. Benchmarking on 50 curated papers shows that GPT-4.1 achieves the highest accuracy (F1 = 0.91 for thermoelectric properties and 0.82 for structural fields), while GPT-4.1 Mini delivers nearly comparable performance (F1 = 0.89 and 0.81) at a fraction of the cost, enabling practical large scale deployment. Applying this workflow, we curated 27,822 temperature resolved property records with normalized units, spanning figure of merit (ZT), Seebeck coefficient, conductivity, resistivity, power factor, and thermal conductivity, together with structural attributes such as crystal class, space group, and doping strategy. Dataset analysis reproduces known thermoelectric trends, such as the superior performance of alloys over oxides and the advantage of p-type doping, while also surfacing broader structure-property correlations. To facilitate community access, we release an interactive web explorer with semantic filters, numeric queries, and CSV export. This study delivers the largest LLM-curated thermoelectric dataset to date, provides a reproducible and cost-profiled extraction pipeline, and establishes a foundation for scalable, data-driven materials discovery beyond thermoelectrics.

cs.LG↗