arXiv subjects

Jennifer L. M. Rupp

Publications and source records attributed to Jennifer L. M. Rupp.

3 recordsLinked to original sources

arXiv2026

Raman Signatures of Lithium Ion Dynamics in LLZO Garnet Electrolytes: Atomistic Insights from MD-Raman Calculations

Lithium lanthanum zirconate (LLZO) garnets are among the most promising solid electrolytes for next-generation batteries owing to their high ionic conductivity, chemical stability, and compatibility with lithium metal. Raman spectroscopy is commonly employed to distinguish the highly conductive cubic phase from the poorly conductive tetragonal phase of LLZO, yet the atomistic origin of these spectral differences and their direct connection to Li-ion transport remain unresolved. Here, we close this gap by comparing computed and experimental Raman spectra for the tetragonal, cubic, and Ta-doped variants of LLZO, with the computed spectra obtained from the MD-Raman approach that combines machine-learning molecular dynamics with first-principles polarizability calculations. We show that the contrasting ionic transport behavior across these LLZO variants is encoded in the vibrational dynamics of the lithium sublattice and gives rise to distinct features in their Raman spectra. A symmetry-resolved analysis further reveals that experimentally observed Raman peaks do not correspond to individual normal modes, but instead arise from overlapping contributions of multiple symmetry-allowed vibrations, challenging conventional peak-assignment approaches. By explicitly connecting experimentally accessible Raman signatures to the underlying atomic-scale dynamics, our results show how Raman spectroscopy can move beyond empirical phase identification toward a microscopic probe of Li-ion dynamics in lithium garnet electrolytes.

cond-mat.mtrl-sci↗

arXiv2025

Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials

Inorganic synthesis planning currently relies primarily on heuristic approaches or machine-learning models trained on limited datasets, which constrains its generality. We demonstrate that language models, without task-specific fine-tuning, can recall synthesis conditions. Off-the-shelf models, such as GPT-4.1, Gemini 2.0 Flash and Llama 4 Maverick, achieve a Top-1 precursor-prediction accuracy of up to 53.8 % and a Top-5 performance of 66.1 % on a held-out set of 1,000 reactions. They also predict calcination and sintering temperatures with mean absolute errors below 126 °C, matching specialized regression methods. Ensembling these language models further enhances predictive accuracy and reduces inference cost per prediction by up to 70 %. We subsequently employ language models to generate 28,548 synthetic reaction recipes, which we combine with literature-mined examples to pretrain a transformer-based model, SyntMTE. After fine-tuning on the combined dataset, SyntMTE reduces mean-absolute error in sintering temperature prediction to 73 °C and in calcination temperature to 98 °C. This strategy improves models by up to 8.7 % compared with baselines trained exclusively on experimental data. Finally, in a case study on Li7La3Zr2O12 solid-state electrolytes, we demonstrate that SyntMTE reproduces the experimentally observed dopant-dependent sintering trends. Our hybrid workflow enables scalable, data-efficient inorganic synthesis planning.

cond-mat.mtrl-sci↗

arXiv2025

Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning

Retrosynthesis strategically plans the synthesis of a chemical target compound from simpler, readily available precursor compounds. This process is critical for synthesizing novel inorganic materials, yet traditional methods in inorganic chemistry continue to rely on trial-and-error experimentation. Emerging machine-learning approaches struggle to generalize to entirely new reactions due to their reliance on known precursors, as they frame retrosynthesis as a multi-label classification task. To address these limitations, we propose Retro-Rank-In, a novel framework that reformulates the retrosynthesis problem by embedding target and precursor materials into a shared latent space and learning a pairwise ranker on a bipartite graph of inorganic compounds. We evaluate Retro-Rank-In's generalizability on challenging retrosynthesis dataset splits designed to mitigate data duplicates and overlaps. For instance, for Cr2AlB2, it correctly predicts the verified precursor pair CrB + Al despite never seeing them in training, a capability absent in prior work. Extensive experiments show that Retro-Rank-In sets a new state-of-the-art, particularly in out-of-distribution generalization and candidate set ranking, offering a powerful tool for accelerating inorganic material synthesis.

physics.chem-ph↗