jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jinyoung Park | Minseong Bae | Jeehye Na | Hyunwoo J. Kim
Source: arXiv (Cornell University) (preprint, 2026-01-18)
Field: Materials Science — Materials Chemistry

Improving Large Molecular Language Model via Relation-aware Multimodal Collaboration Large language models (LLMs) have demonstrated their instruction-following capabilities and achieved powerful performance on various tasks. Inspired by their success, recent works in the molecular domain have led to the development of large molecular language models (LMLMs) that integrate 1D molecular strings or 2D molecular graphs into the language models. However, existing LMLMs often suffer from hallucination and limited robustness, largely due to inadequate integration of diverse molecular modalities such as 1D sequences, 2D molecular graphs, and 3D conformations. To address these limitations, we propose CoLLaMo, a large language model-based molecular assistant equipped with a multi-level molecular modality-collaborative projector. The relation-aware modality-collaborative attention mechanism in the projector facilitates fine-grained and relation-guided information exchange between atoms by incorporating 2D structural and 3D spatial relations. Furthermore, we present a molecule-centric new automatic measurement, including a hallucination assessment metric and GPT-based caption quality evaluation to address the limitations of token-based generic evaluation metrics (i.e., BLEU) widely used in assessing molecular comprehension of LMLMs. Our extensive experiments demonstrate that our CoLLaMo enhances the molecular modality generalization capabilities of LMLMs, achieving the best performance on multiple tasks, including molecule captioning, computed property QA, descriptive property QA, motif counting, and IUPAC name prediction. Language model ; Property (philosophy) ; Domain (mathematical analysis) ; Bridging (networking) ; Molecular graph ; Natural language ; Generalization ; Molecular imaging Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Zhiwen Wang | Jagadish Kumar Galivarapu | Lisha Gu | Hengzhi Qiu | K. Manjunatha | Ke Wang
Source: Surfaces and Interfaces (article, 2026-03-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Anisotropic magnetocaloric effect in GdFeCo thin films Magnetic refrigeration ; Thin film ; Anisotropy ; Magnetization ; Magnetic anisotropy Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials ; Shape Memory Alloy Transformations
Authors: Wanhao Liu | Weida Wang | Jiaqing Xie | Shunkun Yang | Jue Wang | Benteng Chen | Guangtao Mei | Zonglin Yang | Shufei Zhang | Yuchun Mo | Lang Cheng | Jin Zeng | Houqiang Li | Wanli Ouyang | Yuqiang Li
Source: arXiv (Cornell University) (preprint, 2026-04-03)
Field: Materials Science — Materials Chemistry

PolyReal: A Benchmark for Real-World Polymer Science Workflows Multimodal Large Language Models (MLLMs) excel in general domains but struggle with complex, real-world science. We posit that polymer science, an interdisciplinary field spanning chemistry, physics, biology, and engineering, is an ideal high-stakes testbed due to its diverse multimodal data. Yet, existing benchmarks related to polymer science largely overlook real-world workflows, limiting their practical utility and failing to systematically evaluate MLLMs across the full, practice-grounded lifecycle of experimentation. We introduce PolyReal, a novel multimodal benchmark grounded in real-world scientific practices to evaluate MLLMs on the full lifecycle of polymer experimentation. It covers five critical capabilities: (1) foundational knowledge application; (2) lab safety analysis; (3) experiment mechanism reasoning; (4) raw data extraction; and (5) performance &amp; application exploration. Our evaluation of leading MLLMs on PolyReal reveals a capability imbalance. While models perform well on knowledge-intensive reasoning (e.g., Experiment Mechanism Reasoning), they drop sharply on practice-based tasks (e.g., Lab Safety Analysis and Raw Data Extraction). This exposes a severe gap between abstract scientific knowledge and its practical, context-dependent application, showing that these real-world tasks remain challenging for MLLMs. Thus, PolyReal helps address this evaluation gap and provides a practical benchmark for assessing AI systems in real-world scientific workflows. Benchmark (surveying) ; Workflow ; Testbed ; Field (mathematics) ; Limiting ; Data-driven ; Raw data Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Scientific Computing and Data Management
Authors: Yu‐Jin Kong | Hong-Ren Li | Y Tian | Yubing Si | Xi-Yan Dong | Fangfang Pan | Jia‐Hong Huang | Shuang-Quan Zang
Source: Journal of the American Chemical Society (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Sequential Vertex-Specific Modulation of Enantiopure Metal Nanoclusters Controlled modulation of the specific crystal surfaces is vital in nanotechnology but remains challenging to implement. Here, we achieve sequential vertex modulation in atomically precise homochiral silver nanoclusters using a scalable crystallization synthesis. By tuning the reductant conditions, we generate mixed-phase cocrystallized <i>R</i>/<i>S</i><b>-Ag</b><sub><b>19</b></sub><b>·Ag</b><sub><b>20</b></sub> comprising enantiopure <i>R</i>/<i>S</i>-<b>Ag</b><sub><b>19</b></sub> and <i>R</i>/<i>S</i>-<b>Ag</b><sub><b>20</b></sub>, featuring the first icosahedral Ag<sub>13</sub> core with 6 electrons. Using kinetic and thermodynamic control, we sequentially add Ag atoms to the cubic facets, progressing from the Ag<sub>6</sub> shell in <i>R</i>/<i>S</i>-<b>Ag</b><sub><b>19</b></sub> (two missing vertices) to the Ag<sub>7</sub> shell in <i>R</i>/<i>S</i>-<b>Ag</b><sub><b>20</b></sub> (one missing vertex), and ultimately to the closed <b>Ag</b><sub><b>8</b></sub> shell in <i>R</i>/<i>S</i>-<b>Ag</b><sub><b>21</b></sub>, and luminescence is induced. This geometric evolution is reminiscent of the <i>arachno</i> → <i>nido</i> → <i>closo</i> transformations in borane clusters, where vertex recovery leads to increasingly close polyhedral frameworks. Shell closure results in 2e filling, symmetry adaptation and chirality mutation. Additionally, <i>R</i>/<i>S</i>-<b>Ag</b><sub><b>21</b></sub> enables quantitative chiral sensing of amino acids. This work advances nanosynthesis by providing a framework for controlling surface modulation and fine-tuning the nanocluster properties and can be used to facilitate the development of improved functional (chiral) nanomaterials. Nanoclusters ; Icosahedral symmetry ; Enantiopure drug ; Chirality (physics) ; Shell (structure) ; Crystallization ; Luminescence ; Metal ; Borane Nanocluster Synthesis and Applications ; Quasicrystal Structures and Properties ; Surface Chemistry and Catalysis
Authors: Wanhao Liu | Weida Wang | Jiaqing Xie | Shunkun Yang | Jue Wang | Benteng Chen | Guangtao Mei | Zonglin Yang | Shufei Zhang | Yuchun Mo | Lang Cheng | Jin Zeng | Houqiang Li | Wanli Ouyang | Yuqiang Li
Source: ArXiv.org (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

PolyReal: A Benchmark for Real-World Polymer Science Workflows Multimodal Large Language Models (MLLMs) excel in general domains but struggle with complex, real-world science. We posit that polymer science, an interdisciplinary field spanning chemistry, physics, biology, and engineering, is an ideal high-stakes testbed due to its diverse multimodal data. Yet, existing benchmarks related to polymer science largely overlook real-world workflows, limiting their practical utility and failing to systematically evaluate MLLMs across the full, practice-grounded lifecycle of experimentation. We introduce PolyReal, a novel multimodal benchmark grounded in real-world scientific practices to evaluate MLLMs on the full lifecycle of polymer experimentation. It covers five critical capabilities: (1) foundational knowledge application; (2) lab safety analysis; (3) experiment mechanism reasoning; (4) raw data extraction; and (5) performance & application exploration. Our evaluation of leading MLLMs on PolyReal reveals a capability imbalance. While models perform well on knowledge-intensive reasoning (e.g., Experiment Mechanism Reasoning), they drop sharply on practice-based tasks (e.g., Lab Safety Analysis and Raw Data Extraction). This exposes a severe gap between abstract scientific knowledge and its practical, context-dependent application, showing that these real-world tasks remain challenging for MLLMs. Thus, PolyReal helps address this evaluation gap and provides a practical benchmark for assessing AI systems in real-world scientific workflows. Benchmark (surveying) ; Workflow ; Testbed ; Field (mathematics) ; Limiting ; Data-driven ; Raw data Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Scientific Computing and Data Management
Authors: Lauren Gilman | Hui Wang | Nick Birse | Louise Manning
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

SSTLNetwork: A Self-supervised Spectral Reconstruction Network with Hybrid Attention for Near-infrared Spectral Transfer Learning Transfer of learning ; Pattern recognition (psychology) ; Masking (illustration) ; Signal reconstruction ; Artificial neural network ; Process (computing) ; Supervised learning ; Domain (mathematical analysis) Spectroscopy and Chemometric Analyses ; Melamine detection and toxicity ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xue Ma | Yuan Wang | Jinhui He | Jingdong Gou | Yan Zhao | Akang Liu | Shuo Feng | Louwei Cui | H. Chen | Jun Yang | Liuyi Pan | Dong Sheng Li
Source: Fuel (article, 2026-01-13)
Field: Chemistry — Inorganic Chemistry

Synergistic pore-acidity engineering in hierarchical H-beta zeolites for molecular sieving of ethylcyclohexane Adsorption ; Steric effects ; van der Waals force ; Selectivity ; Desorption ; Kinetics ; Zeolite Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis for Biomass Conversion
Authors: Sidhartha Malo | Kumaresh Das | Indrajit Das
Source: The Journal of Organic Chemistry (article, 2026-02-12)
Field: Chemistry — Organic Chemistry

Solvent- and Electrolyte-Dependent Highly Selective Electrochemical Transfer Hydrogenation of Pyrazolo[1,5- <i>a</i> ]pyrimidines Electrochemical transfer hydrogenation using non-H<sub>2</sub> hydrogen sources, such as H<sub>2</sub>O, demonstrates excellent selectivity and supports an environmentally friendly and sustainable process. This method offers a promising alternative to traditional hydrogenation techniques that utilize alkali metal hydrides or molecular hydrogen gas. In this work, we present, for the first time, electrochemical protocols for the selective transfer hydrogenation of polynitrogen-containing aromatic heterocycles. These protocols can be fine-tuned by adjusting the solvent and supporting electrolyte. For instance, using a mixture of MeCN and H<sub>2</sub>O, along with NH<sub>4</sub>Cl as an electrolyte, facilitates the regioselective transfer hydrogenation of the C6-C7 double bond in pyrazolo[1,5-<i>a</i>]pyrimidines at 30 °C. Conversely, employing DMSO combined with H<sub>2</sub>O and <sup><i>n</i></sup>Bu<sub>4</sub>NBF<sub>4</sub> allows for the reduction of both the C6-C7 double bond and the N4-C5 imine bond. Importantly, the unsaturated C-C and C-N bonds within the pyrazole ring of pyrazolo[1,5-<i>a</i>]pyrimidines remain intact during these transfer hydrogenation reactions. The observed selectivities are influenced by two main factors: (a) the stronger reducing ability of DMSO compared to MeCN, and (b) the more robust interaction between ammonium cations (from NH<sub>4</sub>Cl) and the nitrogen's electron lone pair in pyrazolo[1,5-<i>a</i>]pyrimidines, which differs from the interaction with tetrabutylammonium cations (from <sup><i>n</i></sup>Bu<sub>4</sub>NBF<sub>4</sub>). Transfer hydrogenation ; Electrochemistry ; Selectivity ; Lone pair ; Double bond ; Electron transfer ; Regioselectivity ; Imine ; Hydrogen bond Radical Photochemical Reactions ; Asymmetric Hydrogenation and Catalysis ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: Marwa Chaabéne | Soumaya Agren | Mohamed Hassen V. Baouab | Rafik Ben Chaâbane
Source: Journal of Molecular Liquids (article, 2026-01-22)
Field: Chemistry — Spectroscopy

Decoding the methyl substitution effect: a combined experimental, DFT and docking strategy for optimizing Schiff base structures Schiff base ; Substituent ; Titration ; Ligand (biochemistry) ; Selectivity ; Docking (animal) ; Transition metal ; Metal Molecular Sensors and Ion Detection ; Metal complexes synthesis and properties ; Lanthanide and Transition Metal Complexes
Authors: Jacopo Perego | Charl X. Bezuidenhout | Silvia Bracco | Bai-Qiao Song | Mohana Shivanna | Soumya Mukherjee | Michael J. Zaworotko | Angiolina Comotti
Source: Journal of the American Chemical Society (article, 2026-02-17)
Field: Chemistry — Inorganic Chemistry

Coordination-Isomerism-Driven Threshold Pressure Control of CO <sub>2</sub> and C <sub>2</sub> H <sub>2</sub> in Mixed-Ligand Switching Metal−Organic Frameworks Flexible metal-organic frameworks (MOFs) promise access to superior control and enhanced performance for gas separation, storage, and release. For the development of smart materials with "on-demand" responsiveness, the <i>ad hoc</i> design of switching mechanisms is crucial to drive multiple structural transformations. In this study, doubly interpenetrated pillared Zinc(II)-MOFs were customized to fine-tune CO<sub>2</sub> and C<sub>2</sub>H<sub>2</sub> sorption/desorption pressures by modulating their dynamic responses. The incorporation of a specifically designed asymmetric bipyridyl-acrylonitrile pillar, together with benzenedicarboxylate and/or amino-benzenedicarboxylate ligands, leverages reversible metal-coordination isomerism to control gas-stimuli responsiveness. The sophisticated mechanism and temporal response involve framework expansion, node rearrangement, and ligand displacement. Furthermore, evolution of the global dynamics of the framework under cyclical CO<sub>2</sub> stimuli was revealed, demonstrating a progression from switching behavior to a permanently open structure. The pore-opening threshold pressure for CO<sub>2</sub> and acetylene (C<sub>2</sub>H<sub>2</sub>)─from discrete pockets to one-dimensional and two-dimensional interconnected channels─was rationally governed by increasing the amine group content, which modulates the density of hydrogen bonds between the two interpenetrated frameworks. This intricate mechanism was elucidated through <i>in situ</i> PXRD under CO<sub>2</sub> atmosphere, calorimetry-coupled gas adsorption, and density functional theory calculations. Notably, C<sub>2</sub>H<sub>2</sub>-induced gate opening expands the feasibility of C<sub>2</sub>H<sub>2</sub>-responsive systems, offering safer and more efficient sorbents. Density functional theory ; Mechanism (biology) ; Node (physics) ; Acetylene ; Hydrogen bond ; Hydrogen ; Ligand (biochemistry) ; Molecular switch Metal-Organic Frameworks: Synthesis and Applications ; Catalysts for Methane Reforming ; Carbon dioxide utilization in catalysis
Authors: Mirza Samad Ahmed Baig | Syeda Anshrah Gillani
Source: Open MIND (preprint, 2026-03-06)
Field: Materials Science — Materials Chemistry

Symmetry-Constrained Language-Guided Program Synthesis for Discovering Governing Equations from Noisy and Partial Observations Discovering compact governing equations from experimental observations is one of the defining objectives of quantitative science, yet practical discovery pipelines routinely fail when measurements are noisy, relevant state variables are unobserved, or multiple symbolic structures explain the data equally well within statistical uncertainty. Here we introduce SymLang (Symmetry-constrained Language-guided equation discovery), a unified framework that brings together three previously separate ideas: (i) typed symmetry-constrained grammars that encode dimensional analysis, group-theoretic invariance, and parity constraints as hard production rules, eliminating on average 71.3% of candidate expression trees before any fitting; (ii) language-model-guided program synthesis in which a fine-tuned 7B-parameter proposer, conditioned on interpretable data descriptors, efficiently navigates the constrained search space; and (iii) MDL-regularized Bayesian model selection coupled with block-bootstrap stability analysis that quantifies structural uncertainty rather than committing to a single best equation. Across 133 dynamical systems spanning classical mechanics, electrodynamics, thermodynamics, population dynamics, and nonlinear oscillators, SymLang achieves an exact structural recovery rate of 83.7% under 10% observational noise - a 22.4 percentage-point improvement over the next-best baseline - while reducing out-of-distribution extrapolation error by 61% and near-eliminating conservation-law violations (3.1 x 10-3 vs. 187.3 x 10-3 physical drift for the closest competitor). In all tested regimes the framework correctly identifies structural degeneracy, reporting it explicitly rather than returning a confidently wrong single equation. The framework is fully open-source and reproducible, providing a principled pathway from raw data to interpretable, physically auditable symbolic laws. ENCODE ; Extrapolation ; Bayesian probability ; Stability (learning theory) ; Nonlinear system ; Population ; Probabilistic logic ; Noise (video) ; Ambiguity Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Scientific Computing and Data Management
Authors: Masashi Ishii | Yasufumi Watanabe | Ippei Suzuki | Yukiko K. Takahashi
Source: Science and Technology of Advanced Materials Methods (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Comparison of FePt high-density magnetic recording media development between large language models and experimental experts: do LLMs recommend fluoride as a segregant? We investigated segregant development for FePt high-density magnetic recording media using large language models (LLMs) and identified new fluoride-based materials. By increasing the temperature of the softmax function to select probable vocabularies, we successfully incorporated the prior knowledge involved in LLM training into the material selection process (exploratory generation) and identified LaF3 as an optimal material. Meanwhile, we independently conducted sputtering deposition of FePt-LaF3 nanogranular samples and verified whether the LLM could reproduce the results. The inhomogeneity in the surface chemical composition of FePt-LaF3 in the non-equilibrium state of sputtering were also reproduced by the LLM, leading to the identification of AlF3 as an alternative segregant. Structures and magnetic properties strongly dependent on the sputtering apparatus cannot be reproduced by simple LLM predictions, necessitating interactive data exchange between physical and cyber experiments. However, phenomena that can occur in physical experiments are almost accurately reproduced by LLM, demonstrating the usefulness of LLM predictions in material development. LLMs that generate responses from the most likely vocabulary tend to select well-known materials. To identify unknown materials as candidates, techniques such as exploratory generation or inputting unexpected results are required. Vocabulary ; Softmax function ; Sputtering ; Process (computing) ; Deposition (geology) ; Identification (biology) Machine Learning in Materials Science ; Magnetic properties of thin films ; Inorganic Fluorides and Related Compounds
Authors: Zhuoyu Wu | Wenhui Ou | Pei-Sze Tan | Wenqi Fang | Sailaja Rajanala | Raphaël C. -W. Phan
Source: arXiv (Cornell University) (preprint, 2026-04-13)
Field: Materials Science — Materials Chemistry

RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering Retrieving procedure-oriented evidence from materials science papers is difficult because key synthesis details are often scattered across long, context-heavy documents and are not well captured by paragraph-only dense retrieval. We present RECIPER, a dual-view retrieval pipeline that indexes both paragraph-level context and compact large language model-extracted procedural summaries, then combines the two candidate streams with lightweight lexical reranking. Across four dense retrieval backbones, RECIPER consistently improves early-rank retrieval over paragraph-only dense retrieval, achieving average gains of +3.73 in Recall@1, +2.85 in nDCG@10, and +3.13 in MRR. With BGE-large-en-v1.5, it reaches 86.82%, 97.07%, and 97.85% on Recall@1, Recall@5, and Recall@10, respectively. We further observe improved downstream question answering under automatic metrics, suggesting that procedural summaries can serve as a useful complementary retrieval signal for procedure-oriented materials question answering. Code and data are available at https://github.com/ReaganWu/RECIPER. Pipeline (software) ; Question answering ; Context (archaeology) ; Key (lock) ; Document retrieval ; Data retrieval ; Code (set theory) Machine Learning in Materials Science ; Topic Modeling ; Information Retrieval and Search Behavior
Authors: WILLIAM OLIVER RUBIN
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-01-09)
Field: Chemistry — Physical and Theoretical Chemistry

The Universal Tension-Driven Lattice (UTDL): Unification of Gravity, Matter, and Quantum Topology via Dynamic Harmonic Resonance The unification of General Relativity and Quantum Mechanics remains the defining challenge of modern physics. This paper proposes a resolution via the Universal Tension-Driven Lattice (UTDL), a constructivist framework that models spacetime as a discrete, high-tension, non-linear elastic solid operating at the Planck scale. By postulating a single constitutive force law F = −k0 x − α x3, we derive the mechanical origins of 33 fundamental physical phenomena without fine-tuning. These include: The speed of light c Mass-energy equivalence E=mc2 Lorentz invariance restoration Refractive Gravitational Lensing Dark Matter rotation curves Black Hole Entropy Dark Energy The GZK Cutoff The Higgs Mechanism Zitterbewegung Fermionic statistics Chirality U(1) Gauge fields The Weak Interaction Superconductivity Fast Radio Bursts The Fine Structure Constant The Atomic Mass Spectrum The Pythagorean Lattice (Particle Generations) Dynamic Harmonic Resonance Relativistic Orbital Precession Thermodynamic Phase Transitions Color as Topology Universal Conductivity Thermal Coherence Pressure-Induced Insulators Biological Geometric Resonance The Sound Barrier The Water Density Anomaly Pressure Melting Inversion Anomalous Specific Heat Surface Tension as Vacuum Stress Biological Temperature (37°C) We validate the model by identifying the Fundamental Strain Quantum (Eλ ≈ 20.93 MeV) and deriving the masses of the Standard Model fermions with <0.1% error. Crucially, we present computational evidence identifying the Vacuum Stability Criterion (α < 0), proving that matter is the result of vacuum self-focusing, and extend the model to macroscopic regimes, including the Hydro-Geometric Suite, proving that the thermodynamic anomalies of water are not chemical accidents but scale-invariant mechanical consequences of the N=4 Open Topology. Lorentz covariance ; Dark matter ; Dark energy ; QED vacuum ; Mass gap ; Gravitation ; General relativity ; Vacuum energy Advanced Physical and Chemical Molecular Interactions ; Quantum Electrodynamics and Casimir Effect ; Advanced Materials and Mechanics