jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shengnan Zou | Yulin Xu | Zhihong Wang | Xiangan Song | Jingsheng Miao | Chuluo YANG | Yong Zhang
Source: Advanced Optical Materials (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Rigidification with B‐π‐N Scaffolds Through Four Bridging Atoms Toward High Efficiency and Color‐Tunable Electroluminescence Abstract Boron‐based emitters exhibiting thermally activated delayed fluorescence (TADF) nature have emerged as promising guests for organic light‐emitting diodes (OLEDs). However, balancing the trade‐off between the small singlet‐triplet energy gap (Δ E ST ) and the high oscillator strength ( f ) remains a significant challenge. This study demonstrates a rational molecular design strategy for high‐performance TADF emitters by integrating BOO/BOS/BSS acceptors and a benzo[5,6][1,4]oxazino[2,3,4‐kl]phenoxazine (NO) donor into a rigid framework. Systematic modulation of bridging atoms and peripheral tert‐butyl functionalization fine‐tuned the excited‐state properties, yielding broad emission color tenability from blue‐green (498 nm) to orange (572 nm). Theoretical calculations reveal effective spatial separation of frontier molecular orbitals with maintained overlap on the central benzene ring, balancing a small Δ E ST (0.07–0.23 eV) and a high ƒ (0.1728–0.1911). The introduction of tert‐butyl groups increases the horizontal dipole ratio from 61% in BNO4 to 77–78% in the other three derivatives. Among the designed emitters, TBNSO3 exhibits the smallest Δ E ST (0.07 eV) due to symmetry‐breaking charge transfer, facilitating efficient reverse intersystem crossing. The corresponding OLED achieves a maximum external quantum efficiency (EQE max ) of 35.1%, representing state‐of‐the‐art performance among non‐sensitized boron‐based devices. Quantum efficiency ; OLED ; Electroluminescence ; Bridging (networking) ; Dipole ; Intersystem crossing ; Diode Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Organoboron and organosilicon chemistry
Authors: Afrin A | CHINNA AYYA SWAMY P
Source: The Journal of Organic Chemistry (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Diketone Acceptor Modulation as a Structural Design Strategy for Tunable Carbazole–Cyanostilbene Emitters A series of carbazole-cyanostilbene luminophores incorporating four diketone-based acceptors (naphthalimide, anthraquinone, phenanthraquinone, and naphthoquinone) were developed to explore the effect of acceptor tuning on emission color and solid-state behavior. Among them, the naphthalimide (<b>NPCZCS</b>), anthraquinone (<b>AQCZCS</b>), and naphthoquinone (<b>NQCZCS</b>) derivatives exhibited distinct solvatochromic shifts, revealing strong charge-transfer characteristics in the excited state, while the phenanthraquinone analogue (<b>PQCZCS</b>) showed nearly solvent-independent emission on varying polarities of solvents due to its localized excited state. Aggregation studies revealed that the naphthalimide and anthraquinone derivatives exhibit a nonclassical AIE behavior, with emission initially increasing upon aggregation and then decreasing at higher fractions, whereas the other two compounds remained nonemissive. In the solid state, the naphthalimide and anthraquinone derivatives displayed bright green (522 nm, ϕ = 24%) and orangish red (610 nm, ϕ = 2.7%) emissions, respectively, accompanied by irreversible mechanofluorochromic responses with clear red shifts upon grinding, as supported by PXRD studies. The high decomposition temperatures (>360 °C) confirmed their excellent thermal robustness. Density functional theory (DFT) and time-dependent DFT analyses supported the experimental findings. Altogether, this study demonstrates how fine-tuning the electronic nature of diketone acceptors in a carbazole-cyanostilbene scaffold can generate color-tunable, thermally stable, and stimuli-responsive materials suitable for next-generation emissive applications. Solvatochromism ; Excited state ; Anthraquinone ; Density functional theory ; Acceptor ; Time-dependent density functional theory ; Thermal decomposition ; Chromophore Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Organic Electronics and Photovoltaics
Authors: Jiaheng Zhao | Huichun Kang | Yue Li | Qiwei Feng | Hui Peng | Ziqiang Lei
Source: Journal of Electroanalytical Chemistry (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

A study on a local structural regulation strategy for covalent triazine frameworks and their electrocatalytic oxygen reduction performance Tafel equation ; Triazine ; Covalent bond ; Pyridine ; Covalent organic framework ; Electrochemistry ; Catalysis ; Oxygen Covalent Organic Framework Applications ; Electrocatalysts for Energy Conversion ; Oxidative Organic Chemistry Reactions
Authors: Jaideep Mor | K. Srinivasu | Rishabh Dubey | Sanjog S. Nagarkar | Sandeep Kumar Sharma
Source: The Journal of Physical Chemistry Letters (article, 2026-01-16)
Field: Chemistry — Inorganic Chemistry

Unraveling Topology-Driven Flexibility in Chlorinated Zeolitic Imidazolate Frameworks under CO <sub>2</sub> Pressure Using Experimental and Simulations Study Gas-framework interaction-induced flexibility of zeolitic imidazolate frameworks (ZIFs) is of paramount interest as it enhances the gas adsorption capacity and can also be exploited to fine-tune the gas separation selectivity of ZIFs. The gas-framework interaction-induced lattice expansion or linker-twisting leading to pore volume enhanement, a signature of flexibility, observed in ZIFs is primarily attributed to the metal-linker composition. Thus, observed flexible behavior of ZIFs was found to be dependent on crystal size, gas type and pressure, and temperature. In the present study, we report a contrasting flexible behavior of two polymorphs of a chlorinated ZIF (ZIF-71 and COK-17 having RHO and SOD topologies, respectively) having the same chemical composition, Zn(4,5-dichloroimidazolate)<sub>2</sub>. Using in situ positron annihilation lifetime spectroscopy (PALS) under high pressure (≤55 bar) of CO<sub>2</sub> gas, structural dynamics at cavity pores of these ZIFs has been investigated. It is observed that ZIF-71 behaves as a rigid framework, whereas COK-17 shows the characteristics of a highly flexible framework through the enlargement of pores as a result of the gas-framework interaction. It is also inferred from the PALS study that the filling mechanism of CO<sub>2</sub> molecules in the pore networks of these frameworks is distinctly different. The cavity pores of ZIF-71 are directly filled by CO<sub>2</sub> molecules starting from the lower pressure, whereas CO<sub>2</sub> molecules migrate to the cavity pores of COK-17 at higher pressure, which remain initially localized at the surrounding ring apertures. Grand canonical Monte Carlo and density functional theory simulations have been performed to get better insights about the host-guest interaction-induced flexibility in these frameworks. It is observed that different adsorption sites are energetically favored in these polymorphs due to their distinct topologies. CO<sub>2</sub> adsorption at particular sites in COK-17 leads to cavity pore volume expansion due to ring distortions and outward linker twisting. The present study confirms that the gas pressure-induced flexibility of ZIFs can be exclusively controlled by the topology for their enhanced applications in gas storage and separation. Zeolitic imidazolate framework ; Molecule ; Imidazolate ; Flexibility (engineering) ; Density functional theory ; Molecular dynamics ; Adsorption ; Non-equilibrium thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Membrane Separation and Gas Transport
Authors: Tim Habenicht | Bjarne Hastedt | Liam Cassidy | Claudia Kießling | Andreas Tholey | Jan M. Schuller | Ruth A. Schmitz
Source: FEBS Journal (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

<scp> H <sub>2</sub> </scp> ‐dependent modulation of tetrahydromethanopterin S‐methyltransferase (Mtr complex) activity by the small protein <scp>MtrR</scp> in <i>Methanosarcina mazei</i> Small open reading frame (sORF)-encoded proteins, with less than 100 amino acids, have attracted increasing attention over the past decade after being overlooked due to limitations in classical methodologies. For the mesophilic archaeal model system Methanosarcina mazei, a high number of previously unannotated sORFs have recently been identified. However, the physiological role of most of the respective small proteins remains unknown. Here, we report on the functional characterization of the small ORF16-encoded small protein MtrR (49 amino acids). We demonstrate that MtrR forms oligomers localized at the cytoplasmic membrane. There, it interacts with the tetrahydrosarcinapterin S-methyltransferase (Mtr), a key membrane-bound complex of energy metabolism, and impacts its activity. In vitro interaction and in vivo copurification assays showed MtrR interaction with the Mtr complex, which was further validated by microscale thermophoresis analysis demonstrating a specific interaction with the MtrA subunit. Analyzing growth under varying molecular hydrogen (H<sub>2</sub>) availability demonstrated that the mtrR deletion mutant showed significantly impaired growth in the presence of H<sub>2</sub>, independent of the carbon source. Further, we observed induction of mtrR transcription in the presence of H<sub>2</sub>. Consequently, we propose that MtrR fine-tunes the activity of the Mtr complex in response to fluctuating H<sub>2</sub> availabilities, allowing adaptation of the energy metabolism to changing environmental H<sub>2</sub> conditions. MTRR ; Microscale thermophoresis ; Open reading frame ; Immunoprecipitation ; Transcription factor ; Mutant ; Gene Enzyme Structure and Function ; Metalloenzymes and iron-sulfur proteins ; Bacterial biofilms and quorum sensing
Authors: Mohan Kodisana | Ismael A. Elayan | Macarena Tolmos | Hadi D. Arman | Alex Brown | Syed Muhammad Usama
Source: The Journal of Organic Chemistry (article, 2026-04-08)
Field: Chemistry — Spectroscopy

Synthetic Approach to Polymethine-Modified Trimethine Cyanines Gives Access to Viscosity-Sensitive Probes Polymethine functionalization of trimethine cyanine (Cy3) fluorophores remains challenging due to limited synthetic strategies. Here, we report a versatile synthetic strategy for accessing both symmetric and unsymmetric <i>meso</i>-substituted Cy3 fluorophores by using activated carboxyl intermediates. This three-step, high-yielding approach enables polymethine-modified Cy3. Through systematic variation of acyl chloride precursors and indolenine derivatives, we generated a structurally diverse library of Cy3 fluorophores containing aliphatic, aromatic, and electron-withdrawing <i>meso</i>-substituents. The approach is compatible with a broad range of indolenines. The Cy3 fluorophores have absorption and emission maxima between 550 and 590 nm and between 560 and 650 nm, respectively. Unsymmetric Cy3 derivatives have a larger Stokes shift (up to 100 nm) as compared to symmetrical Cy3. We show that <i>meso</i>-substituted benzothiazole-containing analogues exhibit up to 20-fold enhancement in brightness in high-viscosity environments and, as such, could be used as molecular rotors for viscosity sensing. Time-dependent density functional theory (TD-DFT) calculations further support the experimental observations by reproducing the substituent-dependent trends in absorption, emission, and Stokes shift across the series. The computed absorption wavelengths and Stokes shifts are consistent with the experimental results. This work establishes a synthetic scheme for <i>meso</i>-functionalized Cy3 to fine-tune photophysical properties for imaging, sensing, and bioconjugation applications. Work (physics) ; Field (mathematics) ; Yield (engineering) ; Process (computing) Molecular Sensors and Ion Detection ; Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications
Authors: Cui Cui Wang | Jin Hua Wang | Cheng Wen Li | Guan Wang | Yan Chen (4308) | Ya Qing Ge | Yun Kai Zhang | D. Zhang | Azura A. Rashid | Siew Kooi Ong
Source: Journal of Molecular Structure (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Halogen-engineered zinc(II) complexes based on a semicarbazone Schiff base: Correlating molecular structure with enhanced thermal stability and tunable luminescence Schiff base ; Halogen ; Halogen bond ; Thermal stability ; Photoluminescence ; Hydrogen bond ; Triclinic crystal system ; Luminescence ; Molecule Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Zhenyu Lei | Patrick Soga | Yaochen Zhu | Yulin He | Yushun Dong | Jundong Li
Source:  (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

MolEdit: Knowledge Editing for Multimodal Molecule Language Models Understanding and continuously refining multimodal molecular knowledge is crucial for advancing biomedicine, chemistry, and materials science. Molecule language models (MoLMs) have become powerful tools in these domains, integrating structural representations (e.g., SMILES strings, molecular graphs) with rich contextual descriptions (e.g., physicochemical properties, biomedical applications). However, MoLMs can encode and propagate inaccuracies due to outdated web-mined training corpora or malicious manipulation, jeopardizing downstream discovery pipelines. While knowledge editing has been explored for general-domain AI, its application to MoLMs remains uncharted, presenting unique challenges due to the multifaceted and interdependent nature of molecular knowledge. In this paper, we take the first step toward MoLM editing for two critical tasks: molecule-to-caption generation and caption-to-molecule generation. To address molecule-specific challenges, we propose MolEdit, a powerful framework that enables targeted modifications while preserving unrelated molecular knowledge. MolEdit combines a Multi-Expert Knowledge Adapter that routes edits to specialized experts for different molecular facets with an Expertise-Aware Editing Switcher that activates the adapters only when input closely matches the stored edits across all expertise, minimizing interference with unrelated knowledge. To systematically evaluate editing performance, we introduce MEBench, a comprehensive benchmark assessing multiple dimensions, including Reliability (accuracy of the editing), Locality (preservation of irrelevant knowledge), and Generality (robustness to reformed queries). Across extensive experiments on two popular MoLM backbones, MolEdit delivers up to 18.8 % higher Reliability and 12.0 % better Locality than state-of-the-art editing baselines while maintaining efficiency. Our findings chart a clear path toward safer, continuously updatable scientific foundation models. The code is available at: https://github.com/LzyFischer/MolEdit. ENCODE ; Adapter (computing) ; Generality ; Locality ; Language model ; Reliability (semiconductor) ; Collaborative editing ; Code (set theory) Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Advanced Graph Neural Networks
Authors: Abhijith S. Parackal | Rickard Armiento | Florian Trybel
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials Machine learned interatomic potentials (MLIPs) are becoming a standard method for DFT-level accurate molecular dynamics simulation and large-scale studies of crystal energetics. Increasingly popular are universal pre-trained potentials, also called foundation models, based one, e.g. the MACE, CHGNet, M3GNet, ORB, and SevenNet architectures. While there are many benchmarks of these models using validation errors and materials discovery tasks, their fidelity in reproducing the detailed features of potential energy surfaces (PES) is not understood to the same degree. We evaluate the accuracy of these potentials by systematically probing their predicted energy landscapes. Two-dimensional slices of the potential energy surface are constructed where the atomic positions are varied along selected Wyckoff degrees of freedom within a fixed crystal symmetry. This approach enables a direct, visual comparison of the interatomic potentials and DFT-calculated surfaces which reveals potential artifacts e.g., arising from unique local environments. Our analysis highlights the strengths and limitations of different potentials in capturing local minima, saddle points, and overall PES topology, offering insights into the physical accuracy of current pre-trained IAPs and providing benchmarks for future model development. Interatomic potential ; Benchmark (surveying) ; Energy (signal processing) ; Potential energy ; Molecular dynamics ; Fidelity ; Saddle point ; Degrees of freedom (physics and chemistry) Machine Learning in Materials Science ; Quantum many-body systems ; Inorganic Chemistry and Materials
Authors: Yu Cheng | Hang Wang | Longyu Wang | Zhong Wang | Zhibang Liu | Ziyi Zhang | Qiang Ma | chao zhang | Xiaoxue Zhang | Dongmin Bian | Lin Wang | Chuan-De Wu
Source: Journal of Material Science and Technology (article, 2026-03-27)
Field: Chemistry — Inorganic Chemistry

Interlayer-engineered 2D MOF-GO hybrid membranes with sub-nanometer channels for selective ion transport Membrane ; Ion transporter ; Channel (broadcasting) ; Ion channel ; Membrane transport Metal-Organic Frameworks: Synthesis and Applications ; Graphene research and applications ; Membrane Separation Technologies
Authors: Jinyoung Park | Minseong Bae | Jeehye Na | Hyunwoo J. Kim
Source: ArXiv.org (article, 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: Panagiotis Antoniadis | Beatrice Pavesi | Simon Olsson | Ole Winther
Source: Open MIND (preprint, 2026-02-11)
Field: Materials Science — Materials Chemistry

Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over high-dimensional molecular distributions such as Boltzmann distributions and transition densities. However, conventional MD is fundamentally limited by the high computational cost required to generate independent samples. Generative molecular dynamics (GenMD) has recently emerged as an alternative, learning surrogates of molecular distributions either from data or through interaction with energy models. While these methods enable efficient sampling, their transferability across molecular systems is often limited. In this work, we show that incorporating auxiliary sources of information can improve the data efficiency and generalization of transferable implicit transfer operators (TITO) for molecular dynamics. We find that coarse-grained TITO models are substantially more data-efficient than Boltzmann Emulators, and that incorporating protein language model (pLM) embeddings further improves out-of-distribution generalization. Our approach, PLaTITO, achieves state-of-the-art performance on equilibrium sampling benchmarks for out-of-distribution protein systems, including fast-folding proteins. We further study the impact of additional conditioning signals -- such as structural embeddings, temperature, and large-language-model-derived embeddings -- on model performance. Generalization ; Molecular dynamics ; Transferability ; Observable ; Experimental data ; Computational model ; Generative model ; Computation ; Complex system Machine Learning in Materials Science ; Protein Structure and Dynamics ; Generative Adversarial Networks and Image Synthesis
Authors: Panagiotis Antoniadis | Beatrice Pavesi | Simon Olsson | Ole Winther
Source: ArXiv.org (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over high-dimensional molecular distributions such as Boltzmann distributions and transition densities. However, conventional MD is fundamentally limited by the high computational cost required to generate independent samples. Generative molecular dynamics (GenMD) has recently emerged as an alternative, learning surrogates of molecular distributions either from data or through interaction with energy models. While these methods enable efficient sampling, their transferability across molecular systems is often limited. In this work, we show that incorporating auxiliary sources of information can improve the data efficiency and generalization of transferable implicit transfer operators (TITO) for molecular dynamics. We find that coarse-grained TITO models are substantially more data-efficient than Boltzmann Emulators, and that incorporating protein language model (pLM) embeddings further improves out-of-distribution generalization. Our approach, PLaTITO, achieves state-of-the-art performance on equilibrium sampling benchmarks for out-of-distribution protein systems, including fast-folding proteins. We further study the impact of additional conditioning signals -- such as structural embeddings, temperature, and large-language-model-derived embeddings -- on model performance. Generalization ; Molecular dynamics ; Transferability ; Observable ; Experimental data ; Computational model ; Generative model ; Computation ; Complex system Machine Learning in Materials Science ; Protein Structure and Dynamics ; Generative Adversarial Networks and Image Synthesis