jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vera M. Kissling | Stephanie Eitner | Davide Bottone | Gea Cereghetti | Peter Wick
Source: Advanced Healthcare Materials (article, 2025-04-29)
Field: Materials Science — Surfaces, Coatings and Films

Systematic Comparison of Commercial Uranyl‐Alternative Stains for Negative‐ and Positive‐Staining Transmission Electron Microscopy of Organic Specimens Negative- and positive-staining transmission electron microscopy (ns/psTEM) is a cornerstone of research and diagnostics, enabling nanometer-resolution analysis of organic specimens from nanoparticles to cells without requiring costly cryo-equipment. For nearly 70 years, uranyl salts like uranyl acetate (UA) have been the gold-standard ns/psTEM-stains. However, mounting safety concerns due to their high toxicity and radioactivity have led to stricter regulations and expensive licensing requirements. Consequently, there is an urgent global demand for safer, more sustainable stains that deliver uranyl-comparable, high-quality ns/psTEM. Here, the commercially available stain-alternatives UranyLess, UAR, UA-Zero, PTA, STAIN 77, Nano-W, NanoVan, and lead citrate are systematically assessed against UA. The stains are evaluated regarding their contrast, resolution, stain-distribution, and ease-of-use in ns/psTEM across a diverse sample set, including polymethylmethacrylate-nanoplastics, phosphatidylcholine-liposomes, Influenza-A viruses, globular ferritin, fibrillar pyruvate kinase amyloids, and human lung-carcinoma cell-sections. It is shown that for this variety of samples, a ready-to-use uranyl-alternative is commercially available with comparable or even superior ns/psTEM-performance to UA using an efficient staining-protocol. Furthermore, the GUIDE4U tool is developed for the fast identification of the appropriate uranyl-replacements for each sample of interest, saving ns/psTEM-users time and costs while ensuring excellent staining results for ultrastructural analysis, thereby further catalyzing the use of safer stains. Uranyl acetate ; Uranyl ; Stain ; Staining ; Nanotechnology ; Negative stain ; Transmission electron microscopy ; Materials science ; Chemistry ; Nuclear chemistry ; Electron microscope ; Uranium ; Medicine ; Pathology ; Optics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: H. P. Xin | Yunze Huang | Weibo Zhang | Peng Li | Huanrong Li
Source: Advanced Science (article, 2025-08-24)
Field: Materials Science — Materials Chemistry

Visible‐Light‐Activated Multi‐Color Tunable Time‐Dependent Afterglow Triggered by Variable Conjugation Effects via the Transformation of Matrix Achieving multi-color tunable time-dependent afterglow color (TDAC) in pure organic materials under visible light excitation remains a significant challenge. Herein, TDAC composites (CDs/U) are prepared with multi-color tunability upon visible-light excitation. Furthermore, the TDAC mechanism is the coexistence of shorter-lived afterglow and longer-lived afterglow. The long- and short-wavelength afterglow in CDs/U originate from highly conjugated nitrogen heterocyclic structures and abundant surface functional states, respectively. More impressively, p-CDs/U systems (p-phenylenediamine (p-PD) as carbon source) exhibit dynamic TDAC behaviors with different decay rates of long-wavelength red afterglow as varying the reaction temperature varies. The experimental results indicate that the transformation of the matrix from biuret to ammelide and then to cyanuric acid (CA) decreases the conjugation degree of p-CDs/U systems owing to the lower pyrrolic N content. This is not conducive to long-wavelength emission, leading to shorter lifetimes in the long-wavelength region. Meanwhile, the theoretical calculation confirms that the matrix is critical for efficient thermally activated delayed fluorescence (TADF). Moreover, the three composites exhibit distinct TDAC behaviors because p-PD and o-PD develop higher conjugation during CDs formation compared to m-PD. Finally, benefiting from the excellent TDAC characteristics of these composites, we have successfully developed multi-mode anti-counterfeiting and multi-dimensional encryption. Afterglow ; Photochemistry ; Materials science ; Visible spectrum ; Wavelength ; Matrix (chemical analysis) ; Fluorescence ; Chemistry ; Optoelectronics ; Optics ; Composite material ; Physics Luminescence and Fluorescent Materials ; Carbon and Quantum Dots Applications ; Organic Light-Emitting Diodes Research
Authors: Uğur Kafkas | Gökhan Güçlü
Source: Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi (article, 2025-03-15)
Field: Materials Science — Materials Chemistry

Investigation of buckling behavior of carbon nanotube reinforced nanobeams according to nonlocal elasticity and elastic foundation effects In this study, the buckling behavior of carbon nanotube (CNT) reinforced nanobeams on an elastic foundation is investigated using the nonlocal elasticity theory and nonlocal finite element method within the framework of Euler-Bernoulli beam theory. The effects of CNT volume fraction, nonlocal parameter, elastic foundation parameter and length-to-thickness ratio on the critical buckling load are analyzed. CNT reinforced nanobeam is modeled considering short and long CNT reinforcements, and mechanical properties are determined by the rule of mixtures. To determine the critical buckling loads of CNT reinforced nanobeams, stiffness matrices and force vectors, including nonlocal effects, are derived based on the Euler-Bernoulli beam theory and analyses are carried out accordingly. The results show that increasing the CNT volume fraction significantly increases the critical buckling load, suggesting that CNTs play an essential role in strengthening the nanobeams. On the other hand, the nonlocal parameter negatively affects the critical buckling load and decreases the buckling strength of the nanobeam. However, the effect of the nonlocal parameter on the buckling strength is negligible. The elastic foundation parameter positively affects the critical buckling load and increases the buckling resistance. This finding indicates that the elastic foundation plays an important role in improving the structural stability of the nanobeams. The length-to-thickness ratio is another important parameter, indicating that long and thin nanobeams are more prone to buckling and the critical buckling load decreases with increasing this ratio. Since the effects of the above-mentioned four parameters on the buckling strength were determined using the finite element method, the results obtained will guide the subsequent numerical modeling studies of nano-sized beams. Buckling ; Carbon nanotube ; Elasticity (physics) ; Foundation (evidence) ; Materials science ; Structural engineering ; Composite material ; Engineering ; Law Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Thermoelastic and Magnetoelastic Phenomena
Authors: Muhammad Usman | Fuyi Chen
Source: The European Physical Journal D (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Generation and optimization of gold nanoclusters via reinforcement learning Nanoclusters ; Reinforcement learning ; Reinforcement ; Computer science ; Artificial intelligence ; Materials science ; Nanotechnology ; Composite material Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; nanoparticles nucleation surface interactions
Authors: Muhammad Umar Farooq | Fuyi Chen
Source: Applied Surface Science (article, 2025-08-10)
Field: Materials Science — Materials Chemistry

Accelerating the global minimum search in Pd-Ag-Cu nanoclusters using deep reinforcement learning Nanoclusters ; Reinforcement learning ; Reinforcement ; Materials science ; Nanotechnology ; Composite material ; Computer science ; Artificial intelligence Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Xini Chu | Jingguo Li | Di Wang | Liyu Gan | Xu Liu | Zeyu Feng | Peng Yang | Hongli Zhang | Yifan Xie | Fei Wang | Gang Zou
Source: Nature Communications (article, 2025-11-24)
Field: Chemistry — Inorganic Chemistry

Twisted-stacking MOF patterns with tailorable circularly polarized luminescence properties and encryption applications Optical active metal-organic frameworks (MOFs) with circularly polarized luminescence (CPL) functionalities, have recently gained extensive research interest. However, the luminescence dissymmetry factors (g<sub>lum</sub>) of these materials are generally small, and the lack of patterning ability for most optical active MOFs greatly hinders their practical applications. Herein, we report a strategy to design twisted-stacking MOF patterns with a high g<sub>lum</sub> (~0.4) and tailorable CPL performance by the combination of brush coating, bottom-up heteroepitaxial growth and twisted-stacking techniques. Specifically, two-dimensional (2D) programmable patterns of highly oriented MOF films could be achieved by the heteroepitaxial growth from pre-aligned Cu(OH)<sub>2</sub> nanowire (NW) templates. The oriented MOF patterns could be functionalized with fluorescent dyes, offering characteristic anisotropic photoluminescent response. After another similar highly oriented MOF layer was deposited on top of the above MOF/dye patterns (in a twisted fashion) as a phase retarder, a chiroptical response could be generated. This design not only allows the fabrication of arbitrary programmable optical active MOF patterns with enhanced g<sub>lum</sub>, but also enables precise regulation of CPL performance within the patterns, favoring the implementation of the miniaturized chiroptical devices, information encryption and storage. Luminescence ; Fabrication ; Encryption ; Photoluminescence ; Nanowire ; Active layer ; Layer (electronics) Metal-Organic Frameworks: Synthesis and Applications ; Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials
Authors: Yue Pan | Shiyu Zhen | Xiaozhi Liu | Mengshu Ge | Jianxiong Zhao | Lin Gu | Dan Zhou | Liang Zhang | Dong Su
Source: Nature Communications (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Looping metal-support interaction in heterogeneous catalysts during redox reactions Metal-support interfaces fundamentally govern the catalytic performance of heterogeneous systems through complex interactions. Here, utilizing operando transmission electron microscopy, we uncover a looping metal-support interaction in NiFe-Fe<sub>3</sub>O<sub>4</sub> catalysts during the hydrogen oxidation reaction. At the NiFe-Fe<sub>3</sub>O<sub>4</sub> interfaces, lattice oxygens react with NiFe-activated H atoms, gradually sacrificing themselves and resulting in dynamically migrating interfaces. Meanwhile, reduced iron atoms migrate to the {111} surface of Fe<sub>3</sub>O<sub>4</sub> support and react with oxygen molecules. Consequently, the hydrogen oxidation reaction separates spatially on a single nanoparticle and is intrinsically coupled with the redox reaction of the Fe<sub>3</sub>O<sub>4</sub> support through the dynamic migration of metal-support interfaces. Our work provides previously unidentified mechanistic insight into metal-support interactions and underscores the transformative potential of operando methodologies for studying atomic-scale dynamics. Redox ; Catalysis ; Heterogeneous catalysis ; Hydrogen ; Nanoparticle ; Oxygen ; Chemical looping combustion Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Mo Yang | Shengda Chen | Daojing Li | Liuzhou Gao | Guoqiang Wang | Shuhua Li
Source: Advanced Science (article, 2025-08-30)
Field: Chemistry — Organic Chemistry

Direct Carboboration of Aryl Alkenes with Stable Organoborons Through Ziegler‐Type Addition Carbometallation reaction represents a classic strategy for the bifunctionalization of unsaturated hydrocarbons, yet conventional implementations predominantly rely on either highly reactive organometallic reagents or transition metal catalysts. Notably, the direct employment of metalloid organoboron reagents for such addition processes remains elusive except when specific substrate combinations are employed. Herein, a base-catalyzed carboboration reaction of aryl alkenes with bench-stable benzylic boronates is reported. This anionic-based strategy is not only applicable to the carboboration of substituted styrenes but also to vinyl cyclopropanes and vinyl cyclobutane, affording the corresponding 1,5- and 1,6-carboboration-related products. Combined experimental and computational investigations reveal a mechanism that involves the Ziegler-type carbometallation process. The synthetic utility is further highlighted by the facile one-pot derivatization of the resulting benzylic boronates. Furthermore, a modified catalytic system offers an alternative to the anionic polymerization of styrene. Aryl ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Alkyl Organoboron and organosilicon chemistry ; Boron Compounds in Chemistry ; Catalytic Cross-Coupling Reactions
Authors: Joy Datta | Amruth Nadimpally | Nikhil Koratkar | Dibakar Datta
Source: Cell Reports Physical Science (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Generative AI for discovering porous oxide materials for next-generation energy storage Generative AI explores porous TMOs for multivalent-ion battery materials discovery CDVAE generates diverse and novel TMO structures with potential for energy applications LLM generates stable TMO-based structures close to thermodynamic equilibrium Generative grammar ; Porous medium ; Energy storage ; Porosity ; Materials science ; Computer science ; Process engineering ; Artificial intelligence ; Engineering ; Physics ; Composite material ; Thermodynamics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: J.D. Liu | Fang Zhong | Hongming Weng | Quansheng Wu
Source: npj Computational Materials (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

DPmoire: a tool for constructing accurate machine learning force fields in moiré systems In moiré systems, the impact of lattice relaxation on electronic band structures is significant, yet the computational demands of first-principles relaxation are prohibitively high due to the large number of atoms involved. To address this challenge, We introduce a robust methodology for the construction of machine learning potentials specifically tailored for moiré structures and present an open-source software package DPmoire designed to facilitate this process. Utilizing this package, we have developed machine learning force fields (MLFFs) for MX2 (M = Mo, W; X = S, Se, Te) materials. Our approach not only streamlines the computational process but also ensures accurate replication of the detailed electronic and structural properties typically observed in density functional theory (DFT) relaxations. The MLFFs were rigorously validated against standard DFT results, confirming their efficacy in capturing the complex interplay of atomic interactions within these layered materials. Moiré pattern ; Computer science ; Artificial intelligence ; Engineering drawing ; Computer vision ; Engineering Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Jinming Hu | Hassan Nawaz | Yuting Rui | Lijie Chi | Arif Ullah | Pavlo O. Dral
Source: ChemRxiv (preprint, 2025-07-04)
Field: Materials Science — Materials Chemistry

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations We have developed Aitomia – a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This evolving intelligent assistant platform is equipped with chatbots and AI agents to help experts and guide non-experts in setting up and running the atomistic simulations, monitoring their computation status, analyzing the simulation results, and summarizing them for the user in text and graphical forms. We achieve these goals by exploiting open-source large language models (LLMs, original and fine-tuned), rule-based agents, and a retrieval-augmented generation (RAG) system. Aitomia leverages the versatility of our MLatom ecosystem, supporting AI-enhanced computational chemistry tasks ranging from ground- to excited-state calculations such as geometry optimizations, thermochemistry, and spectra calculations. Aitomia is the first intelligent assistant publicly accessible online on a cloud computing platform for atomistic simulations of broad scope (Aitomistic Hub at https://aitomistic.xyz), while it may also be deployed locally as described at http://mlatom.com/aitomia. Aitomia is expected to lower the barrier to performing atomistic simulations, democratizing simulations, and accelerating research and development in the relevant fields. Quantum chemical ; Quantum ; Computer science ; Nanotechnology ; Materials science ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Heather J. Kulik
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2025-03-30)
Field: Materials Science — Materials Chemistry

Using experimental data in computationally guided rational design of inorganic materials with machine learning Abstract While the impact of machine learning (ML) has been felt everywhere, its effect has been most transformative where large, high-quality datasets are available. For promising materials spaces, such as transition metal coordination complexes and metal–organic frameworks, the large chemical diversity has not yet been matched by similarly large datasets, and computational datasets (e.g., from density functional theory) may not be predictive. Extraction of experimental data from the literature represents an alternative approach to the data-driven design of materials. This perspective will describe efforts in (i) extracting experimental data; (ii) associating extracted data with known chemical structures; (iii) leveraging data in ML and screening; (iv) designing materials with enriched stability; and (v) using experimental data to improve high-throughput workflows. I will summarize some of the outstanding challenges and opportunities for data enrichment with high-throughput experimentation and large language models. Graphical abstract Computer science ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Max Dreger | Kourosh Malek | Michael Eikerling
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Large language models for knowledge graph extraction from tables in materials science Large language models are promising tools for unifying scattered data, extracting information, and creating knowledge graphs. Knowledge graph ; Computer science ; Graph ; Natural language processing ; Data science ; Information retrieval ; Theoretical computer science Machine Learning in Materials Science ; Data Quality and Management ; Topic Modeling
Authors: Miikka Sipilä | Farrokh Mehryary | Sampo Pyysalo | Filip Ginter | Milica Todorović
Source: Scientific Data (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Annotated textual dataset PV600 of perovskite bandgaps for information extraction from literature Scientific literature provides a variety of experimental and theoretical data which, if extracted, could offer new opportunities for data-driven discovery in materials research. Natural language processing (NLP) tools enable information extraction (IE) of structured information from unstructured text. The performance of IE tools needs to be systematically evaluated on manually annotated test datasets, but there are few publicly available annotated materials science datasets and none on perovskites, promising materials for photovoltaics. We present a perovskite literature dataset with 600 text segments extracted from an open access manuscript corpus. The PV600 dataset focuses on five inorganic and hybrid perovskites and contains 227 manually annotated bandgap values identified from 188 segments. Moreover, we recorded the bandgap type, whether it was experimental, computational, from the literature, or from unknown source. To demonstrate the intended use of the dataset, we applied it to evaluate the IE performance of a question answering (QA) method, a rule-based method, and generative language models (LLMs). We exhibit a further application in testing segment preselection with LLMs in IE. Computer science ; Biomedical text mining ; Information extraction ; Variety (cybernetics) ; Generative grammar ; Information retrieval ; Artificial intelligence ; Natural language processing ; Text mining Machine Learning in Materials Science ; Topic Modeling ; Expert finding and Q&A systems
Authors: Xiaohan Lin | Yijie Xia | Y. G. Li | Yupeng Huang | Shuo Liu | Jun Zhang | Yi Qin Gao
Source: Nature Communications (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

In-silico 3D molecular editing through physics-informed and preference-aligned generative foundation models Generating molecular structures towards desired properties is a critical task in computer-aided drug and material design. As special 3D entities, molecules inherit non-trivial physical complexity, and many intrinsic properties may not be learnable through pure data-driven approaches, hindering the transaction of powerful generative artificial intelligence (GenAI) to this field. To avoid existing molecular GenAI's heavy reliance on domain-specific models and priors, in this research, we derive theoretical guidelines to bridge the methodological gap between GenAI for images and molecules, allowing pre-training of foundation models for 3D molecular generation. Difficulties due to symmetry, stability and entropy, which are critical for molecules, are overcome through a simple and model-agnostic training protocol. Moreover, we apply physics-informed strategies to force MolEdit, a pre-trained multimodal molecular GenAI, to obey physics laws and align with contextual preferences, and thus suppress undesired model hallucinations. MolEdit can generate valid molecules with comprehensive symmetry, strikes a better balance between configuration stability and conformer diversity, and supports complicated 3D scaffolds which frustrate other methods. Furthermore, MolEdit is applicable for zero-shot lead optimization and linker design following contextual and geometrical specifications. Collectively, as a foundation model, MolEdit offers flexibility and developability for AI-aided editing and manipulation of molecules serving various purposes. In silico ; Foundation (evidence) ; Preference ; Computational biology ; Generative grammar ; Computer science ; Biology ; Data science ; Artificial intelligence ; Genetics ; Gene ; Geography ; Mathematics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Chemical Synthesis and Analysis