jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhen Zhang | K. D. Belashchenko | Vladimir Antropov
Source: AIP Advances (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

High-throughput studies of novel magnetic materials in borides In summary, to explore boride systems’ novel magnetism and magnetic materials, we develop a workflow encompassing crystal structure analysis, high-throughput thermodynamic stability calculations, high-throughput magnetic structure calculations, and magnetic ground state analysis. Utilizing the workflow, we study three families of novel magnetic borides: dimerized quantum magnets in the YCrB4-type family, altermagnets in the FeMo2B2-type family, and magnetic MAB phases in the AlFe2B2-type family. These findings potentially enable new research in borides in areas such as spin-gap QCP, BEC of magnetic excitations, altermagnetism, next-generation spintronics, magnonics, magnetocaloric effect, and 2D magnetism. With high efficiency, the developed workflow is expected to discover more novel magnetism and magnetic materials in material families, including but not limited to borides. Throughput ; Materials science ; Nanotechnology ; Computer science ; Telecommunications Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Rare-earth and actinide compounds
Authors: Lei Guo | Xia Wang | Hong Li | Mengli Zhou | Liqin Liu | Ruihong Wang | Pan Li
Source: BMC Genomics (article, 2025-02-04)
Field: Materials Science — Biomaterials

Using RAD-seq to identify and differentiate the medicinal herb Scrophularia ningpoensis and its adulterants This study successfully identified S. ningpoensis and the three adulterants, enriched the genomic information of Scrophularia species, and contributed to a more profound understanding of the evolutionary dynamics within this genus. Furthermore, it offers novel insights and methodologies for the identification of medicinal plants. Biology ; Adulterant ; Radix (gastropod) ; Traditional medicine ; Phylogenetic tree ; Evolutionary biology ; Botany ; Genetics ; Medicine ; Gene Phytochemistry and Bioactive Compounds ; Phytochemistry and Biological Activities ; Plant Pathogens and Fungal Diseases
Authors: Yuyin Zhang | Zixiao Jiang | Hong Dou | Chenlu Fan | Jianhe Wei | Xuyu Chen
Source: Frontiers in Plant Science (article, 2025-05-13)
Field: Chemistry — Organic Chemistry

Combined analysis of the endophytic fungi and volatile oil content of different Aquilaria sinensis germplasms revealed the correlations between endophytic fungal abundances and agarwood production These findings provide critical insights into the role of endophytic fungi in shaping agarwood's chemical composition and have practical implications for enhancing agarwood production in the industry. Consequently, this research has significant implications for the agarwood industry, as it enhances our understanding of how fungi influence resin quality and paves the way for improving the efficiency of agarwood induction, ultimately leading to higher-quality and more sustainable production. Agarwood ; Plant use of endophytic fungi in defense ; Biology ; Botany ; Horticulture Wood and Agarwood Research ; Identification and Quantification in Food ; Forest Ecology and Biodiversity Studies
Authors: Angel Erbey Ibarra | Wenxin Wu | Haoran Zhang | Lingjun Li
Source: RSC Chemical Biology (review, 2025-01-01)
Field: Chemistry — Spectroscopy

Quantitative neuropeptide analysis by mass spectrometry: advancing methodologies for biological discovery Neuropeptides are critical endogenous signaling molecules involved in a wide range of biological processes, including neurotransmission, hormonal regulation, immune responses, and stress management. Despite their importance, the field of neuropeptide research has been historically hampered by significant technical challenges. These include their low abundance in biological systems, diverse and complex post-translational modifications, dynamic expression patterns, and susceptibility to degradation. As such, traditional proteomics approaches often fall short of accurately characterizing neuropeptides, underscoring the need for specialized methodologies to unlock their biological and translational potential. This review evaluates state-of-the-art quantitative mass spectrometry (MS)-based peptidomics, emphasizing their impact on neuropeptide analysis. We highlight how strategies in label-free and label-based quantitation, tandem MS acquisition, and mass spectrometry imaging provide unprecedented sensitivity and throughput for capturing the landscape of neuropeptides and their modifications. Importantly, the review bridges technological innovation with practical applications, highlighting how these approaches have been utilized to uncover novel neuropeptides and elucidate their roles in systems biology and disease pathways. Mass spectrometry ; Computational biology ; Chemistry ; Computer science ; Chromatography ; Data science ; Biochemical engineering ; Engineering ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Bao Gegen | Bao Huhe | Zhiqiang Ou | F. Guillou | H. Yibole
Source: Materials (article, 2025-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermal Hysteresis and Reversibility of the Giant Magnetocaloric Effect at the Ferromagnetic Transition of Nd2In The Nd<sub>2</sub>In compound exhibits an intriguing borderline first-/second-order transition at its Curie temperature. Several studies have pointed to its potential for magnetic cooling, but also raised controversies about the actual order of the transition, the amplitudes of the hysteresis, and of its magnetocaloric effect. Here, we estimate the thermal hysteresis using magnetic and thermal measurements at different rates. It is found to be particularly small (0.1-0.4 K), leading to almost fully reversible adiabatic temperature changes when comparing zero-field cooling and cyclic protocols. Some open questions remain with regard to the magnetostriction of Nd<sub>2</sub>In, which is presently found to be limited, in line with the absence of a thermal expansion discontinuity at the transition. The comparison of the magnetocaloric effect in Nd<sub>2</sub>In and Eu<sub>2</sub>In highlights that the limited saturation magnetization of the former affects its performance. Further efforts should therefore be made to design materials with such borderline first-/second-order transitions using heavier rare earths. Magnetic refrigeration ; Condensed matter physics ; Curie temperature ; Hysteresis ; Materials science ; Ferromagnetism ; Thermal hysteresis ; Magnetostriction ; Magnetization ; Adiabatic process ; Thermal ; Magnetic hysteresis ; Saturation (graph theory) ; Magnetic field ; Thermodynamics ; Phase transition ; Physics Magnetic and transport properties of perovskites and related materials ; Shape Memory Alloy Transformations ; Magnetic Properties of Alloys
Authors: Paola Sebastiani | Eric Reed | Kevin Brown Chandler | Prisma Lopez | Hannah Lords | Harold Bae | Catherine E. Costello | Matthew Au | Lih‐Wen Deng | Mengze Li | Qingyan Xiang | Heeju Noh | Lance Pflieger | Cory C. Funk | Noa Rappaport | Marianne Nygaard | Meghan I. Short | Michael R. Brent | Stefano Monti | Stacy L. Andersen | Thomas T. Perls
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-29)
Field: Chemistry — Spectroscopy

Robust Serum Proteomic Signatures of APOE2 We previously identified a signature of 16 serum proteins that highlighted a role of the e2 allele of APOE in lipid regulation via apolipoprotein B (APOB) and apolipoprotein E (APOE), and in inflammation. The serum proteins were profiled using the aptamer-based Somalogic technology. Here, we validate and expand the serum protein signature of APOE using a combination of mass-spectrometry, ELISA, Luminex, antibody-based Olink proteomics, and blood transcriptomics. We replicate the association between APOB and the e2 allele of APOE, we correct the pattern of association between APOE genotypes and serum level of APOE, and we detect new associations between APOE genotypes and the complex of apolipoproteins APOC1, APOC4, APOC2, APOC3, APOE, APOF and APOL1. In addition, we discover 13 new proteins that correlate with APOE genotypes. This extended signature includes granule proteins CAMP, CTSG, DEFA3, and MPO secreted from neutrophils and points to olfactomedin 4 (OLFM4) as a new target for the prevention of Alzheimer's disease. Computational biology ; Computer science ; Biology Advanced Proteomics Techniques and Applications ; Protease and Inhibitor Mechanisms ; Protein Kinase Regulation and GTPase Signaling
Authors: Xinyi Mou | Huaidong Du | Gang Qiao | Jing Li
Source: Briefings in Bioinformatics (article, 2025-03-01)
Field: Chemistry — Spectroscopy

Evaluation of imputation and imputation-free strategies for differential abundance analysis in metaproteomics data For metaproteomics data derived from the collective protein composition of dynamic multi-organism systems, the proportion of missing values and dimensions of data exceeds that observed in single-organism experiments. Consequently, evaluations of differential analysis strategies in other mass spectrometry (MS) data (such as proteomics and metabolomics) may not be directly applicable to metaproteomics data. In this study, we systematically evaluated five imputation methods [sample minimum, quantile regression, k-nearest neighbors (KNN), Bayesian principal component analysis (bPCA), random forest (RF)] and six imputation-free methods (moderated t-test, two-part t-test, two-part Wilcoxon test, semiparametric differential abundance analysis, differential abundance analysis with Bayes shrinkage estimation of variance method, and Mixture) for differential analysis in simulated metaproteomic datasets based on both data-dependent acquisition MS experiments and emerging data-independent acquisition experiments. The simulation datasets comprised 588 scenarios by considering the impacts of sample size, fold change between case and control, and missing value ratio at random and nonrandom. Compared to imputation-free methods, KNN, bPCA, and RF imputation performed poorly in datasets with a high missingness ratio and large sample size and resulted in a high false-positive risk. We made empirical recommendations based on the balance of sensitivity in analysis and control of false positives. The moderated t-test was optimal in scenarios of large sample size with a low missingness ratio. The two-part Wilcoxon test was recommended in scenarios of small sample size with a low missingness ratio or large sample size with a high missingness ratio. The comprehensive evaluations in our study can provide guidance for the differential abundance analysis in metaproteomics. Missing data ; Imputation (statistics) ; Metaproteomics ; Statistics ; Sample size determination ; Wilcoxon signed-rank test ; Computer science ; Bayesian probability ; Data mining ; Mathematics ; Biology ; Metagenomics Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Gene expression and cancer classification
Authors: Jaehwan Choi | Seong-Min Kim | Yousung Jung
Source: Journal of the American Chemical Society (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Synthesis-Aware Materials Redesign via Large Language Models We propose a novel framework that leverages large language models (LLMs) to transform synthetically infeasible inorganic crystal structures into synthetically feasible ones. Unlike previous studies on synthesis predictions, which focus primarily on estimating synthesizability, our method provides actionable solutions for redesigning unsynthesizable materials into synthesizable ones. By integrating an invertible structural representation and an iterative fine-tuning strategy, our framework not only predicts synthetic feasibility but also modifies unsynthesizable materials into viable candidates. As a result, we demonstrate that LLMs can effectively modify materials of various types, enhancing their synthesizability and increasing the likelihood of successful synthesis. As an indirect experimental validation, we demonstrate that 34 materials among the top 100 redesigned (but originally unsynthesizable) structures have indeed been experimentally reported in the literature. This approach addresses a critical gap between design and synthesis in materials science, and enables the discovery of experimentally realizable compounds by employing the "learn-and-regenerate" strategy in LLMs. Representation (politics) ; Focus (optics) ; Invertible matrix ; Modeling language ; Language model ; Synthetic data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Xinfang Jia | Yu Bao | Shuo Cao | Ye Su | Ping Qian
Source: Journal of Nuclear Materials (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Temperature effects on radiation damage in HCP-zirconium: A molecular dynamics study using a fine-tuned machine-learned potential Zirconium ; Radiation damage ; Molecular dynamics ; Dynamics (music) ; Radiation ; Materials science ; Nuclear engineering ; Chemical physics ; Nuclear physics ; Chemistry ; Physics ; Metallurgy ; Computational chemistry ; Engineering ; Acoustics Nuclear Materials and Properties ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Nicholas T. Runcie | Charlotte M. Deane | Fergus Imrie
Source: Journal of Chemical Information and Modeling (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Assessing the Chemical Intelligence of Large Language Models Large Language Models are versatile, general-purpose tools with a wide range of applications. Recently, the advent of "reasoning models" has led to substantial improvements in their abilities in advanced problem-solving domains such as mathematics and software engineering. In this work, we assessed the ability of reasoning models to perform chemistry tasks directly, without any assistance from external tools. We created a novel benchmark, called ChemIQ, consisting of 816 questions assessing core concepts in organic chemistry, focused on molecular comprehension and chemical reasoning. Unlike previous benchmarks, which primarily use multiple choice formats, our approach requires models to construct short-answer responses, more closely reflecting real-world applications. The reasoning models, OpenAI's o3-mini, Google's Gemini 2.5 Pro, and DeepSeek R1, answered 50%-57% of questions correctly in their highest reasoning modes, with higher reasoning levels significantly increasing performance on all tasks. These models substantially outperformed the nonreasoning models which achieved only 3%-7% accuracy. We found that Large Language Models can now convert SMILES strings to IUPAC names, a task earlier models were unable to perform. Additionally, we show that the latest reasoning models can elucidate structures from 1D and 2D <sup>1</sup>H and <sup>13</sup>C NMR data, with Gemini 2.5 Pro correctly generating SMILES strings for around 90% of molecules containing up to 10 heavy atoms, and in one case solving a structure comprising 25 heavy atoms. For each task, we found evidence that the reasoning process mirrors that of a human chemist. Our results demonstrate that the latest reasoning models are becoming increasingly capable of performing advanced chemical reasoning. Comprehension ; Construct (python library) ; Task (project management) ; Process (computing) ; Verbal reasoning ; Qualitative reasoning ; Range (aeronautics) ; Language model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Nathany Lisbôa de Souza e Castro | Yiyi Deng | Xiangfei Chen | Márcio Nele | José Carlos Pinto | Jens Gaitzsch
Source: Journal of Polymer Science (article, 2025-07-03)
Field: Materials Science — Biomaterials

Fine‐Tuning the Crystallinity and Enzymatic Degradation of Nanoparticles Produced by Radical Ring‐Opening Polymerization ( <scp>RROP</scp> ) ABSTRACT Radical ring‐opening polymerization (RROP) of cyclic ketene acetals (CKAs) has gained attention in the last decade due to its ability to form a variety of degradable polyesters. Among them, 2‐methylene‐1,3,6‐trioxocane (MTC) and 2‐methylene‐1,3‐dioxepane (MDO) are the most studied. Amphiphilic block copolymers with varying amounts of poly‐MTC and poly‐MDO were prepared by RROP in the present work. Targeted variations in the MTC/MDO ratio allowed the production of block copolymers with different chain lengths and degrees of polymerization. Self‐assembled polymeric nanoparticles were obtained through thin film rehydration of the produced copolymers in phosphate buffered saline (PBS) at 37°C. Dynamic light scattering (DLS) measurements of the samples showed particle size distributions ranging from 171 to 274 nm. Nile red dye was encapsulated in the nanoparticles, and fluorescence spectroscopy was used to study enzymatic degradation and cargo release. Results showed that degradation varied with the composition, as the semi‐crystalline PMDO slowed down the degradation. Degradation with lipase ranged from 37% to 95% depending on the sample. These results hence show that the stability of nanoparticles based on RROP can be tailored by the chemical composition of the hydrophobic block. Crystallinity ; Degradation (telecommunications) ; Nanoparticle ; Polymerization ; Chemical engineering ; Chemistry ; Polymer chemistry ; Ring (chemistry) ; Materials science ; Polymer ; Organic chemistry ; Nanotechnology ; Computer science ; Crystallography biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization ; Graphene and Nanomaterials Applications
Authors: D. F. Cui | Shi-Yu Long | Yixuan Tang | Yue Zhao | Qiao Li
Source: Journal of Chemical Information and Modeling (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Can Reasoning Power Significantly Improve the Knowledge of Large Language Models for Chemistry?─Based on Conversations with LLMs This study presents a systematic evaluation of five reasoning-enhanced Large Language Models (LLMs)─Deepseek-R1-0528, OpenAI-o4 mini, Gemini-2.5-pro, doubao-seed-1.6-thinking, and qwen-max-latest─across nine key chemistry tasks. By comparing these models with traditional LLMs and established computational tools, we systematically investigate the influence of reasoning capabilities and prompt engineering on chemical cognition. The results demonstrate that reasoning-enabled LLMs achieve significant performance improvements in fundamental tasks and that, in most cases, overly complex prompts are not beneficial for these models. However, domain-specific limitations persist; for instance, all five models exhibited structural inaccuracies in CIF file generation (such as incorrect bond topologies). Notably, while reasoning frameworks enhance logical coherence, they do not fundamentally resolve challenges in stereochemical identification or the recognition of rare symmetry groups. In essence, the spatial recognition capabilities of current Large Language Models remain insufficient. These findings underscore the necessity of developing domain-optimized training paradigms to bridge the gap between general reasoning capabilities and specialized chemical applications. Power (physics) ; Computer science ; Natural language processing ; Chemistry ; Physics Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Topic Modeling
Authors: Estefanía Fernández | Patricia Pérez-Bailac | Pablo G. Lustemberg | Ana B. Hungría | L. Pascual | Renato Cataluña | Alejandro Vidal‐Moya | Teresa Blasco | M. V. Ganduglia-Pirovano | A. Martı́nez-Arias
Source: ACS Catalysis (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Fine-Tuning Catalysts: The Role of Support Nanomorphology in Shaping Cu/CeO <sub>2</sub> CO-PROX Properties Understanding how oxide nanomorphology directs metal-support interactions is key to designing selective, low-cost catalysts. The preferential oxidation of CO (CO-PROX) is vital for purifying H<sub>2</sub> streams for fuel cell applications, as even trace amounts of CO strongly poison the electrode catalysts. Cu/CeO<sub>2</sub> systems provide a cost-effective alternative to noble metals, yet the influence of ceria morphology on the performance remains unclear. Here, we compare low-loaded Cu catalysts supported on CeO<sub>2</sub> nanospheres and nanocubes. Although distinct in shape, electron microscopy, low-temperature CO adsorption infrared spectroscopy, and DFT calculations reveal surface reconstructions in nanocubes that diminish structural differences between the two supports. Nevertheless, the Cu/nanosphere catalyst shows higher CO oxidation activity, while the Cu/nanocube catalyst offers superior CO<sub>2</sub> selectivity and a broader full-conversion temperature window. In situ DRIFTS and DFT spectra attribute these contrasts to stronger CO adsorption sites in the nanocube system. Copper speciation and the nature of surface carbonyls were resolved through complementary techniques, including STEM-HAADF imaging, XEDS mapping, EPR, and CO adsorption IR spectroscopy, together with DFT. These results demonstrate that subtle variations in ceria morphology steer interfacial chemistry and reaction pathways, providing design principles for next-generation Cu-based catalysts for CO-PROX and related oxidation reactions. Catalysis ; Adsorption ; Selectivity ; Oxide ; Heterogeneous catalysis ; Copper ; Noble metal ; Redox ; Infrared spectroscopy Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysts for Methane Reforming
Authors: Philip Lenz | Sophia-M. Meinert | Sandra König | Michael Steiger | Michael Fröba
Source: The Journal of Physical Chemistry C (article, 2025-06-27)
Field: Materials Science — Materials Chemistry

Influence of Surface Chemistry on <i>t</i>-Layer Dimension and Density of Nanoconfined Water by Fine-Tuning the Functional Groups of Periodic Mesoporous Organosilicas This study investigates the behavior of water confined in cylindrical nanopores with hydrophilic, charged, and hydrophobic surfaces using water vapor sorption at 298.15 K. MCM-41 silica and periodic mesoporous organosilicas (PMOs) serve as host materials, with PMOs featuring a silica backbone linked by organic bridging units, allowing precise control over surface chemistry. Using a consistent divinylbenzene-based structure, the study assesses how functional groups influence confined water properties. A novel hydrophilicity index is introduced, incorporating contact angle data and adsorption layer thickness to quantify surface chemistry effects. Hydrophilic materials exhibit distinct adsorption profiles compared to hydrophobic PMOs, where surface chemistry and confinement influence water density, adsorption behavior, and t-layer proportions. In hydrophobic materials, such as divinylbenzene- and N,N-dimethyldivinylaniline-bridged PMOs, thicker water adsorption layers with lower average density are observed, which can be attributed to the localization of water near silanol groups and its absence near hydrophobic regions. Fluorinated PMOs fluctuate between hydrophobic and hydrophilic tendencies depending on water saturation. Introduction of surface charges enhances water adsorption, leading to a larger t-layer and higher adsorbed water density. These findings provide critical insights into how surface chemistry and pore size collectively govern water behavior, offering a framework for future studies on nanoconfined water. Mesoporous organosilica ; Dimension (graph theory) ; Chemistry ; Layer (electronics) ; Mesoporous material ; Chemical engineering ; Surface (topology) ; Organic chemistry ; Mesoporous silica ; Catalysis ; Mathematics ; Geometry ; Pure mathematics Mesoporous Materials and Catalysis ; Analytical Chemistry and Chromatography ; Chemical and Physical Properties in Aqueous Solutions