jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jun Ai | Xiang Zhang | Xiaofan Zhang | Jiaqi Wei | T. Zhang | Yaoliang Deng | Pu‐Kun Liu | Zan Nie | Yi Chen | Ning Dong | Zhiqiang Gao | Siqi Sun | Tiannan Guo
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-26)
Field: Chemistry — Spectroscopy

MassNet: billion-scale AI-friendly mass spectral corpus enables robust <i>de novo</i> peptide sequencing Abstract Breakthroughs in artificial intelligence (AI) for natural language processing and computer vision have been largely driven by high-quality, large-scale datasets such as OpenWebText and ImageNet. Inspired by this, we present MassNet, a foundational resource for proteomics designed to accelerate deep learning applications. MassNet is the largest known corpus of data-dependent acquisition (DDA) mass spectrometry (MS) data, derived from ~30 TB of raw files and comprising 1.54 billion MS/MS spectra, resulting in 558 million peptide-spectrum matches (PSMs) across 35 species, including animals, plants, and microbes. Within the human subset, MassNet includes more than 1.7 million precursors and 19,966 proteins, covering 98% of annotated human proteins. To enable efficient AI training, we developed the Mass Spectrometry Data Tensor (MSDT), a structured format based on Parquet that enables standardized, high-performance batch access and seamless integration with GPU and TPU platforms for distributed training. We further extended MassNet to support de novo peptide sequencing, which infers peptide sequences directly from MS/MS spectra without reference databases, and is critical for discovering novel proteins, characterizing non-model organisms, and identifying post-translational modifications (PTMs). We introduce XuanjiNovo, a non-autoregressive Transformer model that leverages a curriculum learning strategy to enhance training stability. By dynamically adjusting learning difficulty based on model performance, XuanjiNovo achieves smooth convergence on complex, multi-distributional data without manual hyperparameter tuning. Trained on 100 million PSMs from the MassNet, it consistently outperforms state-of-the-art methods across diverse benchmarking tasks. Peptide recall exceeds 0.8 on the Bacteroides thetaiotaomicron and Zea mays datasets. On human data acquired using the Orbitrap Astral platform, XuanjiNovo achieves achieves 38.8% to 144.3% improvement over existing models. MassNet represents the first large-scale, standardized foundational dataset in proteomics, marking a critical milestone in the integration of artificial intelligence into proteomics research. Scale (ratio) ; Environmentally friendly ; Computer science ; Computational biology ; Biology ; Geography ; Cartography ; Ecology Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Machine Learning in Bioinformatics
Authors: Paula Giarolla Silveira | Carlos Ramon de Paula Silva | José Luís Contado | V. Anjos | Maria José Valenzuela Bell | Amanda Aparecida de Lima Santos | Roney Alves da Rocha
Source: Brazilian Journal of Development (article, 2025-01-10)
Field: Chemistry — Analytical Chemistry

Fourier Transform Near-Infrared Spectroscopy (FT-NIR(S)) and artificial neural network for the quantitative detection of grape juice adulteration by the addition of apple juice Grape juice is popular for its taste and nutritional value, but due to its high commercial value, it is sometimes adulterated with apple juice, which contains naturally occurring sorbitol. This study used Fourier Transform Near-Infrared Spectroscopy (FT-NIR) alongside Artificial Neural Networks (ANN) to detect sorbitol in grape juice, aiming to identify adulteration. Two key NIR spectral bands, 12400–7400 cm−1 and 6400–5500 cm−1, were strongly correlated with sorbitol concentration in adulterated samples. The combination of FT-NIR and ANN provided accurate quantification of sorbitol levels, demonstrating that this method is a promising tool for the rapid detection of adulteration in grape juice with apple juice. Sorbitol ; Fruit juice ; Food science ; Chemistry ; Near-infrared spectroscopy ; Taste ; Fourier transform infrared spectroscopy ; Mathematics ; Chromatography ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Fritz Deufel | Manuel van Gemmeren
Source: ChemRxiv (preprint, 2025-04-10)
Field: Chemistry — Organic Chemistry

Ligand Controlled Regioselectivities in C–H Activation - A Case Study on Approaches for the Rationalization and Prediction of Selectivities Achieving regioselectivity in C–H activation remains a major challenge. Controlling or even reversing the regioisomeric outcome through the choice of ligand is even harder to achieve let alone to predict. Herein we investigated ligand effects in the regioselective Pd-catalyzed alkynylation of thiophenes using multivariate linear regression to build predictive models that offer deeper insight into the structural factors driving regioselectivity and enabled the discovery of a more selective ligand. Combining experimental and DFT studies we propose a Curtin-Hammett scenario between the C–H activation and migratory insertion as origin of the ligand-controlled selectivities. A detailed investigation of solvent effects uncovered an inadequate description of solvent-solute dispersion interactions by implicit solvation models. The crucial role of the solvent in substrate coordination is further evidenced by an inverse solvent kinetic isotope effect. Additionally, the often poorly understood role of silver was scrutinized, showing that it serves to mitigate a detrimental side reaction. This study provides generalizable insights for the computational description of challenging regio- and stereoselectivities and is expected to aid future mechanistic investigations in Pd-catalyzed C–H activation that holistically consider the role of all reaction components. Rationalization (economics) ; Ligand (biochemistry) ; Chemistry ; Computer science ; Combinatorial chemistry ; Computational chemistry ; Economics ; Receptor ; Biochemistry ; Microeconomics Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Cyclopropane Reaction Mechanisms
Authors: Jun Lan | Jian Han | Lisong Zhu | Jingwen Song | Meiqing Meng | Zhengyi Jiang
Source: Metals (article, 2025-05-30)
Field: Materials Science — Metals and Alloys

Optimization of Forming Parameters and Forming Strategy for Stamping of Novel Ultra-Thin Super Ferritic Stainless Steel Bipolar Plates Based on Numerical Simulation This study investigates the forming process (stamping) of bipolar plates which have applied a novel ultra-thin (0.1 mm) super ferritic stainless steel, i.e., SUS470, whose chromium is sufficiently high for corrosion resistance. A three-dimensional finite element model of the stamping process was developed using the commercial software ABAQUS version 2022. The model incorporated optimized die parameters obtained through Central Composite Design (CCD). This model was used to analyze the effects of key forming parameters, including stamping speed and friction coefficient, on the distribution of stress, strain, and thickness reduction during the stamping process. The finite element modeling (FEM) results disclose that the inner corner of the flow channel is a critical defect-prone region, exhibiting stress concentration, uneven strain distribution, and severe thinning. The optimal forming quality can be achieved at a stamping speed of 100 mm/s and a friction coefficient of 0.185 among all varied options. Further, a comparative study of single-stage, conventional two-stage, and optimized two-stage stamping strategies based on previous investigation demonstrates that the optimized two-stage stamping process can effectively alleviate stress and strain concentrations at the corners, significantly reduce thinning problems, and enhance the uniformity and stability during stamping. In summary, this study provides theoretical support for the design of the forming process (stamping) of high-performance super ferritic stainless steel bipolar plates, which is beneficial to subsequent practical engineering application. Stamping ; Materials science ; Finite element method ; Forming processes ; Hot stamping ; Die (integrated circuit) ; Computer simulation ; Stress (linguistics) ; Structural engineering ; Composite material ; Metallurgy ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Advanced Machining and Optimization Techniques
Authors: Hanna Tsybenko | Sarath Menon | Fei Chen | Abril Azócar Guzmán | Katharina Grünwald | Steffen Brinckmann | Tilmann Hickel | Tim Dahmen | Volker Hofmann | Stefan Sandfeld | Ruth Schwaiger
Source: Data Science Journal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Digital Transformation in Materials Science: A User Journey of Nanoindentation, Image Analysis and Simulations A robust digital infrastructure, built upon overarching frameworks and software tools, is essential for the ongoing digital transformation in materials science and engineering. This user journey demonstrates the seamless integration of distinct technical solutions for data handling and analysis, enabling (a) the pursuit of a specific scientific question and (b) adherence to FAIR principles. The scientific study selected for this user journey focuses on comparing different measures of the elastic modulus of a typical engineering material. The user journey involves three research groups replicating real-world collaborative research scenarios. Specifically, it integrates existing digital solutions for experimental data management (PASTA-ELN), simulation workflow execution (pyiron), and image processing workflow execution (Chaldene). Within the auxiliary data management workflow, generated data and metadata are systematically stored in repositories, with metadata aligned to the MatWerk Ontology. Key insights from this user journey include lessons learned from scientists’ perspectives and recommendations for improvement, such as machine-readable experimental protocols, standardized workflow representation, and automated metadata extraction. Workflow ; Metadata ; Metadata modeling ; Digital transformation ; Key (lock) ; Metadata management ; Transformation (genetics) ; Software ; Data management Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management
Authors: Murugesan Palaniappan | Sivasubramanian Palanisamy | Borhen Louhichi | Nadir Ayrılmış | Thulasi Mani Murugesan
Source: BioResources (article, 2025-08-14)
Field: Materials Science — Polymers and Plastics

Environmentally friendly composites from agricultural residue biomass for lightweight applications in new generation structures: A review The increasing global demand for sustainable materials has spurred extensive research into biopolymer-based composites derived from agricultural residue biomass. These materials offer an eco-friendly alternative to petroleum-based composites, addressing environmental pollution, resource depletion, and the need for low-density materials in sectors such as automotive, aerospace, packaging, and construction. This research focused on low-density bio-based composites as sustainable options for lightweight applications in automotive, aerospace, packaging, and construction. It highlights the use of agricultural residue and discontinuous binder systems to reduce density, as well as manufacturing techniques that improve structural efficiency. It emphasizes critical composite properties such as mechanical strength, thermal behavior, water resistance, biodegradability, and lightweight characteristics. The influence of fiber content and processing parameters on overall performance is also discussed. In addition, the review highlights major challenges, including scalability, cost-effectiveness, and long-term stability and proposes future research directions focused on durability enhancement, production efficiency, and commercial viability. Overall, this work underscores the transformative potential of agricultural residue-derived bio composites in advancing sustainable, high-performance materials for lightweight and eco-conscious construction and industrial applications. Environmentally friendly ; Materials science ; Residue (chemistry) ; Composite material ; Biomass (ecology) ; Agriculture ; Agronomy ; Organic chemistry ; Chemistry Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Feng Wang | Vladislav Vasilyev
Source: Molecules (article, 2025-08-29)
Field: Chemistry — Spectroscopy

Fe 3d Orbital Evolution in Ferrocene Ionization: Insights from ΔSCF, EOES, and Orbital Momentum Distribution The ionization of ferrocene (Fc) remains an active topic of interest due to its complex, ulti-electron character. Accurate prediction of its first ionization potential (IP) requires methods that go beyond single-particle approximations, as Koopmans' theorem, Janak's theorem, and the outer valence Green function (OVGF) approach prove inadequate. Using the ΔSCF method, the first IP of Fc was calculated to be ~6.9 ± 0.1 eV, which is in close agreement with experimental values (6.72-6.99 eV). To benchmark computational accuracy, 42 models were evaluated using the CCSD, CCSD(T), and B3LYP methods with Pople and Dunning basis sets, including Fe-specific modifications to better capture 3d electron behavior. The results underscore the importance of proper treatment of Fe 3d orbitals, with B3LYP/m6-31G(d) offering the best compromise between accuracy and computational efficiency. Notably, the singly occupied molecular orbital (SOMO) in Fc<sup>+</sup> is identified as the 8a<sub>1</sub>' orbital, which is dominated by its Fe 3d character. This orbital, although not the α-HOMO in Fc<sup>+</sup>, becomes the LUMO upon ionization. Analysis of the excess orbital energy spectrum (EOES) reveals substantial energy shifts upon ionization, particularly in Fe-centered orbitals spanning both the core and valence regions. Theoretical momentum distribution (TMD) analysis of the 8a<sub>1</sub>' orbital further quantifies orbital differences before and after ionization, providing complementary insights in momentum space. Finally, energy decomposition analysis (EDA) shows that while most interaction energy components become less stabilizing upon ionization, steric and Pauli terms contribute a small stabilizing effect. Ferrocene ; Non-bonding orbital ; Ionization ; Molecular orbital ; Chemistry ; Momentum (technical analysis) ; Physics ; Distribution (mathematics) ; Atomic physics ; Computational chemistry ; Physical chemistry ; Quantum mechanics ; Mathematics ; Molecule ; Ion Mass Spectrometry Techniques and Applications ; Molecular Junctions and Nanostructures ; Advanced Chemical Physics Studies
Authors: D. Lita Pansy | M. Murali
Source: Journal of Computer Science (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

An Accurate Mango Pest Identification Employing the Gaussian Mixture Model and Expectation-Maximization (EM) Algorithm Mangoes are fruits that originated in South and Southeast Asia. Mango fruits are consumed in high volume worldwide. However, fruit, stem, root, and mango leaves are damaged by pests, and it has a significant negative impact on mango production. Many works were developed for the pest detection of mango; but, none of the works concentrated on the practical applicability and learning efficiency of the model. Furthermore, the boundary features of the leaf regions were not analyzed in depth for accurate identification of different mango pests. Therefore, in this study, Machine Learning (ML) algorithms are used to assess mango fields and identify pests to address the requirement for an early-stage pest identification system. This study presents a novel method for mango plant disease and pest identification and classification using a combination of machine learning, IoT, computer vision, and drone technology. The proposed system is designed to analyze large mango fields and detect biological threats in an early stage and the problems faced by farmers in mango crops. The system utilizes a Dense Net architecture for feature extraction and a custom Corner Net model employing the Gaussian Mixture Model and Expectation Maximization (GMM-EM) algorithm for effective pest classification. The proposed system is tested using the IP102 dataset, which is a large challenging benchmark database for pest identification. The experimental results represented the enhanced pest detection of mango crops with an accuracy of 89.90%, precision of 79.94%, recall of 75.82%, and F1-score of 77.80% by using the proposed model. Furthermore, the proposed system is robust and can reliably locate and categorize different pests even in complicated backgrounds and various insects and their color, size, and brightness. The proposed approach can also send SMS notifications to farmers concerning diseases and pests. Computer science ; Expectation–maximization algorithm ; Identification (biology) ; Mixture model ; Maximization ; Algorithm ; PEST analysis ; Gaussian ; Machine learning ; Artificial intelligence ; Mathematical optimization ; Maximum likelihood ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Moisés Domínguez | Verónica A. Jiménez | Gökçen Savaşçı | Rocío Araya‐Osorio | Janne Pesonen | Raúl Mera‐Adasme
Source: Journal of Computational Chemistry (article, 2025-01-11)
Field: Materials Science — Materials Chemistry

<scp>goChem</scp> : A Composable Library for Multi‐Scale Computational Chemistry Data Analysis Data analysis is a major task for Computational Chemists. The diversity of modeling tools currently available in Computational Chemistry requires the development of flexible analysis tools that can adapt to different systems and output formats. As a contribution to this need, we report the implementation of goChem, a versatile open-source library for multiscale analysis of computational chemistry data. The library, written in and for the Go programming language, allows for easy integration of different levels of theory, in an easy-to-use API, allowing the development of both one-use and complex analysis programs in Go. We describe the library and detail some selected applications that illustrate the capabilities and potential of this tool. The library is available at http://gochem.org. Computer science ; Task (project management) ; Scale (ratio) ; Data science ; Software engineering ; Systems engineering ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Zeolite Catalysis and Synthesis
Authors: Cansu Var | Barış Hasçelik | Sema Palamutcu
Source: Uludağ University Journal of The Faculty of Engineering (article, 2025-12-11)
Field: Materials Science — Polymers and Plastics

CURRENT PRACTICES, TRENDS, AND DEVELOPMENTS IN LABORATORY-TYPE WET SPINNING TECHNOLOGIES The increasing interest in wet-spun filaments has also sparked enthusiasm for custom-designed wet spinning setups. Tailored laboratory systems are valuable both for exploring novel polymer compositions and for modifying the setup components in the future. In this study, a concise outline of current practices, trends, and developments in laboratory-type wet spinning technologies is provided. Also, a custom-designed laboratory-scale wet spinning setup is introduced in detail. The constructed setup combines the controllable syringe, traveller, and winding unit via a single digital interface. The modular needle holder of the setup provides air gap spinning and multiple injection angles. The speeds of both the traveller, which regulates filament winding, and the winding cylinder can be adjusted via the touchscreen monitor. Such an integrated, complex, and modular system enables precise control of process parameters. Also, it is possible to precisely examine the effects of process-related parameters on the filament properties. Moreover, multiple control systems with a single interface provide the ability to draw filaments under controlled tension. This system further offers an adaptable framework for new developments, such as data monitoring and module integration, offering considerable versatility and cost advantages in both research and production processes. Spinning ; Modular design ; Current (fluid) ; Interface (matter) ; Touchscreen ; Process (computing) ; Control system ; Digital control Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Nanofabrication and Lithography Techniques
Authors: 吴思鹏 Wu Sipeng | 陈伟 Chen Wei
Source: Laser & Optoelectronics Progress (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

掺铋光纤及应用研究进展 The construction of computing power facilities requires optical fiber communication networks with high bandwidth, large capacity, and low time delay.However, optical fiber amplifiers with broadband gain, as key materials for relay amplification in optical transmission links, urgently need to be developed.Ultra -wideband gain optical fibers and their optical amplifiers have become research hotspots internationally.This paper elaborates in detail the luminescence characteristics of bismuth ions in different valence states and the research progress of bismuth -doped ultra -wideband gain fibers at home and abroad.It focuses on summarizing the preparation methods of bismuth -doped quartz fibers, the fiber amplification architecture, as well as their research progress in the fields of amplifiers and lasers. Computer science Luminescence Properties of Advanced Materials ; Glass properties and applications ; Catalysis and Oxidation Reactions
Authors: Chung Xu | Richard Schier | Caterina Cocchi
Source: Electronic Structure (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

Electronic and optical properties of computationally predicted Na–K–Sb crystals Abstract Thanks to their favorable electronic and optical properties, sodium–potassium-antimonides are an emerging class of crystals used as photocathodes in particle accelerators. The persisting challenges related to the synthesis and characterization of these materials demand support from theory and make the study of computationally predicted polymorphs particularly relevant to identifying the structure, composition, and properties of the samples. Using first-principles methods based on density-functional theory and many-body perturbation theory, the electronic and optical properties of cubic NaK 2 Sb and hexagonal Na 2 KSb are studied. Both systems, experimentally reported in the hexagonal and cubic phase, respectively, exhibit an indirect fundamental gap that is energetically very close to the direct band gap at Γ of magnitude <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mn>0.81</mml:mn> </mml:mrow> <mml:mstyle scriptlevel="0"/> <mml:mtext>eV</mml:mtext> </mml:mrow> </mml:math> for NaK 2 Sb and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mn>0.70</mml:mn> </mml:mrow> <mml:mstyle scriptlevel="0"/> <mml:mtext>eV</mml:mtext> </mml:mrow> </mml:math> for Na 2 KSb. In the band structure of both materials, Sb p -states dominate the valence region with minor contributions from the alkali p -states, while the alkali s -states mainly contribute at lower energies. The optical spectra of both crystals are not subject to sizeable excitonic effects, except for a redshift of the excitation energies of 50–100 meV and some redistribution of the oscillator strength beyond the lowest-energy peak in the near-infrared region. Our results indicate that computationally predicted cubic NaK 2 Sb and hexagonal Na 2 KSb have favorable characteristics as photocathodes and, as such, their presence in polycrystalline samples is not detrimental for this application. Materials science ; Crystallography ; Optoelectronics ; Chemistry Solid-state spectroscopy and crystallography ; Thermodynamic and Structural Properties of Metals and Alloys