jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simon D. Rihm | Dan Tran | Aleksandar Kondinski | Laura Pascazio | Fabio Saluz | Xinhong Deng | Sebastian Mosbach | Jethro Akroyd | Markus Kraft
Source: Data-Centric Engineering (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Natural language access point to digital metal–organic polyhedra chemistry in The World Avatar Abstract Metal–organic polyhedra (MOPs) are discrete, porous metal–organic assemblies known for their wide-ranging applications in separation, drug delivery, and catalysis. As part of The World Avatar (TWA) project—a universal and interoperable knowledge model—we have previously systematized known MOPs and expanded the explorable MOP space with novel targets. Although these data are available via a complex query language, a more user-friendly interface is desirable to enhance accessibility. To address a similar challenge in other chemistry domains, the natural language question-answering system “Marie” has been developed; however, its scalability is limited due to its reliance on supervised fine-tuning, which hinders its adaptability to new knowledge domains. In this article, we introduce an enhanced database of MOPs and a first-of-its-kind question-answering system tailored for MOP chemistry. By augmenting TWA’s MOP database with geometry data, we enable the visualization of not just empirically verified MOP structures but also machine-predicted ones. In addition, we renovated Marie’s semantic parser to adopt in-context few-shot learning, allowing seamless interaction with TWA’s extensive MOP repository. These advancements significantly improve the accessibility and versatility of TWA, marking an important step toward accelerating and automating the development of reticular materials with the aid of digital assistants. Avatar ; Polyhedron ; Point (geometry) ; Natural (archaeology) ; Chemistry ; Computer science ; Human–computer interaction ; Mathematics ; Geography ; Combinatorics ; Archaeology Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Christophe Bonneville | Nathan Bieberdorf | Arun Hegde | Mark Asta | Habib N. Najm | Laurent Capolungo | Cosmin Safta
Source: npj Computational Materials (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone Abstract Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10 −12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes. Artificial neural network ; Computer science ; Fourier transform ; Bottleneck ; Fast Fourier transform ; Operator (biology) ; Partial differential equation ; Algorithm ; Phase space ; Artificial intelligence ; Applied mathematics ; Mathematics ; Mathematical analysis ; Physics Nanoporous metals and alloys ; Solidification and crystal growth phenomena ; Advanced Mathematical Modeling in Engineering
Authors: Lingling Ma | Wenjing Li | Jian Yuan | Jian Zhu | Yan Wu | Hanliang He | Xiangqiang Pan
Source: Macromolecular Rapid Communications (review, 2025-07-07)
Field: Materials Science — Materials Chemistry

Recent Advances in Machine Learning‐Assisted Design and Development of Polymer Materials The traditional research paradigm for polymer materials relies heavily on time-consuming and inefficient trial-and-error methods, which are no longer sufficient to meet the demands of modern research and development. With the rapid advancement of big data and artificial intelligence technologies, machine learning has emerged as a powerful data analysis tool, revolutionizing polymer material research and development. This paper provides an overview of machine learning techniques, summarizes common machine learning algorithms, and reviews recent progress in machine learning-assisted polymer material design and development. Key areas include polymer sequence design, material property prediction, classification and identification, and applications leveraging computer vision technologies. Furthermore, this study discusses several critical challenges currently faced by the field and offers perspectives on future directions . Computer science ; Field (mathematics) ; Artificial intelligence ; Big data ; Hyper-heuristic ; Identification (biology) ; Data science ; Machine learning ; Key (lock) ; Systems engineering ; Engineering ; Data mining ; Robot learning Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer composites and self-healing
Authors: Qinggong Wang | Wei Zhang | Mingan Chen | Xutong Li | Zhaoping Xiong | Jiacheng Xiong | Zunyun Fu | Mingyue Zheng
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

NMRExtractor: leveraging large language models to construct an experimental NMR database from open-source scientific publications Nuclear magnetic resonance (NMR) spectroscopy is crucial for elucidating molecular structures, but NMR data extraction remains largely manual and time-consuming. We developed NMRExtractor, a locally deployable tool using a fine-tuned large language model, to address this challenge. By processing 5 734 869 open-source scientific publications, we created NMRBank, a dataset containing 225 809 entries with compound IUPAC names, NMR conditions, <sup>1</sup>H and <sup>13</sup>C NMR chemical shifts, data confidence levels, and reference information. Our analysis reveals that NMRBank's chemical space significantly surpasses existing public NMR datasets. The extraction process is highly scalable, allowing automatic processing of new research papers and continuous updates to NMRBank. This approach not only expands the available open NMR data space but also provides a foundation for AI-based NMR predictions and related chemical research. By automating data extraction and creating a comprehensive, regularly updated NMR database, NMRExtractor and NMRBank address the scarcity of publicly available experimental NMR data, potentially accelerating progress in various fields of chemical research. Construct (python library) ; Pipeline (software) ; Computer science ; Open source ; Open data ; Information retrieval ; Database ; World Wide Web ; Programming language ; Software Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Andrea Martelli | Devis Bellucci | Valeria Cannillo
Source: Polymers (review, 2025-10-02)
Field: Materials Science — Materials Chemistry

The Role of Artificial Intelligence in Biomaterials Science: A Review Biomaterials can be defined as materials that interact positively with living tissues, restoring compromised functions, or enhancing tissue regeneration. Currently, biomaterial research often relies on a "trial-and-error method", involving numerous experiments driven largely by experience. This strategy leads to a substantial waste of resources, such as manpower, time, materials, and finances. Optimizing the process is therefore essential. A recent and promising approach to this challenge involves artificial intelligence (AI), as demonstrated by the growing number of studies in this field. AI algorithms rely on data and empower computers with decision-making capabilities, mimicking aspects of the human mind and solving complex tasks with little to no human intervention. Due to their potential, AI and its derivatives are now widely used both in everyday life and in scientific research. In biomaterials science, AI models enable data analysis, pattern recognition, and property prediction. The aim of this review article is to highlight the key results achieved through the application of AI in the field of polymers for biomedical applications and, more broadly, in the development of advanced biomaterials. An overview will be provided on how an AI algorithm works, the differences between traditional programming and AI-based approaches, and their main limitations. Finally, the core topic will be addressed by categorizing biomaterials according to material class. Field (mathematics) ; Applications of artificial intelligence ; Process (computing) ; Biomaterial ; Key (lock) ; Artificial life Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced X-ray and CT Imaging
Authors: Gehad Y Abo El-Reesh | M. A. Azooz | M. A. Ouis | Amira A. Gamal | Reham M. M. Morsi | S. M. Abbas | S. Abd Elkhalik
Source: Scientific Reports (article, 2025-06-03)
Field: Materials Science — Ceramics and Composites

TiO2-doped borate glass and glass-ceramic: properties and prospects for biological and electrical applications This study explores the synthesis, characterization, and potential applications of TiO<sub>2</sub>-doped borate glasses and their glass-ceramics, focusing on their biological and electrical properties. Examining the impact of varying the TiO<sub>2</sub> content on the structural, electrical, and antimicrobial properties of the prepared samples was done. X-ray diffraction, Fourier-transform infrared spectroscopy (FTIR), density, and Field emission scanning electron microscope (FESEM) were employed to analyze the material's structural integrity and phase transitions. The AC conductivity (σ<sub>ac</sub>) was measured within the frequency range of 0.042 kHz-1 MHz and at the temperature range of 298-573 (K). The estimated DC conductivity proved that incorporating of TiO<sub>2</sub> at the expense of BaO results in higher conductivity values than those of the free glass and glass ceramic samples. The prepared samples exhibited a semiconducting nature. The dielectric constant (ɛ') values increase upon doping with TiO<sub>2</sub>. The incorporation of TiO<sub>2</sub> improved the bioactivity (antimicrobial) of the studied glasses, making it suitable for biomedical applications such as drug delivery and tissue engineering. Also, the long-term stability and cytotoxicity were evaluated. The results indicate that TiO<sub>2</sub>-doped borate glasses and glass-ceramics present a promising avenue for the development of multifunctional materials that meet the demands of both biological and electrical applications. Doping ; Boron ; Ceramic ; Materials science ; Glass-ceramic ; Borate glass ; Composite material ; Engineering physics ; Mineralogy ; Optoelectronics ; Chemistry ; Physics Glass properties and applications ; Luminescence Properties of Advanced Materials ; Bone Tissue Engineering Materials
Authors: Qichang Zhao | Haochen Zhao | Linyuan Guo | Kai Zheng | Yajie Li | Qiao Ling | Jing Tang | Yaohang Li | Jianxin Wang
Source: Nature Communications (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

ColdstartCPI: Induced-fit theory-guided DTI predictive model with improved generalization performance Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability compared to structure-based methods in virtual screening. ColdstartCPI also excels in sparse and low-similarity data conditions, demonstrating its potential in data-limited settings. Our results are validated through literature search, molecular docking, and binding free energy calculations. Overall, ColdstartCPI offers a perspective on sequence-based drug design, presenting a promising tool for drug discovery. Generalization ; Computer science ; Artificial intelligence ; Econometrics ; Machine learning ; Mathematics Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Neural Networks and Applications
Authors: Yucheng Zhu | Xueliang Zhu | Xuhai Pan | Lian Liu | Madeleine Bussemaker
Source: RSC Mechanochemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Correlation of sonochemical activities measured <i>via</i> dosimetry and an area-selective analysis of sono(chemi)luminescence A systematic study using new image processing methods quantified different types of sonochemical activity ( i.e. S(C)L and KI dosimetry). Interrelations and distinctions were explored to enhance understanding of sonochemical mechanisms. Luminescence ; Materials science ; Dosimetry ; Optically stimulated luminescence ; Analytical Chemistry (journal) ; Optoelectronics ; Chemistry ; Nuclear medicine ; Medicine ; Environmental chemistry Ultrasound and Cavitation Phenomena ; Advanced Chemical Sensor Technologies ; Radiation Effects and Dosimetry
Authors: Seung-Ki Lee | Rowoon Park | Ho Sang Jung
Source: Advanced Intelligent Discovery (article, 2025-06-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing Surface‐enhanced Raman scattering (SERS) captures intrinsic vibrational signatures of biomolecules, allowing for label‐free detection with high specificity and sensitivity. This makes SERS particularly advantageous for analyzing biofluids, tissues, and cellular environments. However, spectral complexity, signal variability, and overlapping molecular signatures pose challenges in interpretation. To overcome these challenges, artificial intelligence (AI) enhances SERS analysis by automating spectral processing, improving classification, and refining imaging, enabling highly sensitive target detection, molecular interaction validation, and clinical diagnostics. This review explores AI‐enhanced SERS (AI‐SERS) applications in disease screening, biomolecular interaction analysis, and imaging‐based diagnostics, highlighting its potential for clinical translation. By leveraging AI‐driven spectral interpretation, AI‐SERS extracts molecular signatures, quantifies biomolecular interactions, and visualizes spatial distributions, providing diagnostic insights beyond conventional identification. Furthermore, we discuss key considerations for enhancing analytical reliability, optimizing spectral feature extraction, and integrating AI‐SERS into real‐world biomedical applications, outlining future directions to advance its role in medical diagnostics and therapeutic monitoring. By refining the synergy between AI and SERS, future advancements will drive innovations in next‐generation disease monitoring, drug discovery, and medical assistance. Raman scattering ; Materials science ; Surface (topology) ; Nanotechnology ; Scattering ; Raman spectroscopy ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Michelle C. Lallawmkimi | Soumya Patil | D. K. Upadhyay | N. Majumdar | B. Abinaya | Gagan Kumar | Chandan Kumar Panigrahi
Source: Archives of Current Research International (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Application of Nanotechnology in Agriculture: Opportunities and Challenges in the Context of Environmental Sustainability Nanotechnology is an emerging field with immense potential to revolutionize agriculture by enhancing productivity, resource efficiency, and sustainability. Its applications span diverse areas, including nano-fertilizers for improved nutrient uptake, nano-pesticides for targeted pest control, and nanosensors for real-time monitoring of soil and crop health. These advancements address critical agricultural challenges, such as nutrient inefficiency, pest resistance, and environmental degradation. Nano-enabled smart delivery systems optimize the use of fertilizers and pesticides, reducing environmental contamination and improving yield. Nanotechnology also facilitates soil remediation, enhances microbial health, and provides innovative water purification techniques, making it instrumental in mitigating resource scarcity and pollution. Its role in precision agriculture, through IoT-integrated nanosensors, enables data-driven farming practices that improve decision-making and resource allocation. Despite its promise, the adoption of nanotechnology faces barriers, including potential toxicity of nanomaterials, long-term environmental risks, and concerns over food safety and human health. High production costs, limited scalability, and a lack of regulatory frameworks hinder widespread implementation, particularly in developing countries. Future research must focus on eco-friendly and biodegradable nanomaterials, scalable manufacturing methods, and the development of cost-effective technologies accessible to smallholder farmers. Robust policy frameworks, standardized safety guidelines, and public-private collaborations are essential to ensure the safe and ethical integration of nanotechnology in agriculture. Equally important are educational initiatives to enhance farmer awareness and build public trust in these innovations. By addressing these challenges, nanotechnology can play a transformative role in creating a sustainable, climate-resilient agricultural system capable of meeting the demands of a growing global population while protecting environmental and human health. Sustainability ; Business ; Context (archaeology) ; Agriculture ; Agricultural productivity ; Sustainable agriculture ; Environmental planning ; Environmental resource management ; Risk analysis (engineering) ; Environmental science ; Ecology Nanoparticles: synthesis and applications
Authors: Lijie Guo | Xiaoyu Hu | Wenhe Liu | Yang Liu
Source: Applied Sciences (article, 2025-06-05)
Field: Chemistry — Analytical Chemistry

Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis Automated food safety inspection systems rely heavily on the visual detection of contamination, spoilage, and foreign objects in food products. Current approaches typically require extensive labeled training data for each specific hazard type, limiting generalizability to novel or rare safety issues. We propose a zero-shot detection framework for visual food safety hazards that enables the identification of previously unseen contamination types without requiring explicit training examples. Our approach adapts and extends the Knowledge-Enhanced Feature Synthesizer (KEFS) methodology to the food safety domain by constructing a specialized knowledge graph that encodes visual safety attributes and their correlations with food categories. We introduce a Food Safety Knowledge Graph (FSKG) that models the relationships between 26 food categories and 48 visual safety attributes (e.g., discoloration, mold patterns, foreign material characteristics) extracted from food safety databases and expert knowledge. Using this graph as the prior knowledge, our system synthesizes discriminative visual features for unseen hazard classes through a multi-source graph fusion module and region feature diffusion model. Experiments on our newly constructed Food Safety Visual Hazards (FSVH) dataset demonstrate that our approach achieves 63.7% mAP in zero-shot hazard detection, outperforming state-of-the-art general zero-shot detection methods by 6.9%. Furthermore, our framework demonstrates robust generalization to fine-grained novel hazard categories while maintaining high detection performance (59.8% harmonic mean) in generalized zero-shot scenarios where both seen and unseen hazards may occur simultaneously. This work represents a significant advancement toward automated, generalizable food safety inspection systems capable of adapting to emerging visual hazards without a costly retraining process. Shot (pellet) ; Feature (linguistics) ; Zero (linguistics) ; Computer science ; Materials science Spectroscopy and Chemometric Analyses
Authors: Kiarash Farajzadehahary | Shaghayegh Hamzehlou | Nicholas Ballard
Source: Progress in Polymer Science (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Adding machine learning to the polymer reaction engineering toolbox Toolbox ; Flexibility (engineering) ; Key (lock) ; Field (mathematics) ; Perspective (graphical) ; Focus (optics) Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Fault Detection and Control Systems