jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ulloa, Jacinto | Stainier, Laurent
Source: ArXiv.org (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Material data identification in generalized continua We introduce a data-driven framework for identifying material behavior from full-field kinematics and force measurements in generalized (micromorphic) continua. Unlike traditional approaches that rely on constitutive assumptions or homogenization schemes, our method extracts generalized stress--strain data by enforcing non-classical balance laws and compatibility relations on full-field boundary value problems. Specifically, the approach infers the associated generalized stresses and constructs representative material datasets via clustering in a non-classical phase space. We show that the proposed method reliably extracts non-symmetric and higher-order local stress states, providing material data suitable for either model calibration or model-free data-driven simulations of generalized continua. These capabilities are demonstrated in validation simulations with synthetic data and in an application to mechanical metamaterials, suggesting a practical route for material characterization of microstructured solids. Homogenization (climate) ; Cluster analysis ; Compatibility (geochemistry) ; Kinematics ; Boundary value problem ; Experimental data ; Constitutive equation ; Calibration Nonlocal and gradient elasticity in micro/nano structures ; Composite Material Mechanics ; Model Reduction and Neural Networks
Authors: Junqi Zeng | Zelin Yao | Pengyang Song | Jia Wu | Wenbin Hu
Source:  (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Enhancing Template-Free Retrosynthesis Prediction with Cross-Modal Fine-Grained Contrastive Learning Retrosynthesis prediction aims to infer the precursor compounds and synthetic pathways for the given product molecule. Despite the emergence of numerous data-driven template-free approaches, most rely on single-modality inputs (e.g., SMILES or molecular graphs), limiting them to incorporate multimodal information. Besides, existing methods require complex alignment preprocessing for models to capture unaltered molecular structures in reactions, which rigidly restricts the data format and hinders generalizing to practical application scenarios. To address these challenges, this paper proposes a novel end-to-end template-free learning framework, named Cross-modal Fine-grained Contrastive Learning for Retrosynthesis Prediction (CFC-Retro). By taking both sequence and graph data as inputs, CFC-Retro leverages the cross-attention mechanism to effectively integrate multimodal information during encoding. Additionally, CFC-Retro employs multimodal hybrid-enhanced decoding to produce accurate results. Moreover, the contrastive learning strategy in CFC-Retro implicitly aligns multimodal features at the atomic level. This strategy guides the model to perceive molecular structural changes in chemical reactions, while exhibiting high flexibility. Experiments show that CFC-Retro achieves top-1 accuracy of 53.7% and validity of 99.7% on USPTO-50k with reaction class unknown, underscoring its robust performance in retrosynthesis prediction. Preprocessor ; Retrosynthetic analysis ; Limiting ; Class (philosophy) ; Mechanism (biology) ; Decoding methods ; Computational linguistics ; Encoding (memory) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Ratna Sari Listyaningrum | Prakoso Adi | Rizka Mulyani | Sheng-Yen Tsai | Bara Yudhistira | Chang‐Wei Hsieh
Source: Trends in Food Science & Technology (article, 2025-11-26)
Field: Materials Science — Biomaterials

Current status and future directions on cyclodextrin-based metal–organic frameworks for adsorption and controlled release of gaseous/volatiles in active food packaging Active packaging ; Food packaging ; Food spoilage ; Controlled release ; Adsorption ; Shelf life ; Biocompatibility Nanocomposite Films for Food Packaging ; Microencapsulation and Drying Processes ; Polymer Science and PVC
Authors: Ulloa, Jacinto | Stainier, Laurent
Source: arXiv (Cornell University) (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

Material data identification in generalized continua We introduce a data-driven framework for identifying material behavior from full-field kinematics and force measurements in generalized (micromorphic) continua. Unlike traditional approaches that rely on constitutive assumptions or homogenization schemes, our method extracts generalized stress--strain data by enforcing non-classical balance laws and compatibility relations on full-field boundary value problems. Specifically, the approach infers the associated generalized stresses and constructs representative material datasets via clustering in a non-classical phase space. We show that the proposed method reliably extracts non-symmetric and higher-order local stress states, providing material data suitable for either model calibration or model-free data-driven simulations of generalized continua. These capabilities are demonstrated in validation simulations with synthetic data and in an application to mechanical metamaterials, suggesting a practical route for material characterization of microstructured solids. Homogenization (climate) ; Cluster analysis ; Compatibility (geochemistry) ; Kinematics ; Boundary value problem ; Experimental data ; Constitutive equation ; Calibration Nonlocal and gradient elasticity in micro/nano structures ; Composite Material Mechanics ; Model Reduction and Neural Networks
Source: Journal of Physics Conference Series (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Preface It is with great pleasure that we present the proceedings of the International Conference on Next-Generation Materials and Devices (ICNMD 2024), held from August 1 to 3, 2024, at Kalasalingam Academy of Research and Education (KARE), Virudhunagar, Tamil Nadu, India. Organized by the Department of Physics, School of Advanced Sciences, the conference aimed to bring together researchers, academicians, and professionals to explore the latest developments in materials science and device technologies. ICNMD 2024 provided a dynamic platform to facilitate knowledge exchange, promote interdisciplinary research, and highlight emerging trends in areas such as optoelectronic materials, energy devices, ceramic materials, magnetic materials, quantum and nanomaterials, biomaterials, and AI-assisted materials design. The event was supported by the Science and Engineering Research Board (SERB) and the Tamil Nadu State Council for Science and Technology (TNSCST), whose sponsorship significantly contributed to the success of the conference. Over three days, the conference offered a rich blend of sessions, including plenary lectures, invited talks, oral and poster presentations, and special events designed to foster academic and industrial linkages. Notably, the Techno-Pitch Display provided a platform to showcase innovations with commercialization potential, while the Explore Innovation initiative welcomed students from neighbouring districts to engage with current research through curated exhibits and live demonstrations. List of Organizing Committees is available in this PDF. Commercialization ; Tamil ; Engineering ethics ; Engineering ; Library science ; Political science ; Computer science Machine Learning in Materials Science
Authors: Hyunseung Kim | Dae-Woong Jeong | Chang-Young Park | W. K. Lee | Haeun Lee | Jihye Lee | Rodrigo Hormázabal | Sung Moon Ko | Sumin Lee | Soorin Yim | Chanhui Lee | Sehui Han | Sang-Ho Cha | Woohyung Lim
Source: ArXiv.org (preprint, 2025-10-27)
Field: Materials Science — Materials Chemistry

Towards a Generalizable AI for Materials Discovery: Validation through Immersion Coolant Screening Artificial intelligence (AI) has emerged as a powerful accelerator of materials discovery, yet most existing models remain problem-specific, requiring additional data collection and retraining for each new property. Here we introduce and validate GATE (Geometrically Aligned Transfer Encoder) -- a generalizable AI framework that jointly learns 34 physicochemical properties spanning thermal, electrical, mechanical, and optical domains. By aligning these properties within a shared geometric space, GATE captures cross-property correlations that reduce disjoint-property bias -- a key factor causing false positives in multi-criteria screening. To demonstrate its generalizable utility, GATE -- without any problem-specific model reconfiguration -- applied to the discovery of immersion cooling fluids for data centers, a stringent real-world challenge defined by the Open Compute Project (OCP). Screening billions of candidates, GATE identified 92,861 molecules as promising for practical deployment. Four were experimentally or literarily validated, showing strong agreement with wet-lab measurements and performance comparable to or exceeding a commercial coolant. These results establish GATE as a generalizable AI platform readily applicable across diverse materials discovery tasks. Retraining ; Key (lock) ; False positive paradox ; Data collection ; Immersion (mathematics) Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Thermal properties of materials
Authors: Estrella Álvarez | Michael Gracey | Μ. M. Cowper | Quoc Tri Phung | B. Zlobenko | Federica Pancotti | Mafalda Guerra | M. Kala | Janno Torop | Alan H. Tkaczyk
Source: Nuclear Engineering and Design (article, 2025-10-08)
Field: Chemistry — Inorganic Chemistry

Advanced treatment strategies for challenging radioactive Wastes: Recent Developments and future directions • Detailed comparative analysis of treatment methods for diverse challenging wastes. • Case study insights illustrate upscaling obstacles and costly delays. • Challenges remain for scaling up, regulatory approval and stakeholder acceptance. • Harmonized and flexible regulations are needed to deploy new treatment solutions. • Hybrid and modular systems offer tailored solutions for diverse radioactive waste streams. The management of low- and intermediate-level radioactive waste (LILW) presents increasing challenges, particularly for waste streams with complex chemistries, variable radioactivity, or combined toxic and radiological risks. Conventional treatment methods, while effective for conventional waste types, are often inadequate for these problematic streams, which require advanced and tailored approaches. This review evaluates recent progress in thermal (pyrolysis, plasma processes, fluidized bed steam reforming, molten salt oxidation) and chemical technologies (wet and electrochemical oxidation) for treating such wastes. The novelty of this work lies in its integrated analysis, which combines comparative technology mapping with assessments of scalability, regulatory and societal acceptance, and the integration of lifecycle environmental and economic metrics. Cross-technology performance matrices are presented to support process selection, together with a strategic research roadmap that prioritizes secondary waste minimization, flexible acceptance criteria, and the inclusion of Life Cycle Assessment (LCA). The study also highlights future trends such as mobile modular treatment systems, hybrid technologies that integrate multiple treatment approaches, and the increasing use of artificial intelligence (AI) and Internet of Things (IoT) technologies for real-time monitoring and process optimization. Overall, this review aims to provide a practical framework to guide researchers, industry, and regulators toward safe, sustainable, and industrially viable solutions for complex radioactive waste management. Modular design ; Process (computing) ; Stakeholder ; Scope (computer science) ; Novelty ; Emerging technologies ; Automation ; Work in process Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Graphite, nuclear technology, radiation studies
Authors: Sharipov Nuriddin | Rajabov Farmanovich
Source: R E M (Rekayasa Energi Manufaktur) Jurnal (article, 2025-03-26)
Field: Materials Science — General Materials Science

Methods and Characteristics of Quality Control of Composite Materials In modern industry, the demand for high-quality and reliable composite materials is increasing day by day. Ensuring the required operational properties of these materials directly depends on the methods and characteristics of their quality control. This article provides detailed information on the main and general directions of modernization programs adopted in our country, including the improvement of standard requirements, innovative, convenient, and at the same time high-performance methods of quality control and testing of composite materials. In particular, special attention is paid to the introduction of modern equipment and technologies, as well as the effective use of local raw materials and production capabilities[1,4]. Today, the development and improvement of non-destructive testing methods, precision measurement technologies, and automatic quality control systems for composite materials is becoming increasingly important. The use of ultrasonic, radiographic, thermal, optical, and electromagnetic control methods makes it possible to detect internal defects in materials, evaluate their physical and mechanical properties, and monitor the stability of technological processes. In addition, the introduction of digital technologies, artificial intelligence, and modern software complexes ensures the objectivity and accuracy of control results[2,3]. The article also analyzes international experience in ensuring the quality and reliability of composite materials, the role of certification and standardization processes, and the importance of creating regulatory frameworks adapted to the requirements of global markets. As a result, the formation of a comprehensive quality control system covering all stages — from raw material selection to final product testing — is of great importance in increasing the competitiveness of domestic composite materials and expanding their application fields Composite number ; Quality (philosophy) ; Control (management) ; Environmental science ; Materials science ; Composite material ; Computer science ; Physics ; Artificial intelligence Material Properties and Applications
Authors: Muna A. Ali | Sanjay Gairola | Asma Alketbi | Fatima Alketbi | Rania Hamdy | Kareem A. Mosa | Ali El‐Keblawy
Source: Journal of Plant Research (review, 2025-10-13)
Field: Materials Science — Materials Chemistry

Emerging progress in investigating the impact of green-synthesized nanoparticles on seed germination Abiotic component ; Abiotic stress ; Germination ; Plant physiology ; Agriculture ; Drought tolerance ; Reactive oxygen species ; Biomass (ecology) ; Transcriptome Nanoparticles: synthesis and applications ; Seed Germination and Physiology
Authors: Ran Zhao | Xiao Ma | Xin Chang | Zhi‐Jian Zhao | Jinlong Gong
Source: The Journal of Physical Chemistry C (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Exploration of Factors Affecting C–H Bond Activation on BN-Supported Single-Atom Catalysts In this study, we explore key factors influencing the initial C–H bond activation step of propane dehydrogenation on boron nitride-supported single-atom catalysts (SACs) using density functional theory calculations. Transition state energy barrier calculations reveal distinct catalytic activities across different SACs. To explain these variations, the sure independence screening and sparsifying operator algorithm derives interpretable low-dimensional descriptors incorporating one or more of the following key features: electronegativity difference, Bader charge, and work function. These descriptors establish clear correlations between the electronic properties and activation energy, providing a computationally efficient framework for screening and designing high-performance SACs. This work advances the understanding of atomic-scale factors governing the initial C–H bond activation step and offers a foundation for the rational catalyst design for propane dehydrogenation, paving the way for experimental validation and industrial adoption of data-driven catalyst design. Catalysis ; Atom (system on chip) ; Chemistry ; Materials science ; Crystallography ; Computer science ; Organic chemistry ; Parallel computing Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yuxuan Zhou (4245340) | Beirong Zhang (12551182) | Bowen Zhong (7386536) | Min Sun (115484) | Xu Zhang (49484) | Yukui Zhang (1413592) | Lihua Zhang (22614) | Qun Zhao (603175) | Zhou Gong (135082) | Maili Liu (197522)
Source: Figshare (article, 2025-07-09)
Field: Chemistry — Spectroscopy

Dissecting <i>In Vivo</i> Cross-Linker Performance:Insights into Structural Dynamics and Molecular Rigidity Understanding protein structure and dynamics is crucial to elucidating their biological functions. Recent advancements in experimental techniques and AI-driven predictions, such as AlphaFold, have significantly enhanced our ability to determine protein structures. However, most methods are based on <i>in vitro</i> studies, which often fail to capture the complexities of protein behavior <i>in vivo</i>. This study focuses on <i>in vivo</i> chemical cross-linking mass spectrometry (XL-MS) as a promising approach to investigate protein structures within their native cellular environments. We systematically evaluate the performance of four commonly used cross-linkers, analyzing their structural characteristics, like length and dihedral angles, as well as the resulting thermodynamic and kinetic behaviors. By combining extensive <i>in vivo</i> XL-MS data with all-atom molecular dynamics simulations, we elucidate the relationship between cross-linker structure and their effectiveness in probing protein dynamics. The results reveal the relationship between the flexibility of cross-linkers and their structural characteristics and further elucidate that more rigid cross-linkers are able to capture more protein dynamic information, especially the dynamics of disordered regions. This study provides critical insights into cross-linker selection and design, offering a framework for future <i>in vivo</i> XL-MS applications aimed at advancing our understanding of protein dynamics and interactions in living cells. Molecular dynamics ; Protein dynamics ; Dynamics (music) ; Flexibility (engineering) ; Protein structure ; Protein–protein interaction ; Protein folding ; Structural rigidity Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Receptor Mechanisms and Signaling
Authors: Sebastian Eibl | Yiyu Yao | Matthias Scheffler | Markus Rampp | Luca M. Ghiringhelli | Thomas A. R. Purcell
Source: ArXiv.org (preprint, 2025-02-27)
Field: Materials Science — Materials Chemistry

A high-performance and portable implementation of the SISSO method for CPUs and GPUs SISSO (sure-independence screening and sparsifying operator) is an artificial intelligence (AI) method based on symbolic regression and compressed sensing widely used in materials science research. SISSO++ is its C++ implementation that employs MPI and OpenMP for parallelization, rendering it well-suited for high-performance computing (HPC) environments. As heterogeneous hardware becomes mainstream in the HPC and AI fields, we chose to port the SISSO++ code to GPUs using the Kokkos performance-portable library. Kokkos allows us to maintain a single codebase for both Nvidia and AMD GPUs, significantly reducing the maintenance effort. In this work, we summarize the necessary code changes we did to achieve hardware and performance portability. This is accompanied by performance benchmarks on Nvidia and AMD GPUs. We demonstrate the speedups obtained from using GPUs across the three most time-consuming parts of our code. Code (set theory) ; Codebase ; Supercomputer ; CUDA ; Rendering (computer graphics) ; Source code Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Stochastic Gradient Optimization Techniques
Authors: Yong-Zhu Cai | Hong Wang
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A visual language model enabling intelligent nanomaterial scanning electron micrograph annotation Artificial intelligence (AI) has significantly advanced the research and development of materials science through data-driven approaches. However, the large number of labeled datasets required for AI to operate is difficult to obtain in current times due to the time-consuming and laborious nature of manual labeling. The morphology of nanomaterials is crucial for the study of their properties, and scanning electron microscopy is among the key techniques for morphology characterization. For nanomaterials, the structural complexity makes the annotation of Scanning Electron Microscopy (SEM) images extremely challenging, with very few labeled images available. Therefore, it is urgent to develop an automatic pattern recognition technology for SEM images of nanomaterials without relying on labeled data. In this paper, we develop the Scanning Electron Microscopy Vision-Language Model (SEM-VLM), which is a domain-specific adaptation of the Vision-Language Model (VLM) for nanomaterials science. The model is trained <i>via</i> contrastive learning on SEM image-text pairs extracted from the literature. SEM-VLM demonstrates superior cross-modal retrieval performance over the general-domain model Contrastive Language-Image Pretraining (CLIP) and random baselines with Recall@10 and Recall@50 metrics, and keyword searches show its robust capability to retrieve relevant images. SEM-VLM also achieves high accuracy in zero-shot classification through ensemble vision-language alignment, outperforming CLIP. In few-shot settings, SEM-VLM with 2.1% training labels exhibits superior performance compared with the fully supervised model (EMCNet: Graph-Nets for Electron Micrograph Classification). Activation mapping analysis reveals precise localization of critical nanoscale features (particles, holes, and probe tips), providing more interpretable results than conventional approaches while maintaining operational reliability. This multimodal framework reduces labeled dataset dependency by orders of magnitude and enables automated high-precision classification. Annotation ; Scanning electron microscope ; Pattern recognition (psychology) ; Adaptation (eye) ; Nanomaterials ; Dependency (UML) ; Nanoscopic scale ; Image processing Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Machine Learning in Bioinformatics
Authors: Haotian Wang | Xuli Cheng | Yuhao Wang | Yue‐tao Yang | Yuan Xue | Zhitang Song | Sannian Song | Li-Jun Tian | Wei Ren
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Ga₂Se₃-Based Ovonic Threshold Switching Selector for High-Density Memory Applications Materials science ; Optoelectronics ; Computer science Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Semiconductor materials and interfaces