jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel Persaud | Mike Werezak | Mark Xu | Melyne Zhou | Frank Benkel | Xin Pang | Vahid Attari | Brian DeCost | Ashley S. Dale | Nicholas A. Senior | Gabriel Birsan | Jason Hattrick-Simpers
Source: arXiv (Cornell University) (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

Materials Acceleration Platform for Electrochemistry (MAP-E): a Platform for Autonomous Electrochemistry Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. We introduce the Materials Acceleration Platform for Electrochemistry (MAP-E), an autonomous, high-throughput system capable of performing parallel electrochemical experiments. MAP-E integrates robotic liquid handling, sample transfer, and multi-channel potentiostatic control and extract corrosion metrics without human intervention. Validation against an ASTM G61-analog benchmark demonstrates reproducibility, with a standard deviation of 76 mV in pitting potential across 32 automated measurements. The platform was then employed to autonomously construct pH-chloride stability diagrams for 304 stainless steel using an uncertainty-driven sampling strategy on a Gaussian Process surrogate model. This approach reduces operator involvement and accelerates the exploration of environmental spaces. MAP-E establishes a framework for autonomous electrochemical experimentation, enabling generation of corrosion datasets that inform materials discovery, alloy design, and durability assessment in service environments. Corrosion ; Benchmark (surveying) ; Electrochemical noise ; Acceleration ; Electrochemistry ; Process (computing) ; Durability ; Sampling (signal processing) ; Automation Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Nylah Dhawan
Source: International Journal of Scientific Development and Research (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Sustainable Lightweight Materials for Aerospace Interiors: A DataDriven Comparison of BioBased and Recycled Composites with Traditional Aluminium Alloys The aerospace industry is actively seeking sustainable alternatives to traditional materials that can meet performance requirements while reducing environmental impact. This study investigates the potential of bio-based and recycled composites to replace aluminium alloy 2024-T3 in aircraft interior applications, where weight reduction directly translates to fuel savings and lower emissions. Through comprehensive data analysis, we examine flax fibre- epoxy composites and recycled carbon fibre-thermoplastic composites as promising sustainable alternatives. Our methodology involves systematic collection and analysis of mechanical properties, environmental impact data, and cost information from NASA technical reports, material databases, industry publications, and peer-reviewed literature. We employ multi-criteria decision analysis (MCDM) to evaluate trade-offs between performance metrics including specific strength, specific stiffness, and durability against sustainability indicators such as carbon footprint, recyclability, and raw material renewability. The research addresses critical gaps in understanding how these emerging materials compare to aerospace-grade aluminium when considering both technical and environmental criteria simultaneously. Special attention is given to the challenges and opportunities these materials present for secondary structural applications, where certification requirements are less stringent than primary structures. This data-driven approach provides a framework for evaluating sustainable material options without requiring expensive laboratory testing, making it accessible for researchers and engineers in developing countries or institutions with limited experimental facilities. The findings contribute to ongoing efforts to decarbonize aviation while maintaining the safety and performance standards essential to aerospace applications, supporting the industry's commitment to achieving net-zero emissions by 2050. Aerospace ; Sustainability ; Raw material ; Durability ; Certification ; Habitability ; Material efficiency ; Aviation ; Environmental impact assessment Natural Fiber Reinforced Composites ; Aluminum Alloys Composites Properties ; Fiber-reinforced polymer composites
Authors: Stefano Pittalis | Filippo Troiani | Celestino Angeli | Irene D'Amico | Tim Gould
Source: arXiv (Cornell University) (preprint, 2026-04-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantum-Information Measure of Electron Localization Understanding electron localization in molecules and materials plays a central role in electronic structure theory, and will increase in importance with the rise of data driven approaches. The electron localization function (ELF) is widely used to visualize electron organization in molecules and materials, and it remains a central ingredient in modern density functional approximations. Yet its formulation retains highly empirical elements. Here we introduce a fully non empirical measure of electron localization derived from the concurrence of a correlated two spin mixed state. This construction yields a genuine two point localization indicator grounded in quantum information theory, removing the ad hoc steps underlying the ELF. We show that atomic shells, covalent and ionic bonds, lone pairs, molecular dissociation, and charge transfer processes are captured. The method is straightforward to evaluate numerically. Electron localization function ; Measure (data warehouse) ; Electron density ; Charge (physics) ; Electron ; Point (geometry) ; Electron transfer ; Electronic structure ; Function (biology) Magnetism in coordination complexes ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Ning Yang | Jian Zhou | Hongfu Huang | Zhimei Sun
Source: Journal of Materials Informatics (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

Machine learning-assisted high-entropy alloy discovery: a perspective High-entropy alloys (HEAs) have attracted extensive attention due to their exceptional mechanical, physical, and chemical properties, making them promising candidates for extreme environments. Understanding the complex structure-property relationships in these multi-principal element systems is crucial for discovering and designing high-performance HEAs. However, their vast compositional space and high-dimensional chemical complexity pose major challenges to traditional trial-and-error design. Machine learning (ML) offers a transformative strategy to overcome these barriers by enabling data-driven exploration. This perspective first reviews the critical challenges currently limiting HEA development, then summarizes recent ML breakthroughs in phase formation prediction, multi-objective optimization, and accelerated atomistic simulations. Finally, we discuss ongoing challenges and propose future opportunities for integrating ML with experimental and computational methods to create more interpretable, data-efficient, and autonomous ML-driven HEA design frameworks. Transformative learning ; Perspective (graphical) ; Limiting ; Phase (matter) ; Space (punctuation) ; Key (lock) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Catalysis and Oxidation Reactions
Authors: Monika Nehra | Aman Kumar | Akshay Kumar | Ajeet Kaushik | Sandeep Kumar
Source: TrAC Trends in Analytical Chemistry (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Integration of sustainable nanotechnology with artificial intelligence of things (AIoT) for environmental applications Hazardous waste ; Sustainable development ; Applications of artificial intelligence ; Nanosensor ; Risk management ; Environmental pollution ; Internet of Things ; Intelligent decision support system Nanoparticles: synthesis and applications ; Air Quality Monitoring and Forecasting ; Advanced Photocatalysis Techniques
Authors: Jiaoyan Qiu | Liang Tao | Pei Li
Source: Advanced technology in neuroscience . (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Nanomaterials integrated with multiple technologies increase the diagnostic efficacy of biomarkers for neurological diseases: A narrative review Nanomaterials have attracted attention in the field of neurological diseases due to the advantages of diversity, modifiability, biocompatibility, chemical stability, and thermal stability. This article reviews the mechanisms and properties of various nanomaterials, including quantum dots, magnetic nanoparticles, metallic nanoparticles, carbon-based nanomaterials, polymer nanoparticles, and nanocomposites. It also elucidates the research and application progress in biomarker detection for neurological diseases and bioimaging in the field of neural repair. In the field of neurobiomarker detection, nanomaterials can integrate multiple technical approaches to achieve highly sensitive and rapid biomarker detection, which significantly enhances experimental efficiency and the reliability of experimental results. In the fields of bioimaging and neural repair, nanomaterials can be combined with fluorescent molecules to accelerate the real-time localization of lesions, improving their diagnostic and therapeutic efficacy. In addition, the use of nanomaterials integrated with tissue engineering scaffolds offer a novel approach for neural repair. At present, the diagnosis and treatment of neurological diseases are gradually shifting from a single-modal paradigm to a multi-modal paradigm. This shift reflects the evolution of diagnostic detection methods from single detection methods to integrated multiple detection techniques. Artificial intelligence technologies have also been introduced for screening effective targets. When combined with nanomaterials, these technologies can enhance the efficacy of neurological disease diagnosis and treatment. Biomarker ; Emerging technologies ; Field (mathematics) ; Narrative review ; Reliability (semiconductor) Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Nanoparticle-Based Drug Delivery
Authors: Mingshuo Guo | Yueting Tian | Tang Sui | Ying Zhao | Shuang Xu | Shaolong Liu | Giuseppe Milano | Chen Qiu | Jiashun Mao
Source: Macromolecules (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Local Density Field as a Physical Order Parameter for Conductivity Prediction in Block Copolymer/Carbon Nanotube Nanocomposites via a Voxel-Based Graph Attention Network The electrical conductivity of block copolymer nanocomposites is governed by a complex interplay between nanofiller organization and block copolymer morphology. However, establishing a quantitative, predictive link between the molecular-scale structure and macroscopic properties remains a fundamental challenge. Here, we demonstrate that the local particle density field serves as a pivotal order parameter controlling the conductive network. By integrating hybrid particle-field molecular dynamics simulations with a graph learning framework, we transform continuous molecular coordinates into a discrete density representation that explicitly encodes this key physical parameter. Our model achieves accurate conductivity predictions across three archetypal carbon nanotube (CNT)-diblock copolymer nanocomposite configurations: randomly mixed, template-free systems (BRCR), CNTs embedded within preassembled lamellar polymer templates (BTCR), and CNTs selectively confined and aligned within self-assembled lamellar domains (BTCO), over a CNT concentration range of 1.0–8.0 vol %. More importantly, the model’s interpretability decodes the distinct conduction mechanisms operative in each morphology: from stochastic network formation in disordered composites, through directed pathway integration in confined templates, to thermodynamics-driven refinement in compatibilized systems. Our analysis of topological network metrics and attention scores quantitatively links these emergent conductive behaviors to the underlying polymer-mediated filler organization. This work establishes local density encoding as a generalizable strategy for elucidating structure–property relationships in complex polymer composites, offering a new paradigm for the data-driven design of functional nanomaterials. Carbon nanotube ; Lamellar structure ; Dissipative particle dynamics ; Nanotube ; Molecular dynamics ; Nanocomposite ; Conductivity ; Polymer nanocomposite ; Electrical conductor Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Conducting polymers and applications
Authors: Słodczyk, Iwo | Frisch, Alexander | Fang, Xufei | Gitman, Inna | Liu, Fengxian
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Data‐Driven Quest for Room‐Temperature Bulk Plastically Deformable Ceramics The growing number of ceramics exhibiting bulk plasticity at room temperature has renewed interest in revisiting plastic deformation and dislocation-mediated mechanical and functional properties in these materials. In this work, a data-driven approach is employed to identify the key parameters governing room-temperature bulk plasticity in ceramics. The model integrates an existing dataset of 55 ceramic materials, including 38 plastically deformable and 17 brittle, and achieves accurate classification of bulk plasticity. The analysis reveals several key parameters essential for predicting bulk plasticity: (i) Poisson's ratio and Pugh's ratio as macroscopic indicators reflecting the balance between shear and volumetric deformation resistance, and (ii) Burgers vector, crystal structure and melting temperature as crystallographic descriptors associated with lattice geometry, slip resistance and thermal stability, and iii) Bader charge as a microscopic measure of bonding character. Together, these parameters define a multiscale descriptor space linking intrinsic materials properties to bulk room-temperature plasticity in ceramics, bridging the gap between empirical ductility criteria and atomistic mechanisms of dislocation-mediated plasticity. While preliminary, this study provides the first systematic, data-driven mapping of the governing factors of ceramic plasticity. The resulting framework establishes a foundation for unifying experimental and computational studies through shared datasets and descriptors, fostering collective progress toward understanding and designing intrinsically ductile ceramics. Ceramic ; Plasticity ; Crystal plasticity ; Bridging (networking) ; Slip (aerodynamics) ; Ductility (Earth science) ; Deformation (meteorology) ; Lattice (music) Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; Advanced materials and composites
Authors: Florina I. Bura | Rafaela Pezzopane | Melissa Granados | Lidia Rodríguez-Ortíz | Blanca Rufián-Andújar | Ana Martínez-López | Maria del Carmen Vazquez-Borrego | A. Romero-Ruiz | Arjona Sánchez Álvaro
Source: Cirugía Española (English Edition) (article, 2026-02-01)
Field: Chemistry — Spectroscopy

Omic analysis of the protein profile in digestive tumours, from proteomics to clinical practice Proteomics ; Identification (biology) ; Clinical Practice ; Omics ; Posttranslational modification Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Cancer Genomics and Diagnostics
Authors: Iwo Słodczyk | A. M. Frisch | Xufei Fang | Inna M. Gitman | F. Liu
Source: Journal of the American Ceramic Society (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

A Data‐Driven Quest for Room‐Temperature Bulk Plastically Deformable Ceramics ABSTRACT The growing number of ceramics exhibiting bulk plasticity at room temperature has renewed interest in revisiting plastic deformation and dislocation‐mediated mechanical and functional properties in these materials. In this work, a data‐driven approach is employed to identify the key parameters governing room‐temperature bulk plasticity in ceramics. The model integrates an existing dataset of 55 ceramic materials, including 38 plastically deformable and 17 brittle, and achieves accurate classification of bulk plasticity. The analysis reveals several key parameters essential for predicting bulk plasticity: (i) Poisson's ratio and Pugh's ratio as macroscopic indicators reflecting the balance between shear and volumetric deformation resistance, and (ii) Burgers vector, crystal structure and melting temperature as crystallographic descriptors associated with lattice geometry, slip resistance and thermal stability, and iii) Bader charge as a microscopic measure of bonding character. Together, these parameters define a multiscale descriptor space linking intrinsic materials properties to bulk room‐temperature plasticity in ceramics, bridging the gap between empirical ductility criteria and atomistic mechanisms of dislocation‐mediated plasticity. While preliminary, this study provides the first systematic, data‐driven mapping of the governing factors of ceramic plasticity. The resulting framework establishes a foundation for unifying experimental and computational studies through shared datasets and descriptors, fostering collective progress toward understanding and designing intrinsically ductile ceramics. Ceramic ; Plasticity ; Bridging (networking) ; Slip (aerodynamics) ; Crystal plasticity ; Ductility (Earth science) ; Lattice (music) ; Deformation (meteorology) ; Deformation mechanism Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; Advanced materials and composites
Authors: Vsevolod Biryukov | Kamal Choudhary | Timur Bazhirov
Source: Open MIND (preprint, 2026-02-16)
Field: Materials Science — Materials Chemistry

M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution The rapid advancement of artificial intelligence in materials science requires data standards and data management practices that can capture the complexity of real-world structures, including surfaces, interfaces, defects, and dimensionality reduction. We present M-CODE - Materials Categorization via Ontology, Dimensionality and Evolution - a compact categorization system that links materials-science-specific terminology to a set of reusable concepts as building blocks and provenance-aware transformations. M-CODE classifies structures by dimensionality, structural complexity (from pristine to compound pristine, defective, and processed), and variants that capture common structure creation and evolution approaches. A practical implementation of the categorization is provided in an open-source codebase that includes JSON schemas, examples, and Python and TypeScript types/interfaces, designed to support reproducible dataset generation, validation, and community contributions. Categorization ; Curse of dimensionality ; Python (programming language) ; Codebase ; JSON ; Terminology ; Software portability Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Material Selection and Properties
Authors: Y Li | Yilong Jia | Ruida Zhao | Gexi Wang | Youlong Xu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Pre-Crystallized Interface Anodization: A Novel Strategy for Energy-Efficient Fabrication of High-Performance Aluminum Electrolytic Capacitors Anodizing ; Electrolytic capacitor ; Oxide ; Capacitor ; Aluminium ; Fabrication ; Capacitance Anodic Oxide Films and Nanostructures ; Advancements in Battery Materials ; Nanoporous metals and alloys
Authors: P. Uma Maheshwera Reddy | N.  Sivasankara Reddy
Source: Springer Link (Chiba Institute of Technology) (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Preface ICE3MT provides a global platform for researchers, engineers, academicians, students, and professionals to exchange innovative ideas, share research insights, and engage in meaningful discussions on emerging trends and challenges in areas such as materials science, surface engineering, metal finishing, nanotechnology, sustainable manufacturing processes, mechanics, energy systems, process modelling, computational materials science, artificial intelligence, optimization, and robotics and automation.The ICE3MT2025 conference attracted about 250 paper submissions from researchers worldwide.Following a rigorous peer-review process conducted in accordance with the guidelines of the publisher, EDP Sciences -Web of Conferences, 78 high-quality papers, with an acceptance rate of approximately 32%, were selected for oral presentation and inclusion in these proceedings. Nucleofection ; Gestational period ; TSG101 ; Dysgeusia ; Liquation ; Diafiltration ; Emperipolesis ; Triacetin ; Durvalumab Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nanoporous metals and alloys
Authors: P. Uma Maheshwera Reddy | N. Subba Reddy
Source: EPJ Web of Conferences (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Preface ICE3MT provides a global platform for researchers, engineers, academicians, students, and professionals to exchange innovative ideas, share research insights, and engage in meaningful discussions on emerging trends and challenges in areas such as materials science, surface engineering, metal finishing, nanotechnology, sustainable manufacturing processes, mechanics, energy systems, process modelling, computational materials science, artificial intelligence, optimization, and robotics and automation.The ICE3MT2025 conference attracted about 250 paper submissions from researchers worldwide.Following a rigorous peer-review process conducted in accordance with the guidelines of the publisher, EDP Sciences -Web of Conferences, 78 high-quality papers, with an acceptance rate of approximately 32%, were selected for oral presentation and inclusion in these proceedings. Nucleofection ; Gestational period ; TSG101 ; Dysgeusia ; Liquation ; Diafiltration ; Emperipolesis ; Triacetin ; Demotion Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nanoporous metals and alloys
Authors: Bo Wang | Siyuan Wang | Jiwei Zhang | M. S. Kim | Peng Zhan
Source: Future Medicinal Chemistry (article, 2026-03-10)
Field: Chemistry — Organic Chemistry

Overview of recent strategic advances in antiviral covalent inhibitors Targeted covalent inhibitors (TCIs) have emerged as a promising antiviral modality, functioning through irreversible or reversible modification of conserved nucleophilic residues within essential viral proteins. These agents are increasingly recognized in antiviral drug discovery due to their potential to achieve enhanced target engagement and prolonged pharmacological effects when appropriately designed, thereby addressing limitations of traditional inhibitors such as rapid resistance development and suboptimal efficacy arising from insufficient binding durability. This review summarizes strategic frameworks for antiviral covalent inhibitor development, highlighting seminal and field-defining reports as well as recent advances. It provides a systematic overview of progress in high-throughput screening, mechanistic studies, and the integration of computational chemistry with artificial intelligence, thereby offering insights into the rational design and discovery of next-generation covalent antivirals. Covalent binding ; Covalent bond ; Drug discovery ; Rational design ; Chemical biology Click Chemistry and Applications ; Synthesis and Characterization of Pyrroles ; Synthesis and Reactivity of Heterocycles