jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xu Qin | Qinghang Wang | Jianchuan Yan | Biaobiao Yang | Yuhui Zhang | Bin Jiang | Javier LLorca
Source: Acta Materialia (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

A multidimensional continuous conditional generative adversarial network to link composition-process-microstructure-properties in Mg alloys This investigation presents a novel multidimensional continuous conditional generative adversarial network that is able to create alloy microstructures directly from composition-process variables in a single forward pass. The model is applied to discover ternary Mg-Al-Sn alloys manufactured by casting with high ultimate tensile strength and low corrosion rate. From the target performance indices, the model provides the candidate alloy compositions together with their corresponding process parameters, and the resulting microstructures. Experimental validation reveals deviations of only 1.4 % for the strength and 7.5 % for the corrosion rate with respect to the predictions while the errors in the fraction of each phase are below 0.4 %. By coupling continuous microstructure generation with multi-objective optimization, this work provides a scalable artificial intelligence-based inverse-design paradigm for magnesium and broader engineering alloys. Link (geometry) ; Adversarial system ; Generative grammar ; Generative adversarial network ; Key (lock) Machine Learning in Materials Science ; Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties
Authors: Cristina Izquierdo-Lozano | Marrit M. E. Tholen | Valentina Girola | Anna Świetlikowska | Maarten Merkx | Francesca Grisoni | Lorenzo Albertazzi
Source: ACS Nano (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

“ <i>Visualize</i> , <i>Describe</i> , <i>Compare</i> ”─Nanoinformatics Approaches for Material-Omics Bioinformatics and cheminformatics are established disciplines, but nanoinformatics, the development of computational tools for understanding and designing nanomaterials, is still in its infancy. In light of the new data-driven approaches for nanomaterials discovery, there is a growing need for in silico tools tailored to analyze nanomaterials data sets. This is particularly crucial for materials where a crystalline structure cannot be obtained, and therefore, the characterization data sets are less structured, and there are no standard methods for data mining. Here, we present a computational framework capable of visualizing, describing, and comparing nanoparticle data sets obtained with super-resolution microscopy at the single-particle and single-molecule level. Our method allows us to: (i) visualize multiparametric nanoparticle data sets to grasp material properties and heterogeneity; (ii) have a quantitative evaluation of a material through a series of molecular descriptors, and (iii) compare different materials quantitatively and globally, going beyond comparison of a single property. We applied this method to a library of targeted nanoparticles revealing particle heterogeneity, similarities, and correlations between the synthesis and the physicochemical properties of the different nanomaterials. Finally, we show the potential of this approach to reveal batch-to-batch variations in time and between users hidden in standard analysis. Cheminformatics ; GRASP ; Nanomaterials ; Characterization (materials science) ; Nanoparticle ; Experimental data Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Cell Image Analysis Techniques
Authors: Muhammad Usman | Muhammad Umar Farooq | Fuyi Chen
Source: Chemical Engineering Journal (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Deepcluster: A selective and stable strategy to predict the AuPt-based high entropy alloy nanoclusters via deep reinforcement learning Nanoclusters ; Reinforcement learning ; Maxima and minima ; Molecular dynamics ; Radial distribution function ; Alloy ; Convergence (economics) ; Phase space ; Entropy (arrow of time) ; Potential energy Machine Learning in Materials Science ; High Entropy Alloys Studies ; Statistical Mechanics and Entropy
Authors: Marianna Kotzabasaki | Leonidas Mindrinos | Nikolaos Sotiropoulos | Konstantina Filippou | Chrysanthos Maraveas
Source: Polymers (article, 2026-04-07)
Field: Materials Science — Biomaterials

A Data-Driven Framework for Predicting PHBV Biodegradation-Induced Weight Loss Based on Laboratory and Real-Environment Condition Tests Polyhydroxyalkanoates (PHAs) emerge as promising biodegradable polymers for sustainable applications, yet predicting their biodegradation behavior under different environmental conditions remains challenging. In this study, we propose a novel data-driven computational framework for predicting biodegradation-induced weight/mass loss in PHA-based materials. A comprehensive database of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)-based formulations was manually curated by systematically collecting and harmonizing material descriptors, environmental parameters, and experimental biodegradation outcomes from laboratory- and large-scale studies conducted in soil, marine, freshwater, and compost environments. Multiple regression-based quantitative structure–activity relationship (QSAR) models were developed and rigorously validated, demonstrating high predictive performance and strong correlations between polymer structure, environmental conditions and degradation behavior. “Exposure time”, “degradation environment” and “hydroxybutyrate (HB) ratio” were identified as the most important features for weight loss. Finally, the predictive model was integrated into the Jaqpot computational platform, enabling open access and facilitating data-driven assessment and design of biodegradable polymer systems. Biodegradation ; Degradation (telecommunications) ; Predictive modelling ; Polymer ; Compost ; Biodegradable polymer biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Polymer crystallization and properties
Authors: Rohan Gupta | Reetesh Kumar | Naveen Kumar | Pallavi Singh
Source: Journal of Applied Toxicology (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Reimagining Brain Drug Delivery: Mechanistic and Translational Advances in Carbon Nanotube Nanomedicine Neurological and neurodegenerative disorders (NDDs) present ongoing therapeutic challenges attributed to the structural complexity of the central nervous system (CNS) and the restrictive properties of the blood-brain barrier (BBB), which inhibit the effective penetration of most drugs into the brain. Thus, carbon nanotubes (CNTs) possess a high aspect ratio, tunable surface chemistry, and exceptional electrical and mechanical properties, establishing them as versatile nanoplatforms that can address significant challenges in CNS drug delivery. Despite the exploration of various nanocarriers, a significant gap persists in the comprehensive understanding of the convergence of CNT structure, functionalization, mechanistic transport pathways, and safety-by-design strategies for translational neurological applications. This review synthesizes recent advancements in CNT engineering, mechanisms of BBB traversal, cell-specific targeting, and intracellular trafficking, as well as insights into CNT-induced neurotoxicity and clearance pathways. Further, the review emphasizes therapeutic advancements in CNT-mediated interventions for Alzheimer's disease (AD), Parkinson's disease (PD), brain tumors, stroke, and epilepsy, focusing on multifunctional applications in drug delivery, neuroregeneration, immunomodulation, neuromodulation, theranostic, and biosensing. Additionally, it assesses preclinical results, safety factors, regulatory challenges, and the growing influence of artificial intelligence (AI)-driven design in enhancing nanoscale behavior, functionalization, and biocompatibility. Lastly, the current review integrates technological innovations with mechanistic toxicology and translational challenges, establishing CNTs as promising neuro-nanomedicine platforms that may effectively address unmet clinical needs in CNS disorders. Trial Registration: ClinicalTrials.gov: NCT04495634, NCT02873585, NCT01246336, NCT07034248, NCT07034248. Nanomedicine ; Drug ; Translational research ; Disease ; Neurotoxicity ; Carbon nanotube ; Clinical trial ; Drug delivery Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Li-Ying Wang | Z. H. Li | Ting Wang | Min Han | Hongwei An | Hao Wang
Source: Chemical Communications (article, 2026-01-01)
Field: Materials Science — Biomaterials

Self-assembling peptides construct supramolecular materials Programmable peptide self-assembly enables the precise construction of supramolecular materials, establishing it as a defining frontier in nanomaterials research. This review systematically explores the collective contribution of sequence design, secondary structure regulation (including α-helices, β-sheets, and cyclic conformations), and dynamic modulation, and hierarchical organization in facilitating the creation of well-defined nanostructures such as nanotubes, nanopores, and nanocages. Artificial intelligence and computational modeling have emerged as critical tools to guide peptide design and predict assembly pathways, thereby enabling a strategic shift from empirical screening to mechanism-driven design. The review further highlights nanopore-based detection applications, demonstrating the potential for highly accurate, biocompatible detection of ions, nucleic acids, and proteins at single-molecule resolution. By integrating molecular design with biological function, this "from sequence-design to ordered structures to advanced applications" paradigm establishes a foundational framework for the development of precisely constructed functional supramolecular materials. Supramolecular chemistry ; Construct (python library) ; Biocompatible material ; Nanomaterials ; Nanostructure ; Sequence (biology) Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Polydiacetylene-based materials and applications
Authors: Feiyang Xie | Amando Singun | Chao Hao | Jiayi Liang
Source: IEEE Access (article, 2026-01-01)
Field: Materials Science — Biomaterials

Research Status and Trend Analysis of Digitally Enabled Agricultural Product Packaging: A Visual Analysis Based on Knowledge Graph Digital integration in agricultural systems is intensifying the demand for intelligent packaging solutions that enhance food safety, reduce losses of perishable goods, and promote environmental sustainability. This study conducts a bibliometric analysis using Knowledge Graph on relevant publications retrieved from the Web of Science to map the evolutionary trajectory of digitally enabled agricultural packaging research. The results indicate sustained and robust growth in publication output, fueled by interdisciplinary convergence among food science, materials engineering, and computer science. The core research themes have evolved from an early focus on passive preservation techniques toward active and intelligent monitoring systems, with dominant topics including smart sensors, sustainable materials, and data-driven supply chain optimization. Emerging research frontiers increasingly emphasize sustainable packaging solutions that incorporate advanced tracking technologies and circular economy principles to minimize ecological impact. Future advancements will likely rely on cross-disciplinary artificial intelligence solutions, the establishment of standardized sustainability metrics, and deeper integration with digital supply chain infrastructures to overcome scalability challenges within global digital agricultural networks. Sustainability ; Supply chain ; Scalability ; Agriculture ; Circular economy ; Sustainable agriculture ; Graph Nanocomposite Films for Food Packaging ; Food Supply Chain Traceability ; Food Waste Reduction and Sustainability
Authors: John H.T. Luong
Source: Biotechnology Advances (article, 2026-03-30)
Field: Materials Science — Biomaterials

Chitosan nanocrystals as next-generation antimicrobials: Mechanistic insights, antibiotic synergy, and biomedical potential Chitosan ; Antimicrobial ; Biofilm ; Biopolymer ; Chitin ; Biocompatible material ; Nanomedicine ; Antibiotics Nanocomposite Films for Food Packaging ; Advanced Cellulose Research Studies ; Nanoparticles: synthesis and applications
Authors: Qi Zhao | Yulin Gao | Nengxiu Zhu | Fei Wang | De Zhao | John Wang
Source: Matter (article, 2026-04-01)
Field: Chemistry — Inorganic Chemistry

Metal-organic frameworks for energy: From perfect crystals to programmable defective, quasi-, and post-MOFs Economic shortage ; Ingenuity ; Characterization (materials science) ; Key (lock) ; Energy (signal processing) ; Point (geometry) ; Rational design Metal-Organic Frameworks: Synthesis and Applications ; Thermal Expansion and Ionic Conductivity ; MXene and MAX Phase Materials
Authors: Lukas M. Sigmund | Michele Assante | Matthew Ball | Mikhail Kabeshov
Source: ChemRxiv (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

BONAFIDE: A Python Framework for the Calculation of Local Features for Atoms and Bonds in Molecules Data-driven chemistry relies on robust, efficient, and reproducible pipelines for generating digital molecular representations. While comprehensive software for global molecular descriptors is widely available, the calculation of local atom and bond features remains fragmented across disparate tools with inconsistent interfaces. Here, we introduce the BOnd aNd Atom FeaturIzer and Descriptor Extractor (BONAFIDE), a Python package designed to standardize the generation of local descriptors. BONAFIDE provides a unified application programming interface that integrates 11 external computational chemistry and cheminformatics packages, including RDKit, MORFEUS, and Multiwfn as well as quantum chemical engines like xtb and Psi4 for single-point energy computations. The software currently supports the calculation of 590 distinct atom and bond features derived from either 2D or 3D molecular representations and electronic structure data. Key functionalities include flexible input format processing as well as output customization, seamless atom and bond feature calculation across all implemented descriptors, transparent hyperparameter tracking, comprehensive logging, and the possibility to implement custom featurizers within the BONAFIDE ecosystem. We also include runtime statistics for featurization and a case study that demonstrates how the tool is used for the featurization of amines through a multistep featurization pipeline. Current limitations and future development directions are discussed. BONAFIDE is available free of charge as open-source software under the Apache 2.0 license and can be obtained from GitHub (https://github.com/MolecularAI/atom-bond-featurizer). Python (programming language) ; Cheminformatics ; Software ; Atom (system on chip) ; Scripting language ; MIT License ; Interface (matter) ; Topology (electrical circuits) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Jiazheng Wang | Decai Ouyang | Na Zhang | Yuan Li | Tianyou Zhai
Source: SusMat (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Emerging 2D Material Prototype Devices Toward Energy‐Efficient and Sustainable Optoelectronics ABSTRACT Driven by global sustainable development goals and the rapid advancement of artificial intelligence (AI), low energy consumption, high integration density, and sustainability have emerged as core imperatives for optoelectronic device development. Conventional complementary metal‐oxide‐semiconductor (CMOS) architectures, featuring discrete sensing‐memory‐computing functions, are constrained by the von Neumann bottleneck, which is marked by excessive energy consumption in data transmission, and fail to meet the energy‐saving demands of scenarios like the internet of things (IoT) and neuromorphic computing. In contrast, two‐dimensional (2D) materials, with attributes including atomic‐scale thickness, efficient optoelectronic coupling, and low fabrication energy consumption, serve as pivotal carriers to overcome this bottleneck. Prototype 2D material‐based optoelectronic devices are evolving from single‐function units to sensing‐memory‐computing integrated systems, reducing energy redundancy in traditional architectures via material‐structure synergetic adaptation while combining low energy consumption, high integration, and environmental compatibility. With breakthroughs in key technologies, All‐in‐One 2D material devices will most likely reshape the optoelectronic industry landscape, providing hardware support for the synergy between AI and green development. Neuromorphic engineering ; Efficient energy use ; Energy consumption ; Redundancy (engineering) ; Key (lock) ; Sustainable energy ; Fabrication ; Sustainability ; Von Neumann architecture 2D Materials and Applications ; MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Sapna Jain | Abhirup Khanna
Source: ChemistrySelect (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Recent Advancements in Chitosan‐Based Nanomaterials for <i>E. coli</i> Removal From Water: Perspectives on AI Integration ABSTRACT Waterborne pathogens, such as Escherichia coli ( E. coli ), pose a serious problem for human health, particularly in developing countries where access to clean drinking water is limited. The conventional methods of disinfecting contaminated water have challenges, including limited effectiveness, high operational expenses and the formation of unwanted byproducts. To address these issues, advancement in technologies is desirable. Nanotechnology has emerged out as potential contender among various emerging technologies. Globally, chitosan nanoparticles (CNPs) attracting interests of scientists due to their promising antimicrobial activities and distinguishing physico‐chemical properties. The review highlighted the recent advancements (2015–2025) in the development of CNPs to address the problems associated with E. coli present in contaminated water bodies. It also analyses the new opportunities of applying artificial intelligence (AI) to the optimization of systems based on the use of chitosan nanoparticle (CNP). Six thematic areas of focus are included: optimization of nanoparticle synthesis protocols; in silico modeling of bacterial removal efficiency; implementing intelligent sensing and monitoring systems; artificial intelligence in microscopic imaging/image interpretation; integrating data on a large scale alongside decision support frameworks; and environmental and lifecycle effects analysis. All these AI‐based advances will raise the efficiency, scalability, and sustainability of E. coli elimination in water via CNP. Sustainability ; Scale (ratio) ; Emerging technologies ; Applications of artificial intelligence ; Big data Nanoparticles: synthesis and applications ; Nanocomposite Films for Food Packaging ; Biosensors and Analytical Detection
Authors: Sewera Marriam | Gull e Rana Khalid
Source: Next Nanotechnology (article, 2026-02-03)
Field: Materials Science — Biomaterials

The chemo-biochemical convergence in nanomedicine: A review of tailored therapeutic strategies This review examines the emerging concept of ''chemo-biochemical convergence'' within the field of nanomedicine, which represents a synergistic integration of synthetic chemistry and biological principles aimed at developing advanced therapeutic systems. The primary objective is to investigate how the meticulous chemical engineering of nanoplatformsestablishes a robust foundation for complex biological functionality. Key findings from this domain indicate that this approach facilitates both passive targeting via the Enhanced Permeability and Retention effect and active targeting through the use of surface-conjugated ligands. Additionally, it supports the creation of ''smart'' systems that enable on-demand drug release in response to distinct microenvironmental stimuli such as pH, redox potential, and temperature. This is exemplified by the clinical application of Doxorubicin (DOX) nanocarriers, which demonstrate improved efficacy and diminished systemic toxicity. The potential for future integration with artificial intelligence and synthetic biology suggests transformative opportunities for the development of highly personalized, adaptive, and effective treatments. Transformative learning ; Synthetic biology ; Convergence (economics) ; Domain (mathematical analysis) ; Field (mathematics) ; Robustness (evolution) Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Jiamei M. Chen | Xinyi Feng | Jiaying Huang | Xinyi Li | Shijian Huang | Zongbing Wu | Lvqin Qiu | Liping Cao | Qi Liang | Xiaoyan Li
Source: Micromachines (article, 2026-02-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Microfluidics-Assisted Three-Dimensional Confinement of Cholesteric Liquid Crystals for Sensing Applications As a class of self-organized soft matter systems merging fluidic mobility with long-range molecular order, cholesteric liquid crystals (CLCs) possess immense potential for the development of high-sensitivity, visually tractable flexible sensors. Leveraging their unique helical superstructures and stimuli-responsive photonic bandgaps, CLCs can transduce subtle physical or chemical perturbations into discernible optical signatures, such as Bragg reflection shifts or mesomorphic textural transitions. Nonetheless, the intrinsic fluidity of CLCs often compromises their structural integrity, while conventional one-dimensional (1D) or two-dimensional (2D) confinement geometries exhibit pronounced angular dependence, significantly constraining their detection precision in complex environments. Recently, microfluidic technology has emerged as a pivotal paradigm for achieving sophisticated three-dimensional (3D) spatial confinement of CLCs through the precise manipulation of microscale fluid volumes. This review systematically delineates recent advancements in microfluidics-enabled CLC sensors. Initially, the fundamental self-assembly principles and optical properties of CLCs are introduced, emphasizing the unique advantages of 3D spherical confinement in mitigating angular sensitivity and intensifying interfacial interactions. Subsequently, the primary sensing mechanisms are bifurcated into bulk-driven sensing via pitch modulation and interface-driven sensing via topological configuration transitions. We then detail the microfluidic-based fabrication strategies and engineering protocols for diverse 3D architectures, including monodisperse/multiphase droplets, microcapsules, shells, and Janus structures. Building upon these structural frameworks, current sensing applications in physical (temperature, strain/stress), chemical (volatile organic compounds, ions, pH), and biological (biomarkers, pathogens) detection are evaluated. Lastly, in light of persistent challenges, such as intricate signal interpretation and limited robustness in complex matrices, we propose future research trajectories, encompassing the co-optimization of geometric parameters (size and curvature), artificial intelligence-enhanced automated diagnostics, and multi-field-coupled intelligent integration. This work seeks to provide a comprehensive roadmap for the design of next-generation, high-performance, and portable liquid-state photonic sensing platforms. Microscale chemistry ; Microfluidics ; Fluidics ; Liquid crystal ; Photonics ; Robustness (evolution) ; Cholesteric liquid crystal ; Janus particles ; Fabrication Liquid Crystal Research Advancements ; Pickering emulsions and particle stabilization ; Micro and Nano Robotics