jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tingkai Xue | Chin Chun Ooi | Yang Jiang | Luu Trung Pham Duong | Pao-Hsiung Chiu | Weijiang Zhao | Nagarajan Raghavan | My Ha Dao
Source: ArXiv.org (article, 2026-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Informed Uncertainty Enables Reliable AI-driven Design Inverse design is a central goal in much of science and engineering, including frequency-selective surfaces (FSS) that are critical to microelectronics for telecommunications and optical metamaterials. Traditional surrogate-assisted optimization methods using deep learning can accelerate the design process but do not usually incorporate uncertainty quantification, leading to poorer optimization performance due to erroneous predictions in data-sparse regions. Here, we introduce and validate a fundamentally different paradigm of Physics-Informed Uncertainty, where the degree to which a model's prediction violates fundamental physical laws serves as a computationally-cheap and effective proxy for predictive uncertainty. By integrating physics-informed uncertainty into a multi-fidelity uncertainty-aware optimization workflow to design complex frequency-selective surfaces within the 20 - 30 GHz range, we increase the success rate of finding performant solutions from less than 10% to over 50%, while simultaneously reducing computational cost by an order of magnitude compared to the sole use of a high-fidelity solver. These results highlight the necessity of incorporating uncertainty quantification in machine-learning-driven inverse design for high-dimensional problems, and establish physics-informed uncertainty as a viable alternative to quantifying uncertainty in surrogate models for physical systems, thereby setting the stage for autonomous scientific discovery systems that can efficiently and robustly explore and evaluate candidate designs. Workflow ; Uncertainty quantification ; Surrogate model ; Process (computing) ; Engineering design process ; Robust optimization ; Inverse ; Optimization problem ; Inverse problem Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Neural Networks and Reservoir Computing
Authors: Ganesh M. Balasubramaniam | Srinivasa Rao Allam | V. Anand | MD. Haider Ansari | Francis Gracy Arockiaraj | Shlomi Arnon | Purnesh Singh Badavath | Mansi Baliyan | Petr Bouchal | Zdeněk Bouchal | Sakshi Choudhary | Ahmed H. Dorrah | Yuxiang Duan | Kelsey Everts | Andrew Forbes | Matthew R. Foreman | Darius Gailevičius | Akanksha Gautam | Greg Gbur | Shivasubramanian Gopinath | Narmada Joshi | Saulius Juodkazis | O. Korotkova | Kaupo Kukli | Judy Kupferman | Praveen Kumar | Ravi Kumar | Vijay Kumar | Gokul Deepak Manavalan | Ayush Mehra | Naveen K. Nishchal | Takashige Omatsu | C. J. Peters | Andra Naresh Kumar Reddy | Salla Gangi Reddy | Valeria Rodríguez-Fajardo | Carmelo Rosales-Guzmán | Joseph Rosen | Sarita | Allarakha Shikder | Rakesh Kumar Singh | Xinzhou Su | Aile Tamm | Ganesh Velagala | Petr Viewegh | Eulàlia Puig Vilardell | Alan E. Willner | Agnes Pristy Ignatius Xavier | Amit Yadav | Huibin Zhou
Source: Applied Physics B (article, 2026-04-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Roadmap on singular optics and its applications Singular optics is a branch of modern electromagnetics and optics that investigates solutions to Maxwell's equations that exhibit nontrivial topological features under various boundary conditions. These solutions give rise to light fields containing singularities, points or regions at which certain optical properties, such as phase or polarization, become undefined. Over time, singular optics has evolved into a unifying framework for understanding and engineering optical fields that possess phase, polarization, coherence, and spatiotemporal singularities, each characterized by quantized topological properties. Such structured light fields enable high-dimensional information encoding, robust light-matter interactions, and sensitive probing of complex media, thereby impacting optical communication, imaging, sensing, and materials processing. Parallel advances in theory, fabrication techniques, detection hardware, and computational methods have created a diverse and rapidly expanding landscape, underscoring the need for an integrated and forward-looking perspective. This roadmap synthesizes emerging applications of singular optics across multiple platforms, offering a concise overview of current developments and highlighting key physical concepts, new architectures, and transformative technologies that bridge subfields. In addition to reflecting the insights of leading contributors to these research directions, it also surveys selected recent advances, providing a concise overview of current trends and a foundation for shaping the future of singular optics and its applications. Physical optics ; Quantum optics ; Electromagnetics ; Geometrical optics ; Optical physics ; Computational electromagnetics ; Gaussian optics ; Nonlinear optics ; Boundary (topology) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Hao-Yu Chen | Hung-Chang Hsu | Lijin Lin | Yi-Feng Chen | Yan-Ruei Lin | Yu-Wei Chang | Jhih-Yuan Liang | Jyun-Yi He | Hao-Cheng Lee | Ming-Yang Li | Iuliana Radu | Jyh-Pin Chou | Ching-Ming Wei | Ya‐Ping Chiu
Source: Nature Communications (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Electrostatically tunable moiré-mediated Wigner states via interfacial potential engineering in 2D van der Waals heterostructures Electrostatically tunable quantum confinement in nanoscale systems offers unprecedented opportunities for manipulating artificial quantum matter, positioning these platforms at the frontier of quantum science. The strategic integration of distinct confinement mechanisms could revolutionize quantum functionality by enabling novel states with enhanced coherence properties. Here, we demonstrate inherently interfacial potential engineering by combining lateral semiconductor 2D moiré potentials with vertical quantum confinement effects in semimetal bismuth nanofilms, creating localized periodic sites for quantum-confined charges. Using scanning tunneling microscopy, we observe moiré-mediated Wigner crystals with self-organized electron lattices arising from strong Coulomb interactions, exhibiting unexplored multiple energy quantization behaviors that can be manipulated by quantum well states in ultrathin bismuth films. These precisely localizable charge states provide a promising platform for van der Waals (vdW) charge qubits electrostatically confined within 2D materials. Our work demonstrates extended tunability of artificial atom states in phase, space, and energy regimes. With optimized designs, these 2D vdW architectures bridge fundamental Wigner crystallization phenomena with practical applications in advanced electronic systems and quantum state manipulation. van der Waals force ; Heterojunction ; Density functional theory ; Work (physics) 2D Materials and Applications ; Graphene research and applications ; Strong Light-Matter Interactions
Authors: H. L. Zhang | Artem K. Grebenko | D. Litvinov | Wenwen Zheng | Konstantin Iakoubovskii | Sergey Grebenchuk | Anna A. Makarova | Alexander Fedorov | Andrei V. Starkov | Carlo M. Orofeo | D. V. Vyalikh | Mario Lanza | Maciej Koperski | Kostya S. Novoselov | Chee‐Tat Toh | Barbaros Ӧzyilmaz
Source: Advanced Materials (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Breaking the 2‐nm Barrier in Hard Disk Drives Using Monolayer Amorphous Carbon Overcoats The rapid growth of artificial intelligence (AI) has increased the demand for large-scale data storage, making hard disk drives (HDDs) indispensable in data centers due to their cost-effectiveness and stability. To support AI-driven data requirements, increasing the areal storage density is critical. However, this metric is increasingly constrained by the carbon overcoat (COC), the essential protective layer for magnetic media. Traditional diamond-like carbon (DLC) can no longer fulfill the stringent demands for ultrathin coatings and high thermal stability required by next-generation technologies like Heat-Assisted Magnetic Recording (HAMR) and bit-patterned media. Here, we introduce monolayer amorphous carbon (MAC) as a superior alternative. MAC is directly grown on the heterogeneous (Fe, Pt, SiO<sub>2</sub>) HDD surface at low temperatures (∼ 300°C), achieving an uniform 0.8 nm thickness across 2.5-inch disks. Despite its atomic thickness, MAC demonstrates high corrosion resistance and low roughness comparable to commercial 2.5 nm COCs. Its fully amorphous, sp<sup>2</sup>-hybridized structure ensures excellent thermal stability under HAMR-like conditions (∼450°C) and a low friction coefficient, enabling potential lubricant-free operation. Replacing traditional COCs with MAC facilitates the development of HDD media capable of achieving 10 Tb/in<sup>2</sup>, addressing the urgent storage demands of the digital era. Amorphous solid ; Carbon fibers ; Monolayer ; Thermal stability ; Amorphous carbon ; Surface roughness ; Thermal ; Magnetic storage Diamond and Carbon-based Materials Research ; Advanced Data Storage Technologies ; Magnetic properties of thin films
Authors: J. Horacio Silvestre-Martínez | Lorna G. Yañez-Algandar | Karina del Carmen Lugo-Ibarra | Ana B. Castro-Ceseña
Source: RSC Pharmaceutics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Polymer-lipid hybrid nanoparticles co-encapsulating fucoxanthin and carbon dots for targeted anti-inflammatory therapy in Alzheimer's disease Tf-functionalized hybrid NPs promote fucoxanthin and carbon dot brain targeting, creating a theranostic platform to suppress reactive astrocyte markers (GFAP, S100β, APP) via NF-κB pathway inhibition. Fucoxanthin ; Disease ; Carbon fibers ; Process (computing) ; Nanoparticle Carbon and Quantum Dots Applications ; Seaweed-derived Bioactive Compounds ; Advanced Drug Delivery Systems
Authors: Wenbo Zhang | Tianxiao Liu | Xiaoying Dong | Saisai Sun | Xiaojun Yao | Pengyong Li | Lin Gao
Source: Communications Biology (article, 2026-02-17)
Field: Chemistry — Organic Chemistry

De novo covalent drug generation with enhanced drug-likeness and safety Covalent drugs have long played an essential role in therapeutics, yet computational design approaches remain largely confined to virtual screening of existing libraries. Despite recent advances in deep generative models for drug discovery, methods specifically tailored to de novo covalent drug generation are still lacking. Here we introduce CovaGEN, a conditional latent diffusion framework for the de novo design of covalent inhibitors with enhanced drug-likeness and safety. CovaGEN generates ligands from a drug-like latent space while conditioning on target sequences and employing a classifier to guide the formation of desirable covalent warheads. A reinforcement learning strategy further optimizes the safety profiles of generated molecules. Experimental results demonstrate that CovaGEN effectively generates covalent drugs with the desired covalent warheads, exhibiting strong target protein affinity, favorable drug-likeness, and low toxicity. When applied to EGFR T790M and Mpro, the generated compounds exhibit higher probabilities of covalent binding. Overall, CovaGEN offers a pioneering approach for the de novo design of covalent inhibitors, advancing the discovery of covalent drugs with improved properties. Covalent bond ; Drug discovery ; Covalent binding ; Drug target ; Chemical space ; Chemical biology ; Drug development Click Chemistry and Applications ; Protein Degradation and Inhibitors ; Biochemical and Structural Characterization
Authors: Zihan Wang | Yongzhi Liu | Tianxing Li | Peirong Zhu | Guodong Niu | Haoran Du
Source: Electronics (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Study on Thermal Aging Characteristics of Aerospace Motor Polyimide-Enameled Wires Based on Arrhenius Law The windings of aerospace motors are fabricated using enameled wires; with polyimide (PI) serving as the primary material for their insulating enamel coatings, thermal aging is the predominant factor contributing to insulation failure in enameled wires. The prolonged natural aging process of enameled wires, coupled with the complexity and sluggish variation rates of dielectric parameters used for aging monitoring, presents significant challenges in developing a universal method for assessing insulation performance. To address this challenge, our study determined accelerated aging conditions based on the Arrhenius law, fabricated twisted-pair specimens, and implemented a step-stress aging protocol, in order to monitor the insulation capacitance (IC) and dielectric dissipation factor (tan δ) of the sample. Finally, a two-parameter Weibull distribution plot was established to characterize the relationship between service life and failure probability. Initial-value normalization combined with B-spline interpolation was employed to construct IC–life correlation curves. A novel method for monitoring PI-enameled wire insulation life using IC variation rate was proposed and experimentally validated, providing a methodological framework for lifespan prediction of aerospace motor windings. Finally, a two-parameter Weibull distribution plot was established to characterize the relationship between service life and failure probability. Initial-value normalization combined with B-spline interpolation was employed to construct IC–life correlation curves. The rationality of the method using IC change rate to monitor the insulation lifetime of polyimide-enameled wire was verified, the lifetime assessment of aviation motor stator windings was achieved by monitoring corresponding dielectric parameters, and a reference standard for the maintenance and support of aviation equipment was provided. Weibull distribution ; Service life ; Accelerated aging ; Failure rate ; Normalization (sociology) ; Electromagnetic coil ; Arrhenius plot ; Spacecraft High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electrical Fault Detection and Protection
Authors: Md. Mahathy Hasan Jewel | Dr. Md. Nazmul Hossain | Dr. Ashraful Islam Chowdhury
Source: Jahangirnagar University Journal of Business Research (article, 2026-01-06)
Field: Materials Science — Polymers and Plastics

Exploring the Benefits of Artificial Intelligence Adoption in Ready-Made Garment Industry of Bangladesh. This paper investigates the benefits associated with the use of Artificial Intelligence (AI) in the apparel industry of Bangladesh. The study focuses on seven key AI tools: Machine Learning, Expert Systems, Decision Support Systems, Optimization Systems, Image Recognition and Vision, Deep Learning, and Neural Networks. By examining the applications and impacts of these technologies, this research aims to provide insights into how AI can enhance operational efficiency, improve product quality, and foster sustainability within the sector. The study employed a convenience sampling method, utilizing a quantitative research approach through a questionnaire-based survey conducted in ready-made garment factories. A sample size of 300 participants was analyzed using Smart PLS4 Structural Equation Modeling (SEM). The results indicated strong positive correlations among five variables—Expert Systems, Decision Support Systems, Image Recognition and Vision, Deep Learning, and Neural Networks—highlighting their significant impact on the apparel industry in Bangladesh. Conversely, a negative correlation was found between Machine Learning and Optimization Systems and their impact on the industry. These findings offer valuable insights for stakeholders in the Bangladesh apparel sector, guiding the integration of AI technologies. Clothing ; Artificial neural network ; Sample (material) ; Product (mathematics) ; Sustainability ; Structural equation modeling ; Key (lock) ; Decision support system ; New product development Textile materials and evaluations ; AI and HR Technologies ; Digital Transformation in Industry
Authors: Prutthipong Tsuppayakorn-aek | Sirinee Thasitha | Anan Udomkijmongkol | Wiwittawin Sukmas | Dr. Tanveer Hussain | Thiti Bovornratanaraks | Komsilp Kotmool
Source: Research Square (preprint, 2026-02-11)
Field: Materials Science — Materials Chemistry

Possible High-Temperature Superconductivity inLi2CuH6 at Ambient Pressure Metastability ; Ambient pressure ; Superconductivity ; Ab initio ; Coupling (piping) ; Fermi level ; Ab initio quantum chemistry methods ; Van Hove singularity ; Phonon Hydrogen Storage and Materials ; Rare-earth and actinide compounds ; Superconductivity in MgB2 and Alloys
Authors: Wu Xue | Shiyu Yu | Jian Mei | Bing Liu | Shuai Yang
Source: Catalysts (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Boosting Toluene Oxidation over Ru-Doped CoMn2O4 Spinel Catalysts by Constructing Ru–O–Mn/Co Chains The development of efficient spinel oxide catalysts for low-temperature oxidation of volatile organic compounds (VOCs) remains an important research objective. In this work, Ru was doped into a CoMn2O4 spinel to enhance its catalytic activity toward toluene oxidation and the underlying promotion mechanism of Ru doping was systematically investigated. The resulting Ru-CoMn2O4 catalyst showed remarkable performance, with T90 reaching approximately 224 °C at a WHSV of 60,000 cm3 g−1 h−1 and nearly 100% CO2 selectivity above 200 °C. Mechanism studies revealed that the reaction followed both Mars–van Krevelen (MvK) and Eley–Rideal (E–R) pathways. The reaction rates were strongly influenced by the oxidizing capacity of the catalyst, the abundance of highly valent surface species (namely Co3+, Mn4+, and Ru4+), adsorbed toluene, lattice oxygen, gaseous toluene, and adsorbed oxygen. With Ru doping, new Ru–O–Mn and Ru–O–Co chains formed in the CoMn2O4 spinel structure, leading to a moderate enhancement in oxidizing ability and a moderate increase in the concentration of highly valent surface species, adsorbed toluene, and lattice oxygen. Although a slight reduction in adsorbed oxygen was observed, Ru doping significantly boosted the overall toluene oxidation activity of CoMn2O4. In summary, Ru-CoMn2O4 represented a promising catalyst for the efficient oxidation of VOCs. Spinel ; Catalysis ; Oxidizing agent ; Toluene ; Adsorption ; Redox ; Selectivity Catalytic Processes in Materials Science ; Chemical Looping and Thermochemical Processes ; Catalysts for Methane Reforming
Authors: Maria A. Vratsanos | Evangelos Bakalis | Chiwoo Park | Francesco Zerbetto | Nathan C. Gianneschi
Source: Small (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Liquid Phase TEM of Diffusing Emulsion Droplets The origin of the viscoelastic behavior that many nanoparticles display during diffusive motion is unknown. Such dynamics are difficult to record without sophisticated methods that combine a suitable observation window of motion in time with high image resolution. Herein, we study and describe the diffusion of two types of particles in the form of emulsion droplets in situ via liquid phase TEM. For both, the observed particle motion in solution is anomalous (non-Brownian) and is either sub- or super-diffusive. Fractional Brownian motion (fBm) and random walks on fractals (RWF) are the two potential mechanisms. It can be challenging to differentiate these since they may have the same position or velocity autocorrelation function, but they diverge in the average number of sites visited, which is connected to the fractal dimension of the walk. We conclude that droplet-surface interactions and electron beam fluence create a fractal energy landscape yielding peculiar dynamics. Brownian motion ; Fractal dimension ; Fractal ; Random walk ; Diffusion ; Diffusion-limited aggregation ; Emulsion ; Phase (matter) ; Particle (ecology) Pickering emulsions and particle stabilization ; Nanopore and Nanochannel Transport Studies ; Material Dynamics and Properties
Authors: Tejinder Kaur | Mukka Tharuneswar Reddy | Mohamed Hussein Abdirahman | Abhinav Jaiswal | Reshma. S | Jency Lal
Source: International Journal of Aerospace Engineering (article, 2026-01-17)
Field: Materials Science — General Materials Science

PRE- AND POST-REPAIR ANALYSIS OF AIRCRAFT STRUCTURAL COMPONENTS USING ALUMINIUM–ACRYLIC COMPOSITE LAMINATES In the construction and maintenance of contemporary aircraft, structural integrity and damage tolerance are crucial factors.In order to attain better strength-to-weight ratios and increased resistance to structural damage, hybrid composite laminates are being used more and more.The pre-and post-repair behavior of aircraft structural components made of aerospace-grade aluminum Al7075 and acrylic sheet laminates is investigated experimentally and numerically in this work.For comparison, two composite systems were created.The first system employed epoxy resin and hardener as the bonding matrix, while the second incorporated ash and plastic powder as fillers within the epoxy matrix.The primary objective of this research was to evaluate whether the inclusion of waste-derived fillers enhances the mechanical performance of aluminium-acrylic composite laminates compared to a filler-free system. Composite laminates ; Composite number ; Component (thermodynamics) ; Structural integrity ; Finite element method Material Properties and Applications ; Material Selection and Properties ; Mechanical Behavior of Composites
Authors: Yuetao Zhao | Paul K. Chu | Huaiyu Wang
Source: The Innovation Life (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

MOFs: From programmable porous crystals to precision biomedicine Biomedicine ; Porosity ; Nanoparticle ; Feature (linguistics) Metal-Organic Frameworks: Synthesis and Applications ; Magnesium Oxide Properties and Applications ; Advanced Nanomaterials in Catalysis
Authors: Natalia Andronova | Mikhail G. Andronov | Jürgen Schmidhuber | Michael Wand | Djork-Arné Clevert
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search Medusa model and speculative beam search accelerate template-free synthesis planning. Beam search ; Transformer ; Artificial neural network ; Scalability ; Beam (structure) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Neural Networks and Reservoir Computing