jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingyi Zhao | Meiying Su | Rui Ma
Source: IEEE Access (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-Based Prediction of Warpage in Organic Substrates In substrate fabrication, thermal mismatch between materials and structural configurations can readily induce warpage deformation, which severely impacts subsequent chip mounting and solder joint alignment processes, ultimately compromising the reliability of packaging integration. Therefore, investigating the effects of material types and structural layouts on warpage is essential for design optimization. However, traditional experimental approaches incur high costs, while case-by-case finite element method (FEM) modeling presents computational bottlenecks. This study proposes an artificial intelligence (AI)-based method to predict the warpage behavior of organic substrates with diverse material and structural configurations. Through material preparation, substrate manufacturing, and comprehensive characterization testing, process-matched material parameters and warpage data were obtained. Combined with thermomechanical simulation, a high-precision dataset comprising 972 cross-case studies was constructed, achieving a prediction error of less than 10%. Utilizing this dataset, the network architectures and hyperparameters of Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM) algorithms were optimized, and their performance was evaluated in terms of loss convergence, learning rate adaptability, training efficiency, and robustness. The results indicate that MLP demonstrates rapid loss decay, a training time of 135 seconds, a fitting slope of 0.98, minimal dependency on dataset size, and outperforms the other algorithms. This approach reduces warpage evaluation from traditional day scale to AI-driven second scale, facilitating data-driven rapid design iterations for multimaterial variable-structure substrates. Computer science ; Materials science ; Composite material Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection
Authors: Paricha Jebin | Md. Rakib Khan | Syed Shaheen Shah | Nipa Debnath | Md. Abdul Aziz | B. K. Kim | A. J. Saleh Ahammad
Source: Small (article, 2025-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synergistic Integration of Quantum Materials with Smart Electrolytes for Next‐Generation Multifunctional Supercapacitors: Advances, Challenges, and Future Prospects Rapid advancements in artificial intelligence and growing global demand for sustainable energy solutions have accelerated the integration of intelligent functionalities into electrochemical energy storage devices, notably supercapacitors (SCs). Quantum materials (QMs), including quantum dots (QDs), MXenes, metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and transition metal dichalcogenides (TMDs), combined with smart electrolytes, have emerged as critical components for achieving next-generation flexible, wearable, and intelligent SCs. Smart electrolytes, characterized by stimulus-responsiveness, self-healing, and multifunctionality, substantially enhance operational stability, electrochemical performance, and responsiveness. This review critically evaluates recent advancements in coupling QMs with smart electrolytes, emphasizing innovative design strategies, such as morphological engineering, interface tailoring, and surface functionalization. Synergistic interactions at QM-electrolyte interfaces are analyzed, highlighting enhancements in capacitance, energy density, and intelligent functionalities like electrochromism and shape memory, surpassing conventional SC capabilities. Computational modeling, particularly density functional theory, is discussed to elucidate quantum capacitance mechanisms and interfacial charge dynamics, optimizing device performance. This novel integration of QMs with smart electrolytes, previously unexplored comprehensively in existing literature, addresses current research challenges and identifies future research directions, emphasizing scalable synthesis, multifunctional materials development, and extensive mechanistic investigations to bridge laboratory innovations and practical technological applications. Supercapacitor ; Smart material ; Quantum dot ; Electrochromism ; Energy storage ; Interface (matter) Supercapacitor Materials and Fabrication ; Covalent Organic Framework Applications ; MXene and MAX Phase Materials
Authors: A.Ramesh Babu, M.Sankara Rao, B.Nagaseshadri, P.Suresh, V. Prabhakar Rao, K.Sivalakshmi Koteswaramma, R.Chandra Mouli
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

Bio-Inspired and Ecofriendly Routes for Nanoparticle Production: A Review Bio-inspired synthesis of nanoparticles has become one of the most promising branches of green chemistry due to its low environmental impact, mild reaction conditions, and avoidance of toxic chemical reagents. Plants, microorganisms, enzymes, proteins, biopolymers, and other biomolecules possess intrinsic reducing and capping capacities, enabling the formation of metal and metal-oxide nanoparticles under ambient conditions. These green routes align strongly with the 12 principles of green chemistry and circumvent the ecological burden associated with conventional high-energy physicochemical methods. Recent studies have documented an expansion in the diversity of biological sources and mechanisms, with plant phytochemicals, microbial enzymes, and biomacromolecules offering precise control over nanoparticle nucleation, growth, surface functionalization, and stability. Despite remarkable advancements, key challenges remain, particularly in mechanistic understanding, extract variability, reproducibility, toxicity evaluation, scale-up, and regulatory compliance. This review presents an expanded, critical discussion of bio-derived nanoparticle synthesis, integrating advancements in plant-, microbe-, enzyme- and polymer-mediated strategies, incorporating recent mechanistic insights, technological developments, nanotoxicological evidence, engineering strategies for scale-up, and emerging hybrid biosynthetic approaches. Four comprehensive tables summarizing mechanisms, biological routes, applications, and characterization challenges are provided to enhance clarity. The review concludes with future perspectives emphasizing synthetic biology, omics-based profiling, in situ spectroscopic monitoring, continuous-flow microreactor systems, and AI-assisted optimization as essential tools to advance green nanotechnology toward industrial realization. Nanoparticle ; Green chemistry ; Biomolecule ; Reaction conditions ; Functional diversity Nanoparticles: synthesis and applications ; Enzyme Catalysis and Immobilization ; Advanced Nanomaterials in Catalysis
Authors: Norelyza Hussein
Source: International Journal of Advanced Biochemistry Research (article, 2025-01-01)
Field: Materials Science — Biomaterials

Inorganic metallic nanoparticles in dentistry from synthesis and characterization to clinical applications: A comprehensive review Inorganic metallic nanoparticles (NPs)-including silver (Ag), zinc oxide (ZnO), titanium dioxide (TiO₂), gold (Au), copper oxide (CuO), and iron oxide (Fe₃O₄)-represent a transformative advancement in dental materials science. At the nanoscale (1-100 nm), these materials exhibit distinctive physicochemical properties such as high surface-to-volume ratio, surface plasmon resonance (SPR), photocatalytic activity, and controlled ion release, all of which enable a wide spectrum of applications beyond the limitations of conventional passive restorations. The clinical potential of these nanoparticles derives from their tunable parameters, including size, shape, crystallinity, and surface functionalization. Various synthesis strategies-chemical reduction, physical ablation, sol-gel, hydrothermal, and environmentally friendly green biosynthesis-provide precise control over these properties. When incorporated into dental materials, nanoparticles enhance antimicrobial activity, reduce bacterial adhesion, promote remineralization, and enable photothermal or photoactivated disinfection. As summarized in Tables 1, 2, Ag and ZnO are especially effective in restorative and endodontic applications, TiO₂ demonstrates photocatalytic disinfection, and Au serves as a platform for advanced diagnostic and sensing technologies. Despite these benefits, significant barriers hinder clinical translation. As outlined in Table 3, challenges include cytotoxicity at high concentrations, nanoparticle aggregation in complex oral environments, formation of a biologically active protein corona, and the absence of standardized synthesis and regulatory frameworks. Addressing these concerns is crucial to ensuring reproducibility, long-term biocompatibility, and patient safety. Looking forward, the development of smart nanocomposites with pH-responsive ion release, multifunctional hybrid systems such as Ag-ZnO, AI-driven nanoparticle design, and integration into 3D printing technologies are highlighted as future directions (Table 4). By uniting advances in inorganic chemistry, nanotechnology, and clinical dentistry, inorganic metallic nanoparticles hold strong potential to reshape modern oral healthcare into a preventive, precise, durable, and biologically integrated practice. Nanoparticle ; Surface plasmon resonance ; Photocatalysis ; Characterization (materials science) ; Photothermal therapy ; Titanium dioxide ; Nanocomposite Nanoparticle-Based Drug Delivery ; Advanced Nanomaterials in Catalysis ; Nanomaterials for catalytic reactions
Authors: Tongyu Wang
Source: Science and Technology of Engineering Chemistry and Environmental Protection (article, 2025-12-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Applications of Nanotechnology in Electric Vehicles With the continuous development and large-scale application of electric vehicles (EVs), nanotechnology is widely used in this field. Nanotechnology can be used in many aspects of electric vehicles to enhance the performance of EVs, as well as their range. Nanotechnology holds significant value in parts of EVs including energy storage systems for electric vehicles such as batteries and supercapacitors, lightweight materials and energy efficiency. In terms of lithium-ion batteries, the unique properties of nanomaterials can greatly enhance their energy density, and since the lightweight material greatly reduces the mass of the electric vehicle, which allows nanotechnology to be better used in the field of electric vehicles. This article highlights the key findings and applications of nanotechnology today, analyzing its challenges which includes high costs, commercialization, and also security concerns. Subsequently, the article highlights future development through recent industry advancements and case studies, particularly emphasizing the opportunities arising from interaction with other fields such as artificial intelligence and within the environmental protection sector. Applications of nanotechnology ; Impact of nanotechnology ; Societal impact of nanotechnology ; Key (lock) ; Energy storage ; Electric energy Supercapacitor Materials and Fabrication ; Nanotechnology research and applications ; Environmental and Analytical Chemistry Studies
Authors: Shigeru Yamaguchi (148959)
Source: Figshare (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Computational Studyon Data Integration in ChiralCatalyst Design: A Case Study Using Michael Addition Reactions Data science in molecular catalysis often relies on the idea that molecules with similar structures show similar properties. This concept underlies many successful examples of regression analysis based on free energy relationships, typically using data sets in which substituents are varied on a single catalyst or substrate scaffold. Here, we present data integration for chiral catalyst design via regression using three-dimensional image-like descriptors (voxel data) derived from 718 TS (transition-state) structures calculated through DFT methods, along with their corresponding computed ΔΔ<i>G</i><sup>‡</sup> values (energy differences between TSs leading to each enantiomer). These data sets covered seven reaction types, including organocatalysis, transition-metal catalysis, and both Michael addition and Diels–Alder reactions. Regression using integrated data sets from seven pairs of distinct reaction systems enabled the design of chiral catalysts with improved computed enantioselectivity in all seven catalytic systems. To facilitate applications, we have released a web platform (https://mcds.riken.jp) that offers a data-driven design tool and a database of DFT-computed TS data. Catalysis ; Linear regression ; Substrate (aquarium) ; Michael reaction ; Regression ; Molecule ; Regression analysis Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Axial and Atropisomeric Chirality Synthesis
Authors: Shigeru Yamaguchi
Source: The Journal of Organic Chemistry (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Computational Study on Data Integration in Chiral Catalyst Design: A Case Study Using Michael Addition Reactions Data science in molecular catalysis often relies on the idea that molecules with similar structures show similar properties. This concept underlies many successful examples of regression analysis based on free energy relationships, typically using data sets in which substituents are varied on a single catalyst or substrate scaffold. Here, we present data integration for chiral catalyst design via regression using three-dimensional image-like descriptors (voxel data) derived from 718 TS (transition-state) structures calculated through DFT methods, along with their corresponding computed ΔΔ<i>G</i><sup>‡</sup> values (energy differences between TSs leading to each enantiomer). These data sets covered seven reaction types, including organocatalysis, transition-metal catalysis, and both Michael addition and Diels-Alder reactions. Regression using integrated data sets from seven pairs of distinct reaction systems enabled the design of chiral catalysts with improved computed enantioselectivity in all seven catalytic systems. To facilitate applications, we have released a web platform (https://mcds.riken.jp) that offers a data-driven design tool and a database of DFT-computed TS data. Catalysis ; Linear regression ; Substrate (aquarium) ; Michael reaction ; Regression ; Molecule ; Regression analysis Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis
Authors: Heegyu Kim | Taeyang Jeon | Seungtaek Choi | Jina Hong | Dong Won Jeon | Gyeongseon Baek | Geunjae Kwak | Dong‐Hee Lee | Ji‐Hoon Bae | Chihoon Lee | Yunseo Kim | Seon‐Jin Choi | Jin‐Seong Park | Sung Beom Cho | Hyunsouk Cho
Source: ArXiv.org (preprint, 2025-02-23)
Field: Materials Science — Materials Chemistry

Towards Fully-Automated Materials Discovery via Large-Scale Synthesis Dataset and Expert-Level LLM-as-a-Judge Materials synthesis is vital for innovations such as energy storage, catalysis, electronics, and biomedical devices. Yet, the process relies heavily on empirical, trial-and-error methods guided by expert intuition. Our work aims to support the materials science community by providing a practical, data-driven resource. We have curated a comprehensive dataset of 17K expert-verified synthesis recipes from open-access literature, which forms the basis of our newly developed benchmark, AlchemyBench. AlchemyBench offers an end-to-end framework that supports research in large language models applied to synthesis prediction. It encompasses key tasks, including raw materials and equipment prediction, synthesis procedure generation, and characterization outcome forecasting. We propose an LLM-as-a-Judge framework that leverages large language models for automated evaluation, demonstrating strong statistical agreement with expert assessments. Overall, our contributions offer a supportive foundation for exploring the capabilities of LLMs in predicting and guiding materials synthesis, ultimately paving the way for more efficient experimental design and accelerated innovation in materials science. Process (computing) ; Key (lock) ; Work (physics) ; Characterization (materials science) ; Outcome (game theory) ; Expert system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yue Zhang
Source: Transactions on Materials Biotechnology and Life Sciences (article, 2025-10-11)
Field: Chemistry — Organic Chemistry

Advances In The Application Of Click Chemistry In The Research Of Targeted Therapeutic Drugs Click chemistry has become an important tool in the research of targeted therapeutic drugs due to its high efficiency, specificity and mild reaction conditions. Cancer, as one of the major diseases that endanger human health, urgently needs more accurate and efficient treatment methods. Traditional targeted therapeutic methods have the problems of poor selectivity and easy to cause serious side effects, while click chemistry significantly improves the specificity and effect of treatment by efficiently connecting drugs with targeting ligands (such as antibodies, peptides, small molecules, etc.). Especially in the development of antibody-drug conjugates (ADCs) and nanodrug delivery systems, click chemistry not only optimizes the synthesis process, but also improves the stable release of drugs at the tumor site and reduces systemic toxicity. In addition, nanocarriers constructed based on click chemistry can further improve the targeting and bioavailability of drugs and achieve more effective treatment. Although the application of click chemistry in vivo still faces challenges such as reaction conditions, biocompatibility and safety, with the development of bioorthogonal reaction systems and the continuous emergence of new reactions, the application potential of click chemistry in the targeted therapy field will continue to expand. In the future, click chemistry is expected to be combined with cutting-edge technologies such as nanomedicine, immunotherapy, and artificial intelligence-assisted drug design to promote personalized and precision medicine to a higher level. Click chemistry ; Bioorthogonal chemistry ; Nanocarriers ; Drug delivery ; Personalized medicine ; Drug Click Chemistry and Applications ; Cancer Research and Treatment ; Protein Degradation and Inhibitors
Authors: Caichao Ye | Tao Feng | Weishu Liu | Wenqing Zhang
Source: arXiv (Cornell University) (preprint, 2025-03-11)
Field: Materials Science — Materials Chemistry

Functional Unit: A New Perspective on Materials Science Research Paradigms New materials have long marked the civilization level, serving as an impetus for technological progress and societal transformation. The classic structure-property correlations were key of materials science and engineering. However, the knowledge of materials faces significant challenges in adapting to exclusively data-driven approaches for new material discovery. This perspective introduces the concepts of functional units (FUs) to fill the gap in understanding of material structure-property correlations and knowledge inheritance as the "composition-microstructure" paradigm transitions to a data-driven AI paradigm transitions. Firstly, we provide a bird's-eye view of the research paradigm evolution from early "process-structure-properties-performance" to contemporary data-driven AI new trend. Next, we highlight recent advancements in the characterization of functional units across diverse material systems, emphasizing their critical role in multiscale material design. Finally, we discuss the integration of functional units into the new AI-driven paradigm of materials science, addressing both opportunities and challenges in computational materials innovation. Perspective (graphical) ; Paradigm shift ; Civilization ; Key (lock) ; Inheritance (genetic algorithm) Material Selection and Properties
Authors: O.U.K. Reddy | G. Jawahar Sagar | Tarkeshwar Mahto | Amit Yadav | Anand Kumar | Manoj Kumar Kar
Source:  (article, 2025-07-04)
Field: Materials Science — Surfaces, Coatings and Films

Quantum Computing for Enhanced Material Discovery and Optimization in Electric Vehicle Batteries The urgent need for high-performance, sustainable electric vehicle (EV) batteries has driven the exploration of advanced computational methods to accelerate material discovery. Traditional approaches, such as Density Functional Theory (DFT) and Hartree-Fock, face inherent limitations in simulating the complex quantum behaviors of novel battery materials. This paper introduces a pioneering framework leveraging quantum computing, specifically the Variational Quantum Eigen solver (VQE), to overcome these challenges and optimize solid-state battery materials. We focus on Lithium Thiophosphate (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{L i}_{3} \mathbf{P S} \mathbf{4}_{4}$</tex>), a promising electrolyte for next-generation batteries, and demonstrate how quantum simulations can provide a deeper understanding of electronic structures and electrochemical reactions at an unprecedented level of precision. By benchmarking quantum results against classical methods, we highlight the transformative potential of quantum algorithms to capture intricate electron correlations and reaction dynamics, offering more accurate predictions for material performance. Our findings suggest that quantum computing not only offers a significant leap in the accuracy of battery material simulations but also paves the way for scalable, data-driven optimization of next-generation energy storage systems. Quantum ; Quantum computer ; Battery (electricity) ; Solver ; Benchmark (surveying) ; Quantum dot ; Energy storage ; Quantum technology Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials ; Advanced Memory and Neural Computing
Authors: Kurnaz, Salih Can
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-06)
Field: Materials Science — Materials Chemistry

Prediction of Ambient-Temperature Superconductivity in Entropy-Stabilized Perovskite Hydrides: A Computational Approach via Meta-Wall Theory The discovery of high-temperature superconductivity in compressed hydrides (e.g., LaH10 at 250 K under 170 GPa) has renewed interest in room-temperature superconductors, yet the requirement of extreme pressures remains a fundamental obstacle. Here, we present a computational framework based on post-Migdal-Eliashberg theory to identify ambient-pressure superconductors by targeting the "Meta-Wall" regime. Through systematic screening, we identify Mg0.7Ca0.3PdH3 as a promising candidate exhibiting metallic character, thermodynamic stability at 1 atm, and a predicted critical temperature Tc ≈ 295 K. The material employs entropy stabilization via Ca doping to suppress phase separation. We propose a synthesis protocol incorporating mechanical alloying and high-pressure hydrogenation with kinetic trapping. This work demonstrates the viability of AI-driven computational materials design in circumventing the pressure barrier. Superconductivity ; Perovskite (structure) ; Work (physics) ; Density functional theory ; Entropy (arrow of time) ; Doping ; Phase (matter) ; Kinetic energy Hydrogen Storage and Materials ; Inorganic Chemistry and Materials ; High-pressure geophysics and materials