jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yasir Raza | Hassan Raza | Arslan Ahmed | M. M. Quazi | Muhammad Jamshaid | Muhammad Tuoqeer Anwar | Muhammad Nasir Bashir | Talha Younas | Ali Turab Jafry | Manzoore Elahi M. Soudagar
Source: Polymer Composites (article, 2025-05-12)
Field: Materials Science — Polymers and Plastics

Integration of response surface methodology ( <scp>RSM</scp> ), machine learning ( <scp>ML</scp> ), and artificial intelligence ( <scp>AI</scp> ) for enhancing properties of polymeric nanocomposites‐A review Abstract This review elucidates the amalgamation of machine learning (ML), artificial intelligence (AI), and response surface methodology (RSM) for the optimization of fabrication and the enhancement of the properties of polymeric nanocomposites. It analyzes recent accomplishments, methodologies, and future possibilities in this interdisciplinary field. Polymers and their nanocomposites are garnering attention because of their cost‐effectiveness, biodegradability, and non‐toxicity. Polymeric nanocomposites have been employed in several technical applications; nevertheless, their restricted mechanical, electrical, and thermal properties have impeded their extensive use. Numerous additives, including clay, fiber, and two‐dimensional materials such as graphene or MoS 2 , were extensively employed as nanofillers to enhance their qualities. The effects of filler concentration are thoroughly examined by conventional approaches; however, optimization via statistical techniques may be more suitable. The optimization method produces accurate results with a reduced number of tests. Diverse statistical techniques, including Taguchi and RSM, alongside ML algorithms, can be employed to ascertain the optimal filler concentration, type, fabrication method, characterization, and process parameters to enhance the properties, manufacturing, or efficiency of polymers or polymer‐based nanocomposites. The response surface methodology (RSM) produces superior results compared to Taguchi and conventional methods. Nonetheless, ML/AI can also be utilized to attain additional improvements in the requisite mechanical, thermal, electrical, and electrochemical properties. Recent advancements in the optimization of polymeric nanocomposites are emphasized, and the use of machine learning and artificial intelligence techniques is proposed for future progress. Highlights Summarized techniques to enhance polymeric nanocomposites properties. Presented process, production, and additive optimization of various polymers. Summarized statistical techniques and ML‐based nanocomposites efficiency. Future directions: ML and AI to improve polymeric nanocomposite properties. Materials science ; Response surface methodology ; Nanocomposite ; Composite material ; Computer science ; Machine learning Conducting polymers and applications ; Polymer Nanocomposites and Properties ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Yingjie Zheng | Yi Zhao | Wen Luo | Yifan Zhang | Yong Wang | Yang Wu
Source: Catalysts (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Rational Design Strategies for Covalent Organic Frameworks Toward Efficient Electrocatalytic Hydrogen Peroxide Production Hydrogen peroxide (H2O2) is a versatile and environmentally friendly oxidant with broad applications in industry, energy, and environmental remediation. Electrocatalytic H2O2 production via the two-electron oxygen reduction reaction (2e− ORR) has emerged as a sustainable alternative to traditional anthraquinone processes. Covalent organic frameworks (COFs), as a class of crystalline porous materials, exhibit high structural tunability, large surface areas, and chemical stability, making them promising electrocatalysts for 2e− ORR. This review systematically summarizes recent advances in COF-based electrocatalysts for H2O2 production, including both metal-free and metal-containing systems. We discuss key strategies in COF design—such as dimensional modulation, linkage engineering, heteroatom doping, and post-synthetic modification—and highlight their effects on activity, selectivity, and stability. Fundamental insights into the 2e− ORR mechanism and evaluation metrics are also provided. Finally, we offer perspectives on current challenges and future directions, emphasizing the integration of machine learning, conductivity enhancement, and scalable synthesis to advance COFs toward practical H2O2 electrosynthesis. Hydrogen peroxide ; Rational design ; Chemistry ; Production (economics) ; Biochemical engineering ; Combinatorial chemistry ; Nanotechnology ; Organic chemistry ; Materials science ; Engineering Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Yonghyuk Lee | Taehun Lee
Source: Journal of the American Chemical Society (article, 2025-02-19)
Field: Materials Science — Materials Chemistry

Machine-Learning-Accelerated Surface Exploration of Reconstructed BiVO<sub>4</sub>(010) and Characterization of Their Aqueous Interfaces Understanding the semiconductor-electrolyte interface in photoelectrochemical (PEC) systems is crucial for optimizing the stability and reactivity. Despite the challenges in establishing reliable surface structure models during PEC cycles, this study explores the complex surface reconstructions of BiVO<sub>4</sub>(010) by employing a computational workflow integrated with a state-of-the-art active learning protocol for a machine-learning interatomic potential and global optimization techniques. Within this workflow, we identified 494 unique reconstructed surface structures that surpass conventional chemical intuition-driven, bulk-truncated models. After constructing the surface Pourbaix diagram under Bi- and V-rich electrolyte conditions using density functional theory and hybrid functional calculations, we proposed structural models for the experimentally observed Bi-rich BiVO<sub>4</sub> surfaces. By performing hybrid functional molecular dynamics simulations with the explicit treatment of water molecules on selected reconstructed BiVO<sub>4</sub>(010) surfaces, we observed water dissociation from molecular water. Our findings demonstrate significant water dissociation on reconstructed Bi-rich surfaces, highlighting the critical role of bare and undercoordinated Bi sites (only observable in reconstructed surfaces) in driving hydration processes. Our work establishes a foundation for understanding the role of complex, reconstructed Bi surfaces in surface hydration and reactivity. Additionally, our theoretical framework for exploring surface structures and predicting reactivity in multicomponent oxides offers a precise approach to describing complex surface and interface processes in PEC systems. Dissociation (chemistry) ; Density functional theory ; Chemistry ; Molecular dynamics ; Surface reconstruction ; Chemical physics ; Hybrid functional ; Workflow ; Molecule ; Pourbaix diagram ; Self-ionization of water ; Aqueous solution ; Nanotechnology ; Surface (topology) ; Computational chemistry ; Physical chemistry ; Materials science ; Computer science ; Electrochemistry ; Geometry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Benjamin W. J. Chen | Manos Mavrikakis
Source: Nature Chemical Engineering (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts Adsorbates often cover the surfaces of catalysts densely as they carry out reactions, dynamically altering their structure and reactivity. Understanding adsorbate-induced phenomena and harnessing them in our broader quest for improved catalysts is a substantial challenge that is only beginning to be addressed. Here we chart a path toward a deeper understanding of such phenomena by focusing on emerging in silico modeling methodologies, which will increasingly incorporate machine learning techniques. We first examine how adsorption on catalyst surfaces can lead to local and even global structural changes spanning entire nanoparticles, and how this affects their reactivity. We then evaluate current efforts and the remaining challenges in developing robust and predictive simulations for modeling such behavior. Last, we provide our perspectives in four critical areas—integration of artificial intelligence, building robust catalysis informatics infrastructure, synergism with experimental characterization, and adaptive modeling frameworks—that we believe can help surmount the remaining challenges in rationally designing catalysts in light of these complex phenomena. Understanding adsorbate-induced phenomena and leveraging them in the design of improved catalysts presents an exciting challenge that is only beginning to be addressed. This Review explores how cutting-edge computations, combined with in situ and operando experiments, can unravel the dynamic interplay between adsorbates and catalysts, and how these interactions can be used for rational catalyst design. Reactivity (psychology) ; Catalysis ; Computer science ; Materials science ; Chemistry ; Organic chemistry ; Medicine Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Stefan Ganscha | Oliver T. Unke | Daniel Ahlin | Hartmut Maennel | Sergii Kashubin | Klaus-Robert Müller
Source: Scientific Data (article, 2025-03-08)
Field: Materials Science — Materials Chemistry

The QCML dataset, Quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations Machine learning (ML) methods enable prediction of the properties of chemical structures without computationally expensive ab initio calculations. The quality of such predictions depends on the reference data that was used to train the model. In this work, we introduce the QCML dataset: A comprehensive dataset for training ML models for quantum chemistry. The QCML dataset systematically covers chemical space with small molecules consisting of up to 8 heavy atoms and includes elements from a large fraction of the periodic table, as well as different electronic states. Starting from chemical graphs, conformer search and normal mode sampling are used to generate both equilibrium and off-equilibrium 3D structures, for which various properties are calculated with semi-empirical methods (14.7 billion entries) and density functional theory (33.5 million entries). The covered properties include energies, forces, multipole moments, and other quantities, e.g., Kohn-Sham matrices. We provide a first demonstration of the utility of our dataset by training ML-based force fields on the data and applying them to run molecular dynamics simulations. Chemical space ; Quantum chemistry ; Computer science ; Sampling (signal processing) ; Density functional theory ; Ab initio ; Statistical physics ; Multipole expansion ; Chemistry ; Molecule ; Data mining ; Computational chemistry ; Physics ; Quantum mechanics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Awais Ali | Saifuddin Ahmed | Wei Jiang | Gyungse Park | Soong Ju Oh
Source: Current Opinion in Electrochemistry (article, 2025-01-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Harnessing MOF intrinsic properties for enhanced supercapacitor performance Supercapacitor ; Materials science ; Nanotechnology ; Chemistry ; Capacitance ; Electrode Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Battery Materials
Authors: Felix Laufer | Markus Götz | Ulrich W. Paetzold
Source: Energy & Environmental Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication Reproducible large-area fabrication is one of the remaining challenges for the commercialization of perovskite photovoltaics. Imaging methods augmented with deep learning (DL) enable in-line detection of spatial or temporal inconsistencies and predict the impact of observed changes on device performance. In this work, we showcase three use cases of how DL augments complex experimental data analysis of the large-area perovskite thin film formation, even on moderate-sized datasets. First, we demonstrate material composition monitoring by differentiating between precursor property variations, ensuring material consistency during fabrication. Second, we provide early thin-film quality assessment by predicting holistic device performance even before its finalization. Finally, we extend the approach from parameter prediction to generating recommendations for process control by forecasting monitoring signals as a function of a variable process parameter and predicting the corresponding device performances. By addressing tasks that are hardly possible for humans to solve, we present how DL augments data analysis by transforming experimental data into predictions of target parameters. Fabrication ; Scalability ; Materials science ; Perovskite (structure) ; Process (computing) ; Thin film ; Computer science ; Deep learning ; Nanotechnology ; Optoelectronics ; Artificial intelligence ; Engineering ; Medicine ; Chemical engineering ; Operating system Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Qilong Wang | Yujian Liu | Chenchen Li | Shidang Xu | Bin Liu
Source: Advanced Science (review, 2025-06-19)
Field: Materials Science — Biomaterials

Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery In recent years, nanomedicine has emerged as a promising approach to deliver therapeutic agents directly to tumors. However, despite its potential, cancer nanomedicine encounters significant challenges. The synthesis of nanomedicines involves numerous parameters, and the complexity of nano-bio interactions in vivo presents further difficulties. Therefore, innovative approaches are needed to optimize nanoparticle (NP) design and functionality, enhancing their delivery efficiency and therapeutic outcomes. Recent advancements in Machine Learning (ML) and computational methods have shown great promise for precision cancer drug delivery. This review summarizes the potential use of ML across all stages of NP drug delivery systems, along with a discussion of ongoing challenges and future directions. The authors first examine the synthesis and formulation of NPs, highlighting how ML can accelerate the process by searching for optimal synthesis parameters. Next, they delve into nano-bio interactions in drug delivery, including NP-protein interactions, blood circulation, NP extravasation into the tumor microenvironment (TME), tumor penetration and distribution, as well as cellular internalization. Through this comprehensive overview, the authors aim to highlight the transformative potential of ML in overcoming current challenges, assisting nanoscientists in the rational design of NPs, and advancing precision cancer nanomedicine. Nanomedicine ; Drug delivery ; Nanotechnology ; Computer science ; Cancer therapy ; Cancer ; Nanoparticle ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; Machine Learning in Materials Science ; Graphene and Nanomaterials Applications
Authors: Yubeen Lee | Izzati Haizan | Sang Baek Sim | Jin‐Ha Choi
Source: Biosensors (review, 2025-06-05)
Field: Materials Science — Materials Chemistry

Colorimetric Biosensors: Advancements in Nanomaterials and Cutting-Edge Detection Strategies Colorimetric-based biosensors are practical detection devices that can detect the presence and concentration of biomarkers through simple color changes. Conventional laboratory-based tests are highly sensitive but require long processing times and expensive equipment, which makes them difficult to apply for on-site diagnostics. In contrast, the colorimetric method offers advantages for point-of-care testing and real-time monitoring due to its flexibility, simple operation, rapid results, and versatility across many applications. In order to enhance the color change reactions in colorimetric techniques, functional nanomaterials are often integrated due to their desirable intrinsic properties. In this review, the working principles of nanomaterial-based detection strategies in colorimetric systems are introduced. In addition, current signal amplification methods for colorimetric biosensors are comprehensively outlined and evaluated. Finally, the latest trends in artificial intelligence (AI) and machine learning integration into colorimetric-based biosensors, including their potential for technological advancements in the near future, are discussed. Future research is expected to develop highly sensitive and multifunctional colorimetric methods, which will serve as powerful alternatives for point-of-care testing and self-testing. Flexibility (engineering) ; Computer science ; Colorimetry ; Point-of-care testing ; Biosensor ; Nanotechnology ; Biochemical engineering ; Materials science ; Engineering ; Mathematics Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Shuang Lin | Minglang Dong | Chi Li | Xiang Lin | Cong Yan | Wen Xu | Zhouzhou Bao | Bin Dong
Source: Advanced Functional Materials (article, 2025-06-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning‐assisted Ultrasensitive SERS Immunoassays Across Wide Concentration Ranges Toward Clinical Ovarian Cancer Diagnosis Abstract The development of plasmonic devices featuring high‐density hotspots with wide linear response ranges has become critically essential for ultrasensitive carbohydrate antigen 125 (CA125) biomarker detection, enabling both early‐stage diagnosis and real‐time monitoring of ovarian cancer. In this study, an innovative nanophotonic platform is developed through assembling dual‐layers Au nanorods (NRs) within the SiO 2 microbowls for ultrasensitive detection of CA125 biomarkers. The interlaced nanorods inside the microbowl structure can fully utilize the light reflection and focus the energy of the light field, resulting in significantly amplified near‐field enhancements effect and exceptionally sensitive surface‐enhanced Raman scattering (SERS) performance with an analytical enhancement factor (AEF) of 1.09 × 10 8 . More importantly, the 3D nanogap of the NRIB configuration enables extensive contact with large CA125 protein molecules, facilitating the acquisition of their comprehensive SERS signal. As a proof of concept, a cost‐effective NRIB‐based SERS immunoassay platform is fabricated for sensitive CA125 detection across an ultra‐wide concentration range from 1 to 5000 U mL −1 through machine learning‐assisted analysis. The concentration analysis results of CA125 in serum samples from both healthy donors and ovarian cancer patients showed a strong correlation with clinical hospital tests. This hierarchical cavity‐enhanced platform, integrated with machine learning algorithms, is promising for early disease diagnosis and continuous patient monitoring. Materials science ; Ovarian cancer ; Nanotechnology ; Cancer detection ; Immunoassay ; Cancer ; Cancer biomarkers ; Medicine ; Internal medicine ; Immunology Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Biosensing Techniques and Applications ; Biosensors and Analytical Detection
Authors: Chunmei Tang | Baoyin Yuan | Xiaohan Zhang | Fangyuan Zheng | Qingwen Su | Ling Meng | Lei Du | Dongxiang Luo | Yoshitaka Aoki | Ning Wang | Siyu Ye
Source: Advanced Energy Materials (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Rationally Designed Air Electrode Boosting Electrochemical Performance of Protonic Ceramic Cells Abstract Protonic ceramic cells (PCCs) have gained significant attention as a promising electrochemical device for hydrogen production and power generation at intermediate temperatures. However, the lack of high‐performance air electrodes, specifically in terms of proton conduction ability, has severely hindered the improvement of electrochemical performances for PCCs. In this study, a high‐efficiency air electrode La 0.8 Ba 0.2 CoO 3 (LBC) is rationally designed and researched by a machine‐learning model and density functional theory (DFT) calculation, which boosts the performances of PCCs. Specifically, an elements‐property map for designing high‐efficiency oxides is created by predicting and studying the proton uptake ability of La 1– x A′ x BO 3 (A′ = Na, K, Ca, Mg, Ba, Cu, etc.) by an eXtreme Gradient Boosting model. PCC with LBC air electrode yields high current destiny in electrolysis mode (1.72 A cm −2 at 600 °C) and power density in fuel cell mode (1.00 W cm −2 at 600 °C). In addition, an ultra‐low air electrode reaction resistance (0.03 Ω cm 2 at 600 °C) is achieved, because LBC can significantly facilitate the formation of O 2 * . This work not only reports an effective air electrode but also presents a new avenue for the rational design of air electrodes for PCCs. Materials science ; Electrode ; Electrochemistry ; Ceramic ; Power density ; Electrolysis ; Reversible hydrogen electrode ; Chemical engineering ; Nanotechnology ; Reference electrode ; Composite material ; Power (physics) ; Electrolyte ; Thermodynamics ; Physical chemistry ; Chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Thermal Expansion and Ionic Conductivity
Authors: Maruthasalam Pannerselvam | V. Siva | A. Murugan | A. Shameem | Thirugnanam Bavani | Sahadevan Jhelai | S. Shanmugan | Imran Hussain Showkath Ali | Karthik Kannan
Source: Nanomaterials (article, 2025-04-03)
Field: Chemistry — Inorganic Chemistry

Rational Design of Core–Shell MoS2@ZIF-67 Nanocomposites for Enhanced Photocatalytic Degradation of Tetracycline Zeolitic imidazolate frameworks (ZIFs) and their composites are attractive materials for photocatalytic applications due to their distinct characteristics. Core-shell ZIFs have lately emerged as a particularly appealing type of metal-organic frameworks, with improved light-absorption and charge-separation capabilities. In this study, hybrid nanocomposite materials comprising a zeolitic imidazolate framework-67 and molybdenum disulfide (MoS<sub>2</sub>) were fabricated with a core-shell structure. The prepared core-shell MoS<sub>2</sub>@ZIF-67 nanocomposites were studied using XRD, FTIR, XPS, and HR-TEM techniques. The crystalline nature and the presence of characteristic functional groups of the composites were analyzed using XRD and FTIR, respectively. The photocatalytic degradation of antibiotic tetracycline (TC) was measured using visible light irradiation. Compared to pristine MoS<sub>2</sub> (12%) and ZIF-67 (34%), the most active MoS<sub>2</sub>@ZIF-67 nanocomposite (72%) exhibited a greater tetracycline degradation efficacy. Nanocomposite ; Photocatalysis ; Materials science ; Fourier transform infrared spectroscopy ; Zeolitic imidazolate framework ; Imidazolate ; X-ray photoelectron spectroscopy ; Molybdenum disulfide ; Chemical engineering ; Degradation (telecommunications) ; Composite material ; Metal-organic framework ; Chemistry ; Catalysis ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; MXene and MAX Phase Materials