jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xue Jiang | Huadong Fu | Yang Bai | Lei Jiang | Hongtao Zhang | Weidong Wang | Peiwen Yun | Jingjin He | Dezhen Xue | Turab Lookman | Yanjing Su | Jianxin Xie
Source: Advanced Functional Materials (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials Abstract In recent years, data‐driven machine learning has significantly advanced the design of new materials and transformed the research and development landscape. However, its heavy reliance on data and the “black‐box” nature of its model‐mapping mechanisms have hindered its application in materials science research. Integrating material knowledge with machine learning to enhance model generalization and prediction accuracy remains an important objective. Such integration can deepen the understanding of material mechanisms by screening physical and chemical features to uncover explicit intrinsic relationships. Thus, it promotes the advancement of materials science, representing a promising avenue for artificial intelligence (AI) applications in this field. In this review, the algorithms, functionalities, and applications in materials underlying interpretable machine learning approaches are summarized and analyzed. The impact of composition and microstructure on material properties is explored and mathematical expressions for intrinsic relationships of materials are developed. In addition, recent advancements in data‐ and knowledge‐driven strategies for new material discovery, key property enhancement, multi‐objective design trade‐offs, and optimizing the entire preparation and processing workflow are reviewed. Finally, the future prospects and challenges associated with applying AI in materials science and its broader implications for the field are discussed. Materials science ; Nanotechnology ; Artificial intelligence ; Machine learning ; Systems engineering ; Computer science ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Emigdio Chávez‐Ángel | Martin Eriksen | Alejandro Castro‐Álvarez | José H. García | Marc Botifoll | Óscar Ávalos‐Ovando | Jordi Arbiol | Aitor Mugarza
Source: Advanced Intelligent Systems (article, 2025-03-02)
Field: Materials Science — Materials Chemistry

Applied Artificial Intelligence in Materials Science and Material Design Materials science has traditionally relied on a combination of experimental techniques and theoretical modeling to discover and develop new materials with desired properties. However, these processes can be time‐consuming, resource‐intensive, and often limited by the complexity of material systems. The advent of artificial intelligence (AI), particularly machine learning, has revolutionized materials science by offering powerful tools to accelerate the discovery, design, and characterization of novel materials. AI not only enhances the predictive modeling of material properties but also streamlines data analysis in techniques like X‐Ray diffraction, Raman spectroscopy, scanning probe microscopy, and electron microscopy. By leveraging large datasets, AI algorithms can identify patterns, reduce noise, and predict material behavior with unprecedented accuracy. In this review, recent advancements in AI applications across various domains of materials science, including spectroscopy, synchrotron studies, scanning probe and electron microscopies, metamaterials, atomistic modeling, molecular design, and drug discovery, are highlighted. It is discussed how AI‐driven methods are reshaping the field, making material discovery more efficient, and paving the way for breakthroughs in material design and real‐time experimental analysis. Artificial intelligence ; Computer science ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: I-Ting Sung | Y. S. Cheng | Chieh‐Ming Hsieh | Li‐Chiang Lin
Source: Industrial & Engineering Chemistry Research (review, 2025-01-15)
Field: Chemistry — Inorganic Chemistry

Machine Learning for Gas Adsorption in Metal–Organic Frameworks: A Review on Predictive Descriptors This review addresses a critical gap in the literature by focusing on the features (or descriptors) used in machine learning (ML) studies to predict gaseous adsorption properties in metal–organic frameworks (MOFs). Although ML approaches for predicting adsorption properties in MOFs have been extensively reported in recent years, features employed in ML models have not been thoroughly reviewed. A comprehensive review of these features is crucial since they form the foundation for building effective predictive models. These models are also key to facilitating the inverse design of MOFs, as they can be used to efficiently predict the performance of material candidates and explore the structure–property relationship, guiding the creation of optimal MOF structures. Furthermore, ML models can also be naturally employed in inverse design approaches, such as encoder–decoder architectures. This review starts with a brief overview of the importance and applications of MOFs in various fields, followed by a discussion of the historical milestones of MOFs in computational research, highlighting the critical role of ML. This review then discusses traditional features and introduces newly proposed distinctive features, referred to as “beyond traditional features”, that have been reported to date. Furthermore, generalized ML models for predicting the adsorption properties of different gases are also outlined. Finally, we offer future outlooks on ML-assisted computational searches for MOFs in adsorption applications. Overall, this review aims to help researchers grasp current developments and offer insights into future directions in this area. Adsorption ; Metal-organic framework ; Chemistry ; Computer science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Enhanced Oil Recovery Techniques
Authors: Mohammad Ramezanzadeh | Reza Ghamarpoor | Ali Dashan | Akram Fallah | Soolmaz Soleimani | Bahram Ramezanzadeh
Source: Advanced Composites and Hybrid Materials (review, 2025-06-01)
Field: Materials Science — Materials Chemistry

2D-Transition Metal Borides (MBenes): a comprehensive review of the materials, chemistry, advances, and novel applications MBenes and MAB phases are emerging classes of materials with unique properties and potential applications. MAB phases, first introduced in 2017, are ternary compounds with a layered structure similar to MAX phases but with boron replacing carbon. These materials have attracted significant attention due to their outstanding mechanical, electrical, and structural properties. MBene phases, derived from MAB phases through selective etching, are 2D materials analogous to MXenes, exhibiting high conductivity, chemical stability, and a broad range of applications. This review provides an in-depth analysis of MBene and MAB phases, including their structures, properties, synthesis methods, and applications. The history of MAB phase discovery and its evolution into MBene phases is discussed, along with advancements in synthesis techniques and the challenges associated with their production. The review also covers characterization methods for these materials, highlighting their distinct features. The properties of MBenes and MAB phases are explored, with a focus on their applications in energy storage, including supercapacitors and batteries, and their catalytic performance in reactions such as the Hydrogen Evolution Reaction (HER), Nitrogen Reduction Reaction (NRR), Carbon Dioxide Reduction Reaction (CO2RR), Oxygen Reduction Reaction (ORR), Nitric Oxide Reduction Reaction (NORR), and Carbon Capture and Storage (CCS). Additionally, their potential in biological and photocatalytic applications is discussed. Lastly, this review addresses the challenges associated with the synthesis and utilization of MBenes and MAB phases. By providing a comprehensive overview from their discovery to their current applications and limitations, this article aims to serve as a foundational resource for researchers and practitioners in the field. Transition metal ; Nanotechnology ; Materials science ; Engineering physics ; Chemistry ; Engineering ; Computer science MXene and MAX Phase Materials ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Priyanka Priyadarshini | Anshumika Mishra | Susanginee Nayak | Kulamani Parida
Source: Nanoscale (review, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

NH<sub>2</sub>-MIL-125(Ti) and its functional nanomaterials – a versatile platform in the photocatalytic arena Titanium (Ti)-based MOFs are promising materials known for their porosity, stability, diverse valence states, and a lower conduction band (CB) than Zr-MOFs. These features support stable ligand-to-metal charge transfer (LMCT) transitions under photoirradiation, enhancing photocatalytic performance. However, Ti-MOF structures remain a challenge owing to the highly volatile and hydrophilic nature of ionic Ti precursors. The discovery of MIL-125 marked a breakthrough in Ti-cluster coordination chemistry. Combining it with NH<sub>2</sub> chromophores to form NH<sub>2</sub>-MIL-125 enhanced its structural design and extended its activity into the visible light region. This review delves into the high-performance photocatalytic properties of NH<sub>2</sub>-MIL-125, focusing on its applications in H<sub>2</sub>O<sub>2</sub> and H<sub>2</sub> production, CO<sub>2</sub> and N<sub>2</sub> reduction, drug and dye degradation, photocatalytic sensors, and organic transformation reactions. The discussion considers the influence of the Ti precursor, coordination environment, synthesis process, and charge transfer mechanisms. Numerous strategic methods have been discussed to improve the performance of NH<sub>2</sub>-MIL-125 by incorporating linker modification, metal node modification, encapsulation of active species, and post-modification for enhancing light absorption ability, promoting charge separation, and improving photocatalytic efficiency. Moreover, future perspectives include methods to investigate how the efficiency of NH<sub>2</sub>-MIL-125-based materials can be planned in promoting research by highlighting their versatility and potential impacts in the area of photocatalysis. Photocatalysis ; Nanomaterials ; Materials science ; Nanotechnology ; Catalysis ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; MXene and MAX Phase Materials
Authors: Sreerama Amrutha Lahari | Nikhil Kumawat | Khairunnisa Amreen | R. N. Ponnalagu | Sanket Goel
Source: npj Clean Water (article, 2025-02-13)
Field: Chemistry — Electrochemistry

IoT integrated and deep learning assisted electrochemical sensor for multiplexed heavy metal sensing in water samples Abstract Heavy metal measurement is vital for ecological risk assessment and regulatory compliance. This study reports a sensor using gold nanoparticle-modified carbon thread electrodes for the simultaneous detection of Cd²⁺, Pb²⁺, Cu²⁺, and Hg²⁺ in water samples. Differential pulse voltammetry (DPV) was employed, achieving detection limits of 0.99 µM, 0.62 µM, 1.38 µM, and 0.72 µM, respectively, with a linear span of 1–100 µM. The sensor operated effectively in acidic conditions, with excellent selectivity, repeatability, and reproducibility. Real water samples from various lakes in Hyderabad, India, were analyzed to validate their practical application. To extract the sensing features a convolutional neural network (CNN) model was used to process DPV signals, enhancing heavy metal ion classification with high accuracy. Performance metrics such as precision, recall, and F1 score were evaluated. Integration with IoT technology has improved the user experience, advanced heavy metal quantification capabilities, and further enabled remote monitoring. Multiplexing ; Internet of Things ; Remote sensing ; Materials science ; Computer science ; Environmental science ; Telecommunications ; Embedded system ; Geology Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Biosensors and Analytical Detection
Authors: Ali Khodaie | Hamid Heidarzadeh
Source: Scientific Reports (article, 2025-04-19)
Field: Materials Science — Materials Chemistry

Ultra-sensitive surface plasmon resonance sensor integrating MXene (Ti3C2TX) and graphene for advanced carcinoembryonic antigen detection Carcinoembryonic antigen (CEA) is a critical biomarker for diagnosing and monitoring cancers such as liver, colon, and breast cancer. This study presents a highly sensitive surface plasmon resonance (SPR) sensor incorporating 2D materials-graphene and MXene (Ti<sub>3</sub>C<sub>2</sub>T<sub>X</sub>)- in a Kretschmann configuration. The sensor, comprising a BK7 prism, gold (Au), graphene, aluminum oxide (Al₂O₃), and MXene, is optimized for detecting CEA in aqueous solutions with high precision. The performance of the proposed sensor was evaluated using the finite difference time domain (FDTD) numerical method, focusing on key parameters such as sensitivity and figure of merit (FOM). Operating at a wavelength of 633 nm, the sensor achieved an exceptional sensitivity of 163.63 deg/RIU and an FOM of 17.52 RIU⁻¹, marking a significant improvement over previously reported SPR biosensors. Comparative analysis further underscores the superior performance of this design, establishing it as a cutting-edge tool for applications in biosensing, medical diagnostics, food safety, and environmental monitoring. The proposed sensor offers significant potential for real-world applications, including clinical diagnostics for early cancer detection, food safety monitoring, and environmental sensing. Carcinoembryonic antigen ; Surface plasmon resonance ; Graphene ; Materials science ; Nanotechnology ; Medicine ; Cancer ; Nanoparticle ; Internal medicine MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; Graphene research and applications
Authors: Tristan von Münchow | Neeraj Kumar Pandit | Suman Dana | Philipp Boos | Sven Erik Peters | Josselin Boucat | Yi‐Ru Liu | Alexej Scheremetjew | Lutz Ackermann
Source: Nature Catalysis (article, 2025-03-07)
Field: Chemistry — Organic Chemistry

Enantioselective C–H annulations enabled by either nickel- or cobalt-electrocatalysed C–H activation for catalyst-controlled chemodivergence Abstract Enantioselective electrocatalysis shows unique potential for the sustainable assembly of enantiomerically enriched molecules. This approach allows electro-oxidative C–H activation to be performed paired to the hydrogen evolution reaction. Recent progress has featured scarce transition metals with limited availability. Here we reveal that the earth-abundant 3 d transition metals nickel and cobalt exhibit distinctive performance for enantioselective electrocatalysis with chemodivergent reactivity patterns. Enantioselective desymmetrizations of strained bicyclic alkenes were achieved through C–H annulations. A data-driven optimization of chiral N , O -bidentate salicyloxazoline-type ligands was crucial for enhancing enantioselectivity in nickel electrocatalysis. Notably, in the transition state of the enantio-determining step, secondary weak attractive π – π and CH– π interactions were identified, reflecting the informed adaptations in the ligand design. Detailed mechanistic investigations by experimental and computational studies revealed for the nickel electrocatalysis a C–N bond-forming reductive elimination from nickel(III) and for the cobalt electrocatalysis a C–C bond-forming nucleophilic addition from cobalt(III) as the product-determining steps. Cobalt ; Nickel ; Catalysis ; Enantioselective synthesis ; Chemistry ; Inorganic chemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Radical Photochemical Reactions
Authors: Xingxue Zhang | Luping Wang | Kaiqiang Zhang | Kesong Zhou | Kaiyang Hou | Zhixun Zhao | Guiliang Li | Qing Yao | Sun Nan | Xu Wang
Source: Advanced Materials (article, 2025-01-19)
Field: Materials Science — Polymers and Plastics

Hybrid Soft Segments Boost the Development of Ultratough Thermoplastic Elastomers with Tunable Hardness The hardness of thermoplastic elastomers (TPEs) significantly influences their suitability for various applications, but traditionally, enhancing hardness reduces toughness. Herein a method is introduced that leverages hybrid soft segments to fine-tune the hardness of TPEs without compromising their exceptional toughness. Through the selective copolymerization of polytetramethylene ether glycols (PTMEGs) at various molecular weights, supramolecular poly(urethane-urea) TPEs are molecularly engineered to cover a wide spectrum of hardness while retaining good toughness. It is achieved through the formation of graded functional zones-densely packed for enhanced hardness and strength, and loosely packed for greater extensibility and toughness-driven by variations in PTMEG chain length and mismatched supramolecular interactions. Through the establishment and systematic investigation of a TPE library, the intricate interplay between design, structure, and performance of these materials is elucidated, refining the optimization techniques. The TPEs demonstrate exceptional mechanical properties, including a variant with a Shore hardness of 86A and a toughness of 819 MJ m<sup>-3</sup>, alongside a softer variant with a 59A hardness and a 786 MJ m<sup>-3</sup> toughness. The innovation extends to a scalable solvent-based TPE production line, promising widespread industrial application. This advancement reimagines the potential of high-performance TPEs and composites, offering versatile materials for demanding applications. Materials science ; Shore durometer ; Toughness ; Elastomer ; Thermoplastic elastomer ; Composite material ; Thermoplastic ; Compounding ; Fracture toughness ; Polymer ; Copolymer Polymer composites and self-healing ; Polymer Nanocomposites and Properties ; Silicone and Siloxane Chemistry
Authors: Ziao Chen | Lei Zou | Rong Cao | Yuan‐Biao Huang
Source: National Science Review (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

Porous covalent organic framework liquid for boosting CO2 adsorption and catalysis via dynamically expanding effect The features of intrinsic porosity and fluidity endow porous liquids (PLs) with unique properties and performance but the preparation of PLs remains challenging due to the difficulty in liquefying and keeping porous features at the same time. Herein, we develop a stepwise surface functionalization and ion exchange strategy to achieve a rare example of flexible covalent organic framework (COF)-based PL (COF-PLs). By coating the outer surface of the judiciously selected three-dimensional flexible COF-301 with an imidazolium salt corona, followed by liquefaction using the anion canopy potassium poly(ethylene glycol) sulfonate (PEGS) via electrostatic interactions, a flexible PL, COF-301-PL, can be obtained. Theoretical calculations and CO<sub>2</sub> adsorption experiments reveal that the cavities of COF-301-PL undergo dynamic adjustments in response to changes in CO<sub>2</sub> pressure. The dynamic expansion effect can not only provide additional gas adsorption capacity (7.04 mmol/g at 40 bar) but also facilitate the mass transfer of gas molecules during the catalytic process. Consequently, COF-301-PL exhibits superior catalytic efficiency for the conversion of CO<sub>2</sub> into cyclic carbonate by a factor of 24 and 17 compared to those of PEGS and COF-301 solid counterpart, respectively. The optimization of substrate adsorption and mass transfer conditions consequently improves the overall efficiency of catalytic reactions. This work offers a new perspective on the preparation of PLs and their great potential application for gas adsorption and catalysis. Boosting (machine learning) ; Covalent bond ; Covalent organic framework ; Adsorption ; Porosity ; Catalysis ; Materials science ; Chemical engineering ; Porous medium ; Nanotechnology ; Chemistry ; Organic chemistry ; Computer science ; Engineering ; Composite material ; Artificial intelligence Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Shichang Cai | Qing Wang | Naying Zhang | Chaoqun Chen | Hanlu Zhang | Yagang Feng | Lei Duan | Yapeng Cheng | Zihan Meng | Huaiguang Li | Jiabin Wu
Source: Carbon Neutralization (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Recent Progress in Halogen‐Doped Single‐Atom Catalysts for Electrochemical Reactions ABSTRACT Since the concept of single‐atom catalysts (SACs) was first proposed in 2011, related research has grown exponentially, establishing SACs as a highly active research field. Compared to conventional supported nanoparticle catalysts, SACs have attracted significant attention due to their theoretically highest atomic utilization efficiency and tunable active sites. Halogen atoms, with their high electronegativity, possess strong electron‐withdrawing ability, enabling them a powerful regulatory effect on the active sites. Although there are numerous comprehensive and high‐quality reviews on SACs, specialized research on halogen‐doped SACs is relatively scarce. Therefore, this article reviewed recent progress in halogen‐doped SACs, categorizing them by the four halogen atoms: fluorine (F), chlorine (Cl), bromine (Br), and iodine (I). We also discussed the application of halogen‐doped SACs in several key electrochemical reactions commonly relevant to clean energy storage and conversion, including hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), and CO 2 reduction reaction (CO 2 RR), and elaborated on the corresponding reaction mechanisms. Finally, this paper presented prospects to promote the development of SACs with tunable catalytic activity. Halogen ; Electrochemistry ; Catalysis ; Atom (system on chip) ; Doping ; Chemistry ; Materials science ; Inorganic chemistry ; Nanotechnology ; Physical chemistry ; Organic chemistry ; Computer science ; Electrode ; Optoelectronics Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Yurij Stetsyshyn | Halyna Ohar | Andrzej Budkowski | Giuseppe Lazzara
Source: Polymers (review, 2025-06-05)
Field: Chemistry — Organic Chemistry

Molecular Design and Role of the Dynamic Hydrogen Bonds and Hydrophobic Interactions in Temperature-Switchable Polymers: From Understanding to Applications Temperature-induced transitions in polymer systems, often governed by a phenomenon called critical solution temperatures (CSTs), lie on the basis of various advanced technologies such as tissues detachment, smart windows, enhanced DNA biosensors, etc. Despite this application-oriented progress, the molecular mechanisms of the temperature-induced transition based on CSTs remain often underexplored or weakly explained. In this review, we focus on the different molecular mechanisms driving CST-based transitions, systematizing information on homofunctional polymer systems. Understanding these mechanisms is crucial for manipulating temperature-sensitive properties, which offers significant potential for future innovations in smart materials. Polymer ; Smart material ; Nanotechnology ; Smart polymer ; Hydrogen bond ; Materials science ; Chemical physics ; Biosensor ; Chemistry ; Molecule ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Analytical Chemistry and Sensors ; Polymer Surface Interaction Studies