jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pelin Sezgin | Ezgi Gulcay-Ozcan | Marija Vučkovski | Aleksandra M. Bondžić | İlknur Eruçar | Seda Keskın
Source: Industrial & Engineering Chemistry Research (review, 2025-01-14)
Field: Chemistry — Inorganic Chemistry

Biomedical Applications of Metal–Organic Frameworks Revisited Metal-organic frameworks (MOFs) have been shown to be great alternatives to traditional porous materials in various chemical applications, and they have been very widely studied for biomedical applications in the past decade specifically for drug storage. After our review published in 2011 [Keskin and Kızılel, <i>Ind</i>. <i>Eng</i>. <i>Chem</i>. <i>Res</i>. 2011, 50 (4), 1799-1812, 10.1021/ie101312k], we have witnessed a very fast growth not only in the number and variety of MOFs but also in their usage across a broad spectrum of biomedical fields. With the recent integration of molecular modeling and data science approaches to the experimental studies, biomedical applications of MOFs have been significantly accelerated positioning them as pivotal components in the regenerative medicine, medical imaging, and diagnostics. In this review, we visited the diverse biomedical applications of MOFs considering the recent experimental and computational efforts on drug storage and delivery, bioimaging, and biosensing. We focused on the underlying mechanisms governing the molecular interactions between MOFs and biological systems and discussed both the opportunities and challenges in the field to highlight the potential of MOFs in advanced therapeutics for cancer and neurological diseases. Metal-organic framework ; Computer science ; Biochemical engineering ; Nanotechnology ; Chemistry ; Materials science ; Engineering ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Machine Learning in Materials Science
Authors: Cerys M. Cormican | Sinem Bektaş | Francisco J. Martín‐Martínez | Shirin Alexander
Source: Advanced Materials (review, 2025-02-18)
Field: Materials Science — Surfaces, Coatings and Films

Emerging Trends in Bioinspired Superhydrophobic and Superoleophobic Sustainable Surfaces Inspired by nature's ability to master materials for performance and sustainability, biomimicry has enabled the creation of bioinspired materials for structural color, superadhesion, hydrophobicity and hydrophilicity, among many others. This review summarizes the emerging trends in novel sustainable fluorocarbon-free bioinspired designs for creating superhydrophobic and superoleophobic surfaces. It discusses methods, challenges, and future directions, alongside the impact of computational modeling and artificial intelligence in accelerating the experimental development of more sustainable surface materials. While significant progress is made in superhydrophobic materials, sustainable superoleophobic surfaces remain a challenge. However, bioinspiration and experimental techniques supported by computational platforms are paving the way to new renewable and biodegradable repellent surfaces that meet environmental standards without sacrificing performance. Nevertheless, despite environmental concerns, and policies, several bioinspired designs still continue to apply fluorination and other environmentally harmful techniques to achieve the required standard of repellency. As discussed in this critical review, a new paradigm that integrates advanced materials characterization, nanotechnology, additive manufacturing, computational modeling, and artificial intelligence is coming, to generate bioinspired materials with tailored superhydrophobicity and superoleophobicity while adhering to environmental standards. Nanotechnology ; Materials science ; Biomimetics ; Biomimetic materials ; Sustainability ; Characterization (materials science) ; Environmentally friendly ; Systems engineering ; Biochemical engineering ; Engineering Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Aerogels and thermal insulation
Authors: Xiangyu Wen | Xiao Geng | Guosheng Su | Yizheng Li | Qidong Li | Yuxuan Yi | Lifen Liu
Source: Green Chemistry (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-driven design of single-atom catalysts for carbon dioxide valorization to high-value chemicals: a review of photocatalysis, electrocatalysis, and thermocatalysis The research about photocatalytic, electrocatalytic, and thermocatalytic CO 2 reduction, with single-atom catalysts (SACs). Photocatalysis ; Electrocatalyst ; Catalysis ; Carbon dioxide ; Chemistry ; Carbon atom ; Titanium dioxide ; Atom (system on chip) ; Chemical engineering ; Materials science ; Organic chemistry ; Metallurgy ; Computer science ; Electrochemistry ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Paulo C. T. Souza | Luís Borges-Araújo | Christopher Brasnett | Rodrigo A. Moreira | Fabian Grünewald | Peter Park | Liguo Wang | Hafez Razmazma | Ana C. Borges-Araújo | Luis Fernando Cofas‐Vargas | Luca Monticelli | Raúl Mera‐Adasme | Manuel N. Melo | Sangwook Wu | ‪Siewert J. Marrink | Adolfo B. Poma | Sebastian Thallmair
Source: Nature Communications (article, 2025-04-30)
Field: Materials Science — Materials Chemistry

GōMartini 3: From large conformational changes in proteins to environmental bias corrections Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access to spatio-temporal scales beyond all-atom based approaches. The GōMartini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency and accurate representation of protein dynamics with the capabilities of studying proteins in different biological environments. This paper introduces an enhanced GōMartini model, which combines a virtual-site implementation of Gō models with Martini 3. The implementation has been extensively tested by the community since the release of the reparametrized version of Martini. This work demonstrates the capabilities of the model in diverse case studies, ranging from protein-membrane binding to protein-ligand interactions and AFM force profile calculations. The model is also versatile, as it can address recent inaccuracies reported in the Martini protein model. Lastly, the paper discusses the advantages, limitations, and future perspectives of the Martini 3 protein model and its combination with Gō models. Chemistry ; Computational biology ; Biophysics ; Biology Enzyme Structure and Function ; Protein Structure and Dynamics ; Alzheimer's disease research and treatments
Authors: Yue Gong | Ruihuan Duan | Yi Hu | Yao Wu | Song Zhu | Xingli Wang | Qijie Wang | Shu Ping Lau | Zheng Liu | Beng Kang Tay
Source: Nature Communications (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine vision Hardware implementation of reconfigurable and nonvolatile photoresponsivity is essential for advancing in-sensor computing for machine vision applications. However, existing reconfigurable photoresponsivity essentially depends on the photovoltaic effect of p-n junctions, which photoelectric efficiency is constrained by Shockley-Queisser limit and hinders the achievement of high-performance nonvolatile photoresponsivity. Here, we employ bulk photovoltaic effect of rhombohedral (3R) stacked/interlayer sliding tungsten disulfide (WS<sub>2</sub>) to surpass this limit and realize highly reconfigurable, nonvolatile photoresponsivity with a retinomorphic photovoltaic device. The device is composed of graphene/3R-WS<sub>2</sub>/graphene all van der Waals layered structure, demonstrating a wide range of nonvolatile reconfigurable photoresponsivity from positive to negative ( ± 0.92 A W<sup>-1</sup>) modulated by the polarization of 3R-WS<sub>2</sub>. Further, we integrate this system with a convolutional neural network to achieve high-accuracy (100%) color image recognition at σ = 0.3 noise level within six epochs. Our findings highlight the transformative potential of bulk photovoltaic effect-based devices for efficient machine vision systems. Tungsten disulfide ; Materials science ; Graphene ; Optoelectronics ; Computer science ; Photovoltaic system ; Photovoltaics ; van der Waals force ; Photovoltaic effect ; Nanotechnology ; Electrical engineering ; Physics ; Molecule 2D Materials and Applications ; Advanced Memory and Neural Computing ; Perovskite Materials and Applications
Authors: Qin Xu | Qinghang Wang | Xinbing Zhao | Shouxin Xia | Li Wang | Yuhui Zhang | Chao He | D.L. Chen | Bin Jiang
Source: Scripta Materialia (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

PCS: Property-composition-structure chain in Mg-Nd alloys through integrating sigmoid fitting and conditional generative adversarial network modeling Sigmoid function ; Materials science ; Property (philosophy) ; Chain (unit) ; Generative grammar ; Composition (language) ; Generative adversarial network ; Adversarial system ; Biological system ; Artificial intelligence ; Computer science ; Artificial neural network ; Physics ; Deep learning Nuclear Materials and Properties ; Magnesium Alloys: Properties and Applications ; Surface Treatment and Coatings
Authors: Tianyi Wu | Sina Kheiri | Riley J. Hickman | Huachen Tao | Tony Wu | Zhibo Yang | Xin Ge | Wei Zhang | Milad Abolhasani | Kun Liu | Alán Aspuru‐Guzik | Eugenia Kumacheva
Source: Nature Communications (article, 2025-02-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-driving lab for the photochemical synthesis of plasmonic nanoparticles with targeted structural and optical properties Many applications of plasmonic nanoparticles require precise control of their optical properties that are governed by nanoparticle dimensions, shape, morphology and composition. Finding reaction conditions for the synthesis of nanoparticles with targeted characteristics is a time-consuming and resource-intensive trial-and-error process, however closed-loop nanoparticle synthesis enables the accelerated exploration of large chemical spaces without human intervention. Here, we introduce the Autonomous Fluidic Identification and Optimization Nanochemistry (AFION) self-driving lab that integrates a microfluidic reactor, in-flow spectroscopic nanoparticle characterization, and machine learning for the exploration and optimization of the multidimensional chemical space for the photochemical synthesis of plasmonic nanoparticles. By targeting spectroscopic nanoparticle properties, the AFION lab identifies reaction conditions for the synthesis of different types of nanoparticles with designated shapes, morphologies, and compositions. Data analysis provides insight into the role of reaction conditions for the synthesis of the targeted nanoparticle type. This work shows that the AFION lab is an effective exploration platform for on-demand synthesis of plasmonic nanoparticles. Plasmon ; Nanoparticle ; Nanotechnology ; Materials science ; Photochemistry ; Chemistry ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Copper-based nanomaterials and applications
Authors: Qi Wang | Yonggang Yao
Source: npj Computational Materials (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction High-entropy alloys (HEAs) have emerged as promising candidates for catalyst applications due to their inherent compositional, structural, and site-level diversities, which enable highly tunable catalytic properties. However, these complexities pose grand challenges for traditional “trial-and-error” experimentation or computationally expensive “brute-force” ab initio calculations. Machine learning (ML) demonstrates great potential to address these challenges by establishing efficient, scalable mappings from composition, structure or site environment to HEA properties. Among these properties, adsorption energy, which quantifies the binding strength between catalytic intermediates and surface sites, is a crucial indicator of catalytic activity. This review provides a comprehensive overview of ML-driven strategies for adsorption energy prediction in the context of HEAs. Two primary strategies are introduced: “direct” prediction from unrelaxed structure and “iterative” prediction via ML potential-guided relaxation modeling. Both strategies can leverage handcrafted features or end-to-end frameworks such as graph neural networks. We also discuss how pretrained models on large-scale databases can extend to out-of-domain HEA systems. Beyond methodology, we address key challenges and future directions, including benchmarking ML strategies, developing HEA-specific datasets, pretraining and fine-tuning, integrating chained ML models, advancing multi-objective optimization, and bridging ML predictions with experimental validation. By critically evaluating existing strategies and highlighting emerging trends, this review underscores the critical role of ML in advancing adsorption energy predictions, offering a foundation for accelerating the discovery and optimization of HEA catalysts. Alloy ; Adsorption ; Focus (optics) ; Entropy (arrow of time) ; High entropy alloys ; Materials science ; Computer science ; Artificial intelligence ; Process engineering ; Thermodynamics ; Chemistry ; Metallurgy ; Engineering ; Physics ; Physical chemistry ; Optics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Qinghang Wang | Qin Xu | Shouxin Xia | Li Wang | Weiqi Wang | Weiying Huang | Yan Song | Weineng Tang | D.L. Chen
Source: Journal of Magnesium and Alloys (article, 2025-01-31)
Field: Materials Science — Biomaterials

Interpretable machine learning excavates a low-alloyed magnesium alloy with strength-ductility synergy based on data augmentation and reconstruction • This work proposed an interpretable ML method based on data augmentation and reconstruction. • The model's prediction accuracy exceeded 95 % (R 2 ) for UTS and EL. • A new as-extruded MZAX2000 alloy with a strength-ductility synergy was developed. • Heterogeneous fibrous structure acted a crucial role in enhancing the strength and ductility of low-alloyed mg alloys. The application of machine learning in alloy design is increasingly widespread, yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships. This work proposes an interpretable machine learning method based on data augmentation and reconstruction, excavating high-performance low-alloyed magnesium (Mg) alloys. The data augmentation technique expands the original dataset through Gaussian noise. The data reconstruction method reorganizes and transforms the original data to extract more representative features, significantly improving the model's generalization ability and prediction accuracy, with a coefficient of determination (R 2 ) of 95.9 % for the ultimate tensile strength (UTS) model and a R 2 of 95.3 % for the elongation-to-failure (EL) model. The correlation coefficient assisted screening (CCAS) method is proposed to filter low-alloyed target alloys. A new Mg-2.2Mn-0.4Zn-0.2Al-0.2Ca (MZAX2000, wt%) alloy is designed and extruded into bar at given processing parameters, achieving room-temperature strength-ductility synergy showing an excellent UTS of 395 MPa and a high EL of 17.9 %. This is closely related to its hetero-structured characteristic in the as-extruded MZAX2000 alloy consisting of coarse grains (16 %), fine grains (75 %), and fiber regions (9 %). Therefore, this work offers new insights into optimizing alloy compositions and processing parameters for attaining new high strong and ductile low-alloyed Mg alloys. Materials science ; Ductility (Earth science) ; Magnesium alloy ; Alloy ; Magnesium ; Metallurgy ; Creep Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Titanium Alloys Microstructure and Properties
Authors: Bokai Liu | Pengju Liu | Weizhuo Lu | Thomas Olofsson
Source: International journal of mechanical system dynamics (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Explainable Artificial Intelligence (XAI) for Material Design and Engineering Applications: A Quantitative Computational Framework ABSTRACT The advancement of artificial intelligence (AI) in material design and engineering has led to significant improvements in predictive modeling of material properties. However, the lack of interpretability in machine learning (ML)‐based material informatics presents a major barrier to its practical adoption. This study proposes a novel quantitative computational framework that integrates ML models with explainable artificial intelligence (XAI) techniques to enhance both predictive accuracy and interpretability in material property prediction. The framework systematically incorporates a structured pipeline, including data processing, feature selection, model training, performance evaluation, explainability analysis, and real‐world deployment. It is validated through a representative case study on the prediction of high‐performance concrete (HPC) compressive strength, utilizing a comparative analysis of ML models such as Random Forest, XGBoost, Support Vector Regression (SVR), and Deep Neural Networks (DNNs). The results demonstrate that XGBoost achieves the highest predictive performance (), while SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model‐Agnostic Explanations) provide detailed insights into feature importance and material interactions. Additionally, the deployment of the trained model as a cloud‐based Flask‐Gunicorn API enables real‐time inference, ensuring its scalability and accessibility for industrial and research applications. The proposed framework addresses key limitations of existing ML approaches by integrating advanced explainability techniques, systematically handling nonlinear feature interactions, and providing a scalable deployment strategy. This study contributes to the development of interpretable and deployable AI‐driven material informatics, bridging the gap between data‐driven predictions and fundamental material science principles. Computer science ; Artificial intelligence ; Management science ; Engineering Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Fault Detection and Control Systems
Authors: Laura Jimenez-Lopez | Rafael Morales-Ospino | Leandro Goulart de Araujo | Alain Celzard | Vanessa Fierro
Source: Nanoscale (review, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Latest developments in the synthesis of metal–organic frameworks and their hybrids for hydrogen storage Metal-organic frameworks (MOFs) are promising materials for hydrogen (H<sub>2</sub>) storage due to their versatile structures, high surface areas and substantial pore volumes. This paper provides a comprehensive review of MOF synthesis and characterization, as well as their practical applications for H<sub>2</sub> storage. We explore various MOF synthesis techniques, highlighting their impact on the nanopore structure and functionality. Special emphasis is placed on strategies for enhancing H<sub>2</sub> storage capacities by increasing specific surface areas, optimizing pore size distributions, and facilitating H<sub>2</sub> release by improving thermal conductivity. Key advances in MOF-based hybrids, such as MOFs combined with carbonaceous materials, metals or other inorganic materials, are discussed. This review also addresses the effectiveness of linker functionalization and the introduction of unsaturated metal centers to optimize H<sub>2</sub> storage under ambient conditions. We conclude that the development of competitive MOF-based hybrids, particularly those that incorporate carbons, offers significant potential for improving H<sub>2</sub> storage and recovery, enhancing thermal stability and increasing thermal conductivity. These advancements are in line with the US Department of Energy (DOE) specifications and pave the way for future research into the optimization of MOFs for practical H<sub>2</sub> storage applications. Hydrogen storage ; Metal-organic framework ; Materials science ; Hydrogen ; Metal ; Nanotechnology ; Process engineering ; Chemistry ; Metallurgy ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Carbon dioxide utilization in catalysis
Authors: Yu Tian | Jinli Liu | Jiali Qiao | Fuguo Ge | Yukun Yang | Qi Zhang
Source: Food Chemistry X (review, 2025-01-01)
Field: Chemistry — Electrochemistry

Advancements in Electrochemical Sensing Technology for Heavy Metal Ions Detection Most heavy metal ions are carcinogenic and non-biodegradable, posing threats to ecological balance and human health in trace amount. Therefore, there is a pressing demand for rapid and dependable detection technologies. Electrochemical sensing technology distinguishes itself with its ease of use and swiftness, rendering it perfect for the expeditious detection of heavy metal elements. This review examines various electrochemical detection techniques for on-site real-time monitoring of heavy metal ions. Advanced methods using innovative electrochemical sensor technologies are explored, highlighting the importance of sensing strategies for the quick and easy monitoring of metal levels in different environments. Additionally, the role of nanotechnology and electrochemical techniques in enhancing the sensitivity and selectivity of sensors for better detection of heavy metals is discussed. Finally, the future direction of sensor development is addressed, focusing on integrating new materials and technologies to improve the performance of sensor in environmental monitoring, food safety and public health. Electrochemistry ; Metal ions in aqueous solution ; Heavy metals ; Nanotechnology ; Ion ; Materials science ; Environmental science ; Metal ; Computer science ; Remote sensing ; Environmental chemistry ; Chemistry ; Metallurgy ; Geology ; Electrode Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors