jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Austin M. Mroz | Annabel R. Basford | Friedrich Hastedt | Inuka Jayasekera | Irea Mosquera‐Lois | Ruby Sedgwick | Pedro J. Ballester | Joshua D. Bocarsly | Ehecatl Antonio del Rio‐Chanona | Matthew L. Evans | Jarvist M. Frost | Alex M. Ganose | Rebecca L. Greenaway | King Kuok Hii | Yingzhen Li | Ruth Misener | Aron Walsh | Dandan Zhang | Kim E. Jelfs
Source: Chemical Society Reviews (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry From accelerating simulations and exploring chemical space, to experimental planning and integrating automation within experimental labs, artificial intelligence (AI) is changing the landscape of chemistry. We are seeing a significant increase in the number of publications leveraging these powerful data-driven insights and models to accelerate all aspects of chemical research. For example, how we represent molecules and materials to computer algorithms for predictive and generative models, as well as the physical mechanisms by which we perform experiments in the lab for automation. Here, we present ten diverse perspectives on the impact of AI coming from those with a range of backgrounds from experimental chemistry, computational chemistry, computer science, engineering and across different areas of chemistry, including drug discovery, catalysis, chemical automation, chemical physics, materials chemistry. The ten perspectives presented here cover a range of themes, including AI for computation, facilitating discovery, supporting experiments, and enabling technologies for transformation. We highlight and discuss imminent challenges and ways in which we are redefining problems to accelerate the impact of chemical research <i>via</i> AI. Transformative learning ; Cross disciplinary ; Discipline ; Face (sociological concept) ; Chemistry ; Engineering ethics ; Nanotechnology ; Cognitive science ; Data science ; Computer science ; Sociology ; Engineering ; Psychology ; Materials science ; Social science ; Pedagogy Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shengnan Yuan | Dehong Hu | Duyang Gao | Christopher J. Butch | Yiqing Wang | Hairong Zheng | Zonghai Sheng
Source: Journal of Nanobiotechnology (review, 2025-07-08)
Field: Materials Science — Biomaterials

Recent advances of engineering cell membranes for nanomedicine delivery across the blood–brain barrier The blood-brain barrier (BBB) poses a major challenge to the effective delivery of therapeutic agents for the treatment of central nervous system (CNS) disorders. The integration of cell membrane engineering with nanotechnology has recently enabled the development of membrane-engineered nanoparticles (CNPs). These nanocarriers exhibit enhanced BBB penetration and improved CNS targeting. This review systematically summarizes the latest advances in the development and application of CNPs, emphasizing how different cellular sources-such as erythrocytes, platelets, tumor cells, and leukocytes-impact delivery efficiency and therapeutic outcomes. We also examine the molecular mechanisms underlying nanoparticle-BBB interactions and highlight the importance of biosafety evaluations. Moreover, critical barriers to clinical translation, including large-scale manufacturing challenges, batch-to-batch variability, and regulatory complexities, are discussed. Finally, we explore emerging strategies-particularly the integration of artificial intelligence (AI)-that hold potential for overcoming existing clinical gaps, enabling the rational design and optimized development of CNP-based therapeutics for CNS disorders. By integrating mechanistic insights with translational perspectives, this review provides a clear conceptual and technological foundation for the development of next-generation CNS-targeted nanotherapeutics. Nanocarriers ; Nanomedicine ; Nanotechnology ; Drug delivery ; Blood–brain barrier ; Neuroscience ; Computer science ; Biology ; Materials science ; Central nervous system ; Nanoparticle Nanoparticle-Based Drug Delivery ; Extracellular vesicles in disease ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Tian Sun | Zhenxiang Wang | Liang Zeng | Guang Feng
Source: npj Computational Materials (article, 2025-04-03)
Field: Chemistry — Inorganic Chemistry

Identifying MOFs for electrochemical energy storage via density functional theory and machine learning Electrochemical energy storage (EES) systems demand electrode materials with high power density, energy density, and long cycle life. Metal-organic frameworks (MOFs) are promising electrode materials, while new MOFs with high conductivity, high stability, and abundant redox-reactive sites are demanded to meet the growing needs of EES. Density Functional Theory (DFT) could calculate these properties of MOFs and provide atomic-level insights into the mechanisms, based on which machine learning (ML) can screen MOFs for EES efficiently. In this review, we first review the exploration of mechanisms based on DFT calculations. We focus on the conductivity, stability, and reactivity of MOFs in EES systems. Then, we review the steps to apply ML in screening MOFs. Establishing datasets of MOFs, extracting features from MOF structure, and applying ML in screening MOFs are discussed. Finally, the review proposes the future avenue of DFT and ML to make up the gaps in the knowledge of MOFs. Density functional theory ; Electrochemical energy storage ; Energy storage ; Electrochemistry ; Computer science ; Materials science ; Supercapacitor ; Chemistry ; Physics ; Thermodynamics ; Computational chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Battery Materials ; Machine Learning in Materials Science
Authors: Haowen Yuan | Zi Wang | Di Lan | Siyuan Zhang | Zhou Zang | Guoqing Jiang | Hua-Chao Wei | Yiyi Zhang | Jiajia Zheng | Junwen Ren | Guanglei Wu | Shenli Jia
Source: Advanced Composites and Hybrid Materials (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Aramid nanofibers at ultralow loadings: driving significant multifunctionality in epoxy composite dielectrics Epoxy dielectrics with superior insulation, mechanical, and thermal performance are of great interest for electrical equipment and power electronics. However, integrating these excellent advantages into epoxy presents a formidable challenge. Herein, we detail a simple yet effective strategy for the concurrent enhancement of the dielectric breakdown strength, mechanical toughness, mechanical strength, and the glass transition temperature (Tg) of the epoxy dielectrics by incorporation of a minimal amount of aramid nanofibers (ANFs). It is revealed that a robust interfacial interaction is established between epoxy matrix and the high aspect ratio of ANFs as corroborated by both molecular dynamics simulations and dielectric relaxation spectroscopy. The strong interaction facilitates an optimized interface that enables efficient transfer of interfacial stress and energy dissipation, in turn conferring the ANFs/Epoxy with exceptional mechanical strength (up to 75.68 MPa) and toughness (195 MJ/m3) as well as high Tg (155 °C), respectively. Furthermore, the incorporation of ANFs introduces a multitude of deep traps which effectively impede the migration of charge carriers, contributing to a substantial improvement of the dielectric breakdown strength (196.8 kV/mm) of the ANFs/Epoxy composite, which is almost 4.1 times higher than that of epoxy. Aramid ; Epoxy ; Composite material ; Materials science ; Dielectric ; Composite number ; Nanofiber ; Fiber ; Optoelectronics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Shisheng Zheng | Ximing Zhang | Heng-Su Liu | Ge-Hao Liang | S.J. Zhang | Wentao Zhang | Bingxu Wang | Jingling Yang | Xian’an Jin | Feng Pan | Jian‐Feng Li
Source: Nature Communications (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

Active phase discovery in heterogeneous catalysis via topology-guided sampling and machine learning Understanding active phases across interfaces, interphases, and even within the bulk under varying external conditions and environmental species is critical for advancing heterogeneous catalysis. Describing these phases through computational models faces the challenges in the generation and calculation of a vast array of atomic configurations. Here, we present a framework for the automatic and efficient exploration of active phases. This approach utilizes a topology-based algorithm leveraging persistent homology to systematically sample configurations across diverse coordination environments and material morphologies. Simultaneously, efficient machine learning force fields enable rapid computations. We demonstrate the effectiveness of this framework in two systems: hydrogen absorption in Pd, where hydrogen penetrates subsurface layers and the bulk, inducing a "hex" reconstruction critical for CO<sub>2</sub> electroreduction, explored through 50,000 sampled configurations; and the oxidation dynamics of Pt clusters, where oxygen incorporation renders the clusters less active during oxygen reduction reactions, investigated through 100,000 sampled configurations. In both cases, the predicted active phases and their impacts on catalytic mechanisms closely align with previous experimental observations, indicating that the proposed strategy can model complex catalytic systems and discovery active phases under specific environmental conditions. Active site ; Computer science ; Catalysis ; Computation ; Topology (electrical circuits) ; Network topology ; Biological system ; Nanotechnology ; Materials science ; Chemistry ; Algorithm Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Surface Chemistry and Catalysis
Authors: Gokhan Onder Aksu | Seda Keskın
Source: ACS Materials Letters (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

The COF Space: Materials Features, Gas Adsorption, and Separation Performances Assessed by Machine Learning Covalent organic frameworks (COFs) are promising materials for gas adsorption; however, only a small number of COFs has been studied for a few types of gas separations to date. To unlock the full potential of the COF space, composed of 69 784 different types of materials, we studied the adsorption of five important gas molecules, CO<sub>2</sub>, CH<sub>4</sub>, H<sub>2</sub>, N<sub>2</sub>, and O<sub>2</sub> in COFs at various pressures combining high-throughput molecular simulations and machine learning. Adsorbent performances of COFs were then explored for industrially critical separations, such as CO<sub>2</sub>/CH<sub>4</sub>, CO<sub>2</sub>/H<sub>2</sub>, CO<sub>2</sub>/N<sub>2</sub>, CH<sub>4</sub>/H<sub>2</sub>, CH<sub>4</sub>/N<sub>2</sub>, and O<sub>2</sub>/N<sub>2</sub>. The key structural and chemical properties of the most promising adsorbents were revealed. Our work offers the most extensive dataset produced for COFs in the literature composed of ∼4.3 million data points for all synthesized and hypothetical COFs' structural, chemical, and energetic features; gas adsorption properties; and selectivities to facilitate the materials discovery. Adsorption ; Covalent bond ; Molecule ; Characterisation of pore space in soil ; Chemical space ; Materials science ; Gas separation ; Nanotechnology ; Chemistry ; Chemical engineering ; Organic chemistry ; Composite material ; Engineering ; Porosity ; Drug discovery Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Peng Peng | Chunyi Peng | Fuguo Liu | Shuai Long | Cheng Zhang | Ai Tao Tang | Jia She | Jianyue Zhang | Fusheng Pan
Source: Journal of Material Science and Technology (article, 2025-04-23)
Field: Materials Science — Biomaterials

Bayesian optimization and explainable machine learning for High-dimensional multi-objective optimization of biodegradable magnesium alloys Bayesian optimization ; Magnesium ; Bayesian probability ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Metallurgy Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Aluminum Alloy Microstructure Properties
Authors: Ziduo Yang | Xian Wang | Yifan Li | Qiujie Lv | Calvin Yu‐Chian Chen | Lei Shen
Source: npj Computational Materials (article, 2025-02-26)
Field: Materials Science — Materials Chemistry

Efficient equivariant model for machine learning interatomic potentials In modern computational materials, machine learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional molecular dynamics (MD) simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, efficient models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. Here, we introduce an efficient equivariant graph neural network (E2GNN) that can enable accurate and efficient interatomic potential and force predictions for molecules and crystals. Rather than relying on higher-order representations, E2GNN employs a scalar-vector dual representation to encode equivariant features. By learning geometric symmetry information, our model remains efficient while ensuring prediction accuracy and robustness through the equivariance. Our results show that E2GNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Furthermore, we conduct MD simulations using the E2GNN force field across solid, liquid, and gas systems. It is found that E2GNN can achieve the accuracy of ab initio MD across all examined systems. Equivariant map ; Computer science ; Artificial intelligence ; Statistical physics ; Physics ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nuclear Materials and Properties
Authors: Nesrin İlgin Beyazıt
Source: Processes (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Comparative Study of Hydrogen Storage and Metal Hydride Systems: Future Energy Storage Solutions Hydrogen is a key energy carrier, playing a vital role in sustainable energy systems. This review provides a comparative analysis of physical, chemical, and innovative hydrogen storage methods from technical, environmental, and economic perspectives. It has been identified that compressed and liquefied hydrogen are predominantly utilized in transportation applications, while chemical transport is mainly supported by liquid organic hydrogen carriers (LOHC) and ammonia-based systems. Although metal hydrides and nanomaterials offer high hydrogen storage capacities, they face limitations related to cost and thermal management. Furthermore, artificial intelligence (AI)- and machine learning (ML)-based optimization techniques are highlighted for their potential to enhance energy efficiency and improve system performance. In conclusion, for hydrogen storage systems to achieve broader applicability, it is recommended that integrated approaches be adopted—focusing on innovative material development, economic feasibility, and environmental sustainability. Hydrogen storage ; Hydride ; Energy storage ; Hydrogen ; Metal ; Environmental science ; Chemistry ; Materials science ; Process engineering ; Metallurgy ; Engineering ; Thermodynamics ; Physics ; Organic chemistry Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Hybrid Renewable Energy Systems
Authors: Shengpeng Wang | Clemens Altaner | Feng Lin | Panpan Liu | Zhiyu Song | Luqing Li | Anhui Gui | Xueping Wang | Jingming Ning | Pengcheng Zheng
Source: Food Research International (review, 2025-02-15)
Field: Chemistry — Analytical Chemistry

A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions Preprocessor ; Computer science ; Quality (philosophy) ; Control (management) ; Machine learning ; Algorithm ; Data pre-processing ; Artificial intelligence ; Chemometrics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Tea Polyphenols and Effects
Authors: Nargish Parvin | Sang Woo Joo | Jae Hak Jung | Tapas Kumar Mandal
Source: Chemosensors (article, 2025-06-21)
Field: Materials Science — Materials Chemistry

Unlocking the Future: Carbon Nanotubes as Pioneers in Sensing Technologies Carbon nanotubes (CNTs) have emerged as pivotal nanomaterials in sensing technologies owing to their unique structural, electrical, and mechanical properties. Their high aspect ratio, exceptional surface area, excellent electrical conductivity, and chemical tunability enable superior sensitivity and rapid response in various sensor platforms. This review presents a comprehensive overview of recent advancements in CNT-based sensors, encompassing both single-walled (SWCNTs) and multi-walled carbon nanotubes (MWCNTs). We discuss their functional roles in diverse sensing applications, including gas sensing, chemical detection, biosensing, and pressure/strain monitoring. Particular emphasis is placed on the mechanisms of sensing, such as changes in electrical conductivity, surface adsorption phenomena, molecular recognition, and piezoresistive effects. Furthermore, we explore strategies for enhancing sensitivity and selectivity through surface functionalization, hybrid material integration, and nanostructuring. The manuscript also covers the challenges of reproducibility, selectivity, and scalability that hinder commercial deployment. In addition, emerging directions such as flexible and wearable CNT-based sensors, and their role in real-time environmental, biomedical, and structural health monitoring systems, are critically analyzed. By outlining both current progress and existing limitations, this review underscores the transformative potential of CNTs in the design of next-generation sensing technologies across interdisciplinary domains. Carbon nanotube ; Nanotechnology ; Data science ; Computer science ; Materials science Carbon Nanotubes in Composites ; Nanotechnology research and applications ; Mechanical and Optical Resonators
Authors: Nicodemo Di Pasquale | Jesús Algaba | Pablo Montero de Hijes | Ignacio Sanchez‐Burgos | Andrés R. Tejedor | Stephen R. Yeandel | Felipe J. Blas | Ruslan L. Davidchack | Jorge R. Espinosa | Colin L. Freeman | John H. Harding | Brian B. Laird | Eduardo Sanz | Carlos Vega | Lorenzo Rovigatti
Source: Chemical Reviews (review, 2025-05-12)
Field: Materials Science — Materials Chemistry

Solid–Liquid Interfacial Free Energy from Computer Simulations: Challenges and Recent Advances The study of interfacial properties in liquid-liquid and liquid-vapor systems has a history of nearly 200 years, with significant contributions from scientific luminaries such as Thomas Young and Willard Gibbs. However, a similar level of understanding of solid-liquid interfaces has emerged only more recently, largely because of the numerous complications associated with the thermodynamics needed to describe them. The accurate calculation of the interfacial free energy of solid-liquid systems is central to determining which interfaces will be observed and their properties. However, designing and analyzing the molecular dynamics simulations required to do this remains challenging, unlike the liquid-liquid or liquid-vapor cases, because of the unique complications associated with solid-liquid systems. Specifically, the lattice structure of solids introduces spatial directionality, and atomic configurations in solids can be altered by stretching. The primary aim of this review is to provide an overview of the numerical approaches developed to address the challenge of calculating the interfacial free energy in solid-liquid systems. These approaches are classified as (i) direct methods, which compute interfacial free energies explicitly, albeit often through convoluted procedures, and (ii) indirect methods, which derive these free energies as secondary results obtained from the analysis of simulations of an idealized experimental configuration. We also discuss two key topics related to the calculation of the interfacial free energy of solid-liquid systems: nucleation theory and curved interfaces, which represent important problems where research remains highly active. Chemistry ; Nanotechnology ; Energy (signal processing) Pickering emulsions and particle stabilization ; Fluid Dynamics and Thin Films ; Surface Modification and Superhydrophobicity
Authors: Monika Kaczmarek | Aneta Białkowska
Source: Journal of Nanobiotechnology (review, 2025-02-04)
Field: Materials Science — Biomaterials

Enzymatic functionalization of bacterial nanocellulose: current approaches and future prospects Faced with the challenges of modern industry and medicine associated with the dynamic development of civilization, there is a constantly growing demand for the production of novel functional materials that are clearly oriented towards fulfilling specific applications. Herein, we provide an overview of the current status and recent findings related to the enzymatic functionalization of bacterial nanocellulose. Commonly, biocellulose modification involves the utilization of simple and cost-effective chemical and/or physical approaches. However, these methods may have an adverse effect on both the biological properties of the biomaterial and the natural environment. An alternative to these procedures is the highly specific enzymatic modification of bacterial nanocellulose, which perfectly fits into the assumptions of green technologies, making the process eco-friendly and not limiting any outlooks for further usage of the obtained biocomposites. The employment of enzymes for the targeted alteration of this material's properties is based on either a direct method, such as controlled hydrolysis and nanofication [i.e., synthesis of different morphological forms of bacterial cellulose (e.g., rod-shaped nanocrystals)] using cellulases, and/or attachment of reactive functional groups into the polymer structure via oxidation (e.g., utilizing a laccase/TEMPO catalytic system or lytic polysaccharide monooxygenases) and esterification catalyzed by lipases; or an indirect procedure involving the application of bacterial nanocellulose as a matrix for enzyme immobilization (e.g., laccase, glucose oxidase, horseradish peroxidase, lysozyme, bromelain, lipase, papain), thus creating a specific catalytic system. Overall, enzymatic functionalization of bacterial nanocellulose is a sustainable and promising strategy to create biocomposites with tailored properties for a wide range of industrial and medical applications. Nanocellulose ; Surface modification ; Current (fluid) ; Nanotechnology ; Chemistry ; Biochemistry ; Materials science ; Cellulose ; Engineering Advanced Cellulose Research Studies ; Enzyme Production and Characterization ; Biofuel production and bioconversion