jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunhui Zeng | Hongkun Cao | Xin Jin
Source: Nanophotonics (article, 2025-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Anchor‐controlled generative adversarial network for high‐fidelity electromagnetic and structurally diverse metasurface design Metasurfaces, capable of manipulating light at subwavelength scales, hold great potential for advancing optoelectronic applications. Generative models, particularly Generative Adversarial Networks (GANs), offer a promising approach for metasurface inverse design by efficiently navigating complex design spaces and capturing underlying data patterns. However, existing generative models struggle to achieve high electromagnetic fidelity and structural diversity. These challenges arise from the lack of explicit electromagnetic constraints during training, which hinders accurate structure-to-electromagnetic mapping, and the absence of mechanisms to handle one-to-many mappings dilemma, resulting in insufficient structural diversity. To address these issues, we propose the Anchor-controlled Generative Adversarial Network (AcGAN), a novel framework that improves both electromagnetic fidelity and structural diversity. To achieve high electromagnetic fidelity, AcGAN proposes the Spectral Overlap Coefficient (SOC) for precise spectral fidelity assessment and develops AnchorNet, which provides real-time physics-guided feedback on electromagnetic performance to refine the structure-to-electromagnetic mapping. To enhance structural diversity, AcGAN incorporates a cluster-guided controller that refines input processing and ensures multilevel spectral integration, guiding the generation process to explore multiple configurations. Empirical analysis shows that AcGAN reduces the Mean Squared Error (MSE) by 73 % compared to current state-of-the-art and significantly expands the design space to generate diverse metasurface architectures that meet precise spectral demands. Generative grammar ; Adversarial system ; High fidelity ; Fidelity ; Nanomaterials ; Computer science ; Generative adversarial network ; Nanotechnology ; Engineering ; Materials science ; Artificial intelligence ; Telecommunications ; Deep learning ; Electrical engineering Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Antenna Design and Optimization
Authors: Tianyi Gao | Honghao Huang | Yang Liu
Source: Advanced Materials (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Machine Learning‐Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data‐Driven Methodologies to Closed‐Loop Optimization The rational design of functional nanomaterials is fundamentally challenged by complex synthesis-structure-performance relationships and vast design spaces that defy conventional trial-and-error methods. In particular, nanomaterials have become integral to electrocatalysis, where their tunable surface structures and quantum-scale effects govern catalytic activity and selectivity. Nevertheless, translating their intrinsic physicochemical advantages into catalytic performance remains difficult, as it requires precise control over synthetic parameters to access desired surface structures and active sites. Machine learning (ML) has emerged as a transformative framework, integrating predictive modeling, data-driven synthesis optimization, and autonomous experimentation to accelerate the discovery of high-performance nanocatalysts. This review outlines how ML provides a unified foundation for nanomaterials research by integrating data curation, algorithmic development, and application-specific modeling. It also enables controllable synthesis through reaction condition optimization, multimodal descriptor learning, and autonomous experimentation, while linking structural complexity to catalytic function via interpretable learning frameworks. Building on these capabilities, ML is redefining materials innovation through physics-informed generative models, autonomous platforms, and multiscale interpretability. These advances collectively support closed-loop, end-to-end strategies for nanocatalyst design by integrating precision synthesis, model-guided optimization, and multimodal characterization. Together, they lay the foundation for a new paradigm in the discovery of intelligent nanomaterials. Rational design ; Nanomaterials ; Transformative learning ; Function (biology) ; Generative grammar ; Nanoscopic scale Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Shi Xuan Leong | Sergio Pablo‐García | Brandon Wong | Alán Aspuru‐Guzik
Source: Matter (article, 2025-07-25)
Field: Materials Science — Materials Chemistry

MERMaid: Universal multimodal mining of chemical reactions from PDFs using vision-language models Data digitization of scientific literature is essential for creating machine-actionable knowledge bases to advance data-driven research and integrate with self-driving laboratories. It is especially critical to extract, interpret, and structure data from graphical elements, the primary medium for conveying complex scientific insights. However, this remains challenging due to the inherent lack of semantic structure in the prevalent PDF format, the complexity of visual content, and the need for multimodal integration. We present MERMaid (multimodal aid for reaction mining), an end-to-end pipeline that converts disparate visual data across PDFs into a coherent knowledge graph. Leveraging the emergent visual cognition and reasoning capabilities of vision-language models, MERMaid demonstrates chemical context awareness, self-directed context completion, and robust coreference resolution to achieve 87% end-to-end accuracy across three chemical domains. Its modular design facilitates future application to diverse data beyond reaction mining, promising to unlock the full potential of scientific literature for knowledge-intensive applications. Computer science ; Artificial intelligence ; Natural language processing Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Stef A. H. Jansen | Albert J. Markvoort | Freek V. de Graaf | Martin G. T. A. Rutten | Patricia Y. W. Dankers | Ghislaine Vantomme | Tom F. A. de Greef | E. W. Meijer
Source: Journal of the American Chemical Society (article, 2025-09-04)
Field: Materials Science — Biomaterials

Bayesian Optimization for Multicomponent Supramolecular Systems The diversity of noncovalent interactions makes the design space of multicomponent molecular systems highly complex. To efficiently explore supramolecular design space, data-driven strategies are needed. Here, we demonstrate a methodological framework for the targeted design of multicomponent molecular systems with noncovalent interactions using Bayesian optimization. Its effective applicability to supramolecular polymers is illustrated by three representative cases that reveal accelerated exploration of diverse multicomponent systems with a universal Bayesian optimization framework. The number of experiments required to arrive at optimal compositions is significantly reduced compared to random or uninformed sampling strategies, enabling the experimental study of high-dimensional design spaces. In this way, we can tune the formulation of intricate mixtures and achieve tailored macroscopic properties with minimal experimental effort. Our results show that Bayesian optimization is a general tool for developing multicomponent supramolecular systems with designed functionality. Chemistry ; Supramolecular chemistry ; Bayesian probability ; Bayesian optimization ; Combinatorial chemistry ; Nanotechnology ; Artificial intelligence ; Organic chemistry ; Molecule ; Computer science Supramolecular Self-Assembly in Materials ; Machine Learning in Materials Science ; Polydiacetylene-based materials and applications
Authors: Sambarta Chatterjee | William M. Jacobs
Source: Physical Review X (article, 2025-03-31)
Field: Materials Science — Biomaterials

Multiobjective Optimization for Targeted Self-Assembly among Competing Polymorphs Most approaches for designing self-assembled materials focus on the thermodynamic stability of a target structure or crystal polymorph. Yet in practice, the outcome of a self-assembly process is often controlled by kinetic pathways. Here we present an efficient machine-learning-guided design algorithm to identify globally optimal interaction potentials that maximize both the thermodynamic yield and kinetic accessibility of a target polymorph. We show that optimal potentials exist along a Pareto front, indicating the possibility of a trade-off between the thermodynamic and kinetic objectives. Although the extent of this trade-off depends on the target polymorph and the assembly conditions, we generically find that the trade-off arises from a competition among alternative polymorphs: The most kinetically optimal potentials, which favor the target polymorph on short timescales, tend to stabilize a competing polymorph at longer times. Our work establishes a general-purpose approach for multiobjective self-assembly optimization, reveals fundamental trade-offs between crystallization speed and defect formation in the presence of competing polymorphs, and suggests guiding principles for materials design algorithms that optimize for kinetic accessibility. Computer science ; Multi-objective optimization ; Nanotechnology ; Statistical physics ; Mathematical optimization ; Physics ; Materials science ; Mathematics Supramolecular Self-Assembly in Materials ; Modular Robots and Swarm Intelligence ; Pickering emulsions and particle stabilization
Authors: Qing Ma | Yuhao Zhou | Jianfeng Li
Source: Macromolecular Rapid Communications (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Automated Retrosynthesis Planning of Macromolecules Using Large Language Models and Knowledge Graphs Identifying reliable synthesis pathways in materials chemistry is a complex task, particularly in polymer science, due to the intricate and often nonunique nomenclature of macromolecules. To address this challenge, an agent system that integrates large language models (LLMs) and knowledge graphs is proposed. By leveraging LLMs' powerful capabilities for extracting and recognizing chemical substance names, and storing the extracted data in a structured knowledge graph, the system fully automates the retrieval of relevant literature, extraction of reaction data, database querying, construction of retrosynthetic pathway trees, further expansion through the retrieval of additional literature and recommendation of optimal reaction pathways. By considering the complex interdependencies among chemical reactants, a novel Multi-branched Reaction Pathway Search Algorithm (MBRPS) is proposed to help identify all valid multi-branched reaction pathways, which arise when a single product decomposes into multiple reaction intermediates. In contrast, previous studies are limited to cases where a product decomposes into at most one reaction intermediate. This work represents the first attempt to develop a fully automated retrosynthesis planning agent tailored specially for macromolecules powered by LLMs. Applied to polyimide synthesis, the new approach constructs a retrosynthetic pathway tree with hundreds of pathways and recommends optimized routes, including both known and novel pathways. Retrosynthetic analysis ; Computer science ; Macromolecule ; Artificial intelligence ; Natural language processing ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Graph Theory and Algorithms
Authors: Shusuke Ito | Koki Muraoka | Akira Nakayama
Source: Chemistry of Materials (article, 2025-03-24)
Field: Chemistry — Inorganic Chemistry

Knowledge-Informed Molecular Design for Zeolite Synthesis Using General-Purpose Pretrained Large Language Models Toward Human-Machine Collaboration The design of organic molecules lies at the heart of solving numerous chemistry-related challenges, necessitating effective collaboration between human intuition and computational power. This study demonstrates how general-purpose Large Language Models (LLMs) can facilitate the design of molecules, leveraging feedback from empirical knowledge through natural language. We used this approach to design organic structure-directing agents (OSDAs) that guide the crystallization of zeolites. In our computational workflow, LLM proposes OSDA candidates that are evaluated by empirical knowledge and atomistic simulation. Feedback is then provided to the LLM in natural language to refine subsequent proposals, thus progressively enhancing the proposed OSDAs and promoting the exploration of the chemical space. The predicted candidates encompassed experimentally validated OSDAs, structurally analogous ones, and novel ones with superior affinity scores, underscoring the robust capability of the LLM. The human-machine collaboration, utilizing natural language as the communication interface, holds potential for application in other molecular design tasks. Zeolite ; Computer science ; Human–computer interaction ; Artificial intelligence ; Knowledge management ; Natural language processing ; Nanotechnology ; Chemistry ; Materials science ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Humaira Aslam | Ali Umar | Misbah Khan | Tapankumar Trivedi | Ezhilarasan Ganesan | Deepak Bhanot | Numan Abbas | Khaled Fahmi Fawy
Source: Reviews in Inorganic Chemistry (article, 2025-07-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent trends in supercapacitor technology; basics, history, fabrications, classifications and their application in energy storage materials Abstract Supercapacitors (SCs), also known as ultracapacitors or electrochemical capacitors, have attracted significant attention as promising energy storage devices due to their superior power density, rapid charge-discharge capability, and long cycle life. This review comprehensively discusses the recent advancements in supercapacitor technology, focusing on the development of novel electrode materials, electrolytes, device designs, and fabrication methods. Particular emphasis is placed on carbon-based materials, metal oxides, conducting polymers, and their hybrid composites, which have shown remarkable improvements in specific capacitance and stability. Additionally, the role of advanced electrolytes, including ionic liquids and gel polymer electrolytes, in enhancing the energy and power density of SCs is explored. The integration of hybrid supercapacitor systems, combining EDLCs and pseudocapacitors, is also highlighted for their potential to overcome the limitations of conventional capacitors and batteries. This review aims to provide valuable insights into the current progress, emerging trends, and future directions for improving the performance and practical applicability of supercapacitors in real-world energy storage applications. Supercapacitor ; Chemistry ; Energy storage ; Nanotechnology ; Engineering physics ; Electrochemistry ; Engineering ; Materials science ; Physical chemistry ; Electrode ; Thermodynamics ; Physics Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Hydrogen Storage and Materials
Authors: Yaping Qi | Weijie Yang
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

From Data to Discovery: How AI-Driven Materials Databases Are Reshaping Research AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization. Platforms such as Digital Catalysis Platform (DigCat) and Dynamic Database of S... | Find, read and cite all the research you need on Tech Science Press Database ; Computer science ; Data science ; World Wide Web ; Information retrieval Machine Learning in Materials Science
Authors: Yubo Fan | C. N. H. Long | Yingying Cai | Yingkun Hu | Li‐Hua Peng
Source: Frontiers in Bioengineering and Biotechnology (review, 2025-08-29)
Field: Chemistry — Inorganic Chemistry

Functionalized metal-organic framework and MOF-derived materials for bone regeneration applications Bone defects resulting from trauma, tumors, infections, and aging present significant clinical challenges, with conventional grafts hindered by limitations in biocompatibility, mechanical strength, and integration. Metal-organic frameworks (MOFs), as advanced nanomaterials with tunable porosity, high surface area, and stimuli-responsive properties, hold immense potential for bone regeneration. This review provides a comprehensive overview of the classification, synthesis methods, osteogenic mechanisms, and applications of functionalized MOFs and their derivatives in bone repair. MOFs are classified based on structural topology, chemical composition, and functional applications. Synthesis techniques, including solvothermal, ultrasonic, and electrochemical approaches, are evaluated for customizing physical properties such as pore architecture and stability. Osteogenic mechanisms encompass enhancing implant physical characteristics to promote cell adhesion, sustained release of metal ions to activate signaling pathways, controlled drug delivery for targeted therapy, and anti-inflammatory/antioxidant effects through reactive oxygen species scavenging. Applications address various bone pathologies, demonstrating improved angiogenesis, osteointegration, and antibacterial performance in preclinical studies. Key challenges, including cytotoxicity, long-term biosafety, and scalability, are discussed, alongside strategies like surface modification and hybrid composites to overcome these barriers. Future perspectives focus on developing smart MOF-based scaffolds for personalized regenerative medicine, underscoring their transformative potential in orthopedic therapies. Nanotechnology ; Materials science ; Biocompatibility ; Metal-organic framework ; Osseointegration ; Surface modification ; Regeneration (biology) ; Drug delivery ; Scaffold ; Regenerative medicine ; Biomedical engineering ; Chemistry ; Adsorption ; Medicine ; Implant ; Cell Metal-Organic Frameworks: Synthesis and Applications ; Bone Tissue Engineering Materials ; MXene and MAX Phase Materials
Authors: Howida A. Fetouh | Engy E. A. Aleem | N. H. Mohammed | Jihad M. Abd-Almajeed Aldesouky | Amel M. Ismail
Source: Scientific Reports (article, 2025-01-31)
Field: Materials Science — Biomaterials

Formulation of new drug delivery systems for insulin from natural bioactive biocompatible polymers New insulin drug delivery systems (IDDs): insulin@chitin; insulin@chitin-grafted (g)-guar gum were prepared by using a modified sol-gel method. Insulin vials were loaded on the safe natural inert bioactive polymers (chitin and chitin-g-GG copolymer) carriers using water green solvent. Traces amount additives were below toxicity limits. Guar gum increased the numbers of the functional groups of the polymer carrier. Insulin release monitored at 37 ± 0.5 °C and buffer solutions of pH (1.2, 6.8 and 7.4) simulating physiological body fluids: stomach, intestine colon and blood stream. Insulin released from insulin@chitin only at pH 7.4. No release observed at pH 1.2, 6.8 due strong bonding to acetyl group of chitin. Insulin@chitin-GG system showed sustained targeting insulin-release at pH: 6.8 > 7.4 > 1.2. Release data obeyed pseudo second order kinetic model indicating that IDDs is heterogeneous solid surface of energetically different active sites. Each insulin molecule occupied two active sites. The slow release at pH 1.2 indicated protection against stomach juice. Release kinetic depend on physicochemical characteristics (porosity, swelling ratio as well as peptide and amino acid sequence). Both IDDs showed negative zeta potential indicating stability against aggregation. Gaur gum improved particle size distribution and insulin release. Biocompatible material ; Natural polymers ; Drug delivery ; Insulin delivery ; Drug ; Insulin ; Chemistry ; Polymer ; Pharmacology ; Nanotechnology ; Medicine ; Materials science ; Biomedical engineering ; Diabetes mellitus ; Internal medicine ; Endocrinology ; Organic chemistry ; Type 1 diabetes biodegradable polymer synthesis and properties ; Polysaccharides Composition and Applications ; Enzyme Catalysis and Immobilization
Authors: Julia Westermayr | Philipp Marquetand
Source: Chemical Science (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning spectroscopy to advance computation and analysis Spectroscopy, the exploration of matter through its interaction with electromagnetic radiation, is relevant in many diverse research fields, such as biology, materials science, medicine, and chemistry, and enables the qualitative and quantitative characterization of samples. Machine learning has revolutionized spectroscopy by enabling computationally efficient predictions of electronic properties, expanding libraries of synthetic data, and facilitating high-throughput screening. While machine learning has strengthened theoretical computational spectroscopy, its potential in processing experimental data has yet to be adequately explored. At the same time, automating structure and composition predictions from spectra remains a formidable challenge that requires theoretical simulations and expert knowledge. This review addresses the synergy between machine learning and spectroscopy, covering various techniques including optical, X-ray, nuclear magnetic resonance, and mass spectrometry. It outlines the fundamentals of machine learning, summarizes the techniques, and previews future developments to fully exploit the potential of machine learning and advance the field. Exploit ; Computation ; Artificial neural network ; Experimental data ; Computational learning theory ; Expert system Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Electron Microscopy Techniques and Applications
Authors: Yiming Ma | Wei Li | Jiaxu Liu | Shang Gao | Huaiyu Yang | Junbo Gong | Zoltán K. Nagy | Brahim Benyahia
Source: AIChE Journal (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Digital design and optimization of the integrated synthesis and crystallization process using data‐driven approaches Abstract This study presents a data‐driven modeling and multi‐objective optimization framework for an integrated section of continuous pharmaceutical manufacturing, focusing on flow synthesis and continuous crystallization. To address data scarcity and trade‐offs among product quality, efficiency, and environmental impact, the framework combines generative adversarial networks (GANs), artificial neural networks (ANNs), and genetic algorithms (GAs). An integrated dual‐GAN (ID‐GAN) generates data under physicochemical constraints, which are merged with real data to train an ANN with 15%–20% mean absolute errors for particle size, productivity, and a sustainability throughput index. The ANN is then coupled with a GA to identify Pareto‐optimal solutions based on user‐defined objectives and constraints. Case studies validate the framework's capability to facilitate process design decisions by systematically exploring trade‐offs among competing objectives, underscoring its potential utility in the digitalization of critical units within continuous manufacturing systems. Crystallization ; Process (computing) ; Process engineering ; Computer science ; Process optimization ; Biochemical engineering ; Chemical engineering ; Engineering ; Programming language Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Process Optimization and Integration