jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fucheng Gao | Qiang Fu | Ying Ruan | Can Li | Yan‐Dong Wang | Hui Li | Jichao Li | Yanyan Jiang
Source: Advanced Science (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Elucidating Manganese Single‐Atom Doping: Strategies for Fluorescence Enhancement in Water‐Soluble Red‐Emitting Carbon Dots and Applications for FL/MR Dual Mode Imaging The absence of the enhancement of fluorescence in carbon dots (CDs) through doping with transition metal atoms (TMAs) hinders the advancement of multi-modal bio-imaging CDs with high photoluminescence quantum yield (PLQY). Herein, Mn-atomically-doped R-CDs (R-Mn-CDs) with a high PLQY of 41.3% in water is presented, enabling efficient in vivo dual-mode fluorescence/magnetic resonance (MR) imaging. The comprehensive characterizations reveal that the incorporation of Mn atoms leads to a Mn-N<sub>2</sub>O<sub>2</sub> coordinating structure, resulting in five significant effects: an increase in sp<sup>2</sup> conjugation domains, a reduction in band gap, a decreased oxidation level, an increase in photo-excited electron numbers, and the suppression of non-radiative electron relaxation pathways. Collectively, these factors contribute to the remarkable PLQY of R-Mn-CDs. Additionally, the doping of Mn atoms also endows R-Mn-CDs with superior MR imaging capabilities due to, which highlights their promising prospect as a dual-modal bio-imaging platform for fluorescence/MR imaging. Furthermore, the findings indicate that the introduction of various TMAs, such as Mn, Zn, Ni, and Cu, can universally improve the PLQY of water-soluble CDs through the construction of TMAs─O bonds. This research provides valuable theoretical insights into the mechanisms underlying the fluorescence enhancement induced by TMAs doping and offers guidance for the future design of high PLQY CDs. Photoluminescence ; Quantum yield ; Materials science ; Fluorescence ; Doping ; Photochemistry ; Excited state ; Atom (system on chip) ; Band gap ; Nanotechnology ; Chemistry ; Optoelectronics ; Optics ; Atomic physics ; Physics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Abdolrahim Zandi | Seyedali Hosseinirad | Hossein Kashani Zadeh | Kouhyar Tavakolian | Byoung–Kwan Cho | Fartash Vasefi | Moon S. Kim | Pantea Tavakolian
Source: Frontiers in Plant Science (review, 2025-04-30)
Field: Chemistry — Analytical Chemistry

A systematic review of multi-mode analytics for enhanced plant stress evaluation The advantages of MMA over conventional single-mode techniques are significant, particularly in the detection and management of plant stress in challenging environments. Integrating advanced analytical methods supports precision agriculture by enabling proactive responses to stress conditions. These innovations are pivotal for enhancing food security in terrestrial and space agriculture, ensuring sustainability and resilience in food production systems. Analytics ; Stress (linguistics) ; Computer science ; Data mining Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Leonardo Sabattini | Annalisa Coriolano | Corneel Casert | Stiven Forti | Edward S. Barnard | Fabio Beltram | Massimiliano Pontil | Stephen Whitelam | Camilla Coletti | Antonio Rossi
Source: Communications Physics (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Towards AI-driven autonomous growth of 2D materials based on a graphene case study The scalable synthesis of two-dimensional (2D) materials remains a key challenge for their integration into solid-state technology. While exfoliation techniques have driven much of the scientific progress, they are impractical for large-scale applications. Advances in artificial intelligence (AI) now offer new strategies for materials synthesis. This study explores the use of an artificial neural network (ANN) trained via evolutionary methods to optimize graphene growth. The ANN autonomously refines a time-dependent synthesis protocol without prior knowledge of effective recipes. The evaluation is based on Raman spectroscopy, where outcomes resembling monolayer graphene receive higher scores. This feedback mechanism enables iterative improvements in synthesis conditions, progressively enhancing sample quality. By integrating AI-driven optimization into material synthesis, this work contributes to the development of scalable approaches for 2D materials, demonstrating the potential of machine learning in guiding experimental processes. The authors propose an artificial neural network enabled to autonomously learn and refine time-dependent protocols for graphene growth, optimizing synthesis quality through iterative feedback based on Raman spectral analysis. This AI-driven methodology advances the development of large-scale and high-quality 2D-Materials. Graphene ; Nanotechnology ; Materials science ; Chemical engineering ; Computer science ; Engineering Graphene research and applications ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Jing Lin | Danfeng Zhao | San Lu | Rushuo Li | Xinmeng Xu | Zhaokun Wang | Wenqing Li | Yujing Ji | Chenjun Zhang | Lei Shi | Jin Xu | Hongyi Gao | Ge Wang
Source: ACS Nano (article, 2025-06-24)
Field: Chemistry — Inorganic Chemistry

Conversational Large-Language-Model Artificial Intelligence Agent for Accelerated Synthesis of Metal–Organic Frameworks Catalysts in Olefin Hydrogenation Metal-organic frameworks (MOFs) attract significant attention for their structural diversity and design flexibility, making them ideal candidates for catalytic applications. However, the traditional trial-and-error approach for optimizing MOF synthesis remains inefficient. In this study, we introduce the MOFsyn agent, an AI-driven framework that harnesses large language models (LLMs) for MOF synthesis optimization. This system integrates data automatic analysis, material mechanism analysis, and experimental protocol navigation by employing retrieval-augmented generation (RAG) to refine synthetic strategies based on natural language inputs. Using Ni@UiO-66(Ce) for olefin hydrogenation as a case study, the MOFsyn agent analyzed the relationship between synthesis conditions, structural characteristics, and catalytic performance, with a particular focus on the electronic structure of nickel. Through adaptive optimization, a novel stepwise reduction strategy was proposed that outperformed conventional one-pot reduction. The optimized Ni@UiO-66(Ce)-R2T1, synthesized under MOFsyn agent's guidance, exhibited nearly twice the Ni<sup>0</sup>/Ni<sup>total</sup> ratio compared to the best-performing sample from an initial experimental set and achieved 100% conversion and selectivity for dicyclopentadiene hydrogenation under mild conditions (70 °C, 2 MPa). These results validate the accuracy and efficiency of the MOFsyn agent. This study provides an efficient tool for intelligent material synthesis, enabling researchers without programming expertise to accelerate material development. Computer science ; Flexibility (engineering) ; Olefin fiber ; Catalysis ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Aparna Ashok | Jitendra Pal Singh | Anuj Kumar | Neeru Bhagat
Source: Materials Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Bridging current and future innovations to unlock the potential of multifunctional materials for sustainable energy applications Recent advances in multifunctional energy materials, driven by machine learning and bio-inspired design, are transforming photovoltaics, energy storage, and thermoelectrics, accelerating progress toward sustainable energy solutions. Bridging (networking) ; Nanotechnology ; Sustainable energy ; Current (fluid) ; Engineering physics ; Materials science ; Biochemical engineering ; Computer science ; Engineering ; Renewable energy ; Electrical engineering MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Arda Genç | Justin Marlowe | Aishah Abdul Jalil | Daniel Belzberg | Libor Kovařík | Phillip Christopher
Source: Ultramicroscopy (article, 2025-02-16)
Field: Materials Science — Surfaces, Coatings and Films

A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles Workflow ; Throughput ; Catalysis ; Computer science ; Nanotechnology ; Chemical engineering ; Materials science ; Chemistry ; Operating system ; Engineering ; Database ; Organic chemistry Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Talha Bin Nadeem | Muhammad Imran | Emad Tandis
Source: Nanomaterials (review, 2025-01-27)
Field: Chemistry — Spectroscopy

Applications of MOF-Based Nanocomposites in Heat Exchangers: Innovations, Challenges, and Future Directions Metal-organic frameworks (MOFs) have garnered significant attention in recent years for their potential to revolutionize heat exchanger performance, thanks to their high surface area, tunable porosity, and exceptional adsorption capabilities. This review focuses on the integration of MOFs into heat exchangers to enhance heat transfer efficiency, improve moisture management, and reduce energy consumption in Heating, Ventilation and Air Conditioning (HVAC) and related systems. Recent studies demonstrate that MOF-based coatings can outperform traditional materials like silica gel, achieving superior water adsorption and desorption rates, which is crucial for applications in air conditioning and dehumidification. Innovations in synthesis techniques, such as microwave-assisted and surface functionalization methods, have enabled more cost-effective and scalable production of MOFs, while also enhancing their thermal stability and mechanical strength. However, challenges related to the high costs of MOF synthesis, stability under industrial conditions, and large-scale integration remain significant barriers. Future developments in hybrid nanocomposites and collaborative efforts between academia and industry will be key to advancing the practical adoption of MOFs in heat exchanger technologies. This review aims to provide a comprehensive understanding of current advancements, challenges, and opportunities, with the goal of guiding future research toward more sustainable and efficient thermal management solutions. HVAC ; Process engineering ; Heat exchanger ; Materials science ; Nanocomposite ; Nanotechnology ; Air conditioning ; Mechanical engineering ; Engineering Aerogels and thermal insulation ; Thermal properties of materials ; Magnesium Oxide Properties and Applications
Authors: Bin Wang | Gongchao Chen | Juan Wen | Linfang Li | Songlin Jin | Yan Li | Ling Zhou | Weidong Zhang
Source: Frontiers in Plant Science (article, 2025-01-16)
Field: Chemistry — Analytical Chemistry

SSATNet: Spectral-spatial attention transformer for hyperspectral corn image classification Hyperspectral images are rich in spectral and spatial information, providing a detailed and comprehensive description of objects, which makes hyperspectral image analysis technology essential in intelligent agriculture. With various corn seed varieties exhibiting significant internal structural differences, accurate classification is crucial for planting, monitoring, and consumption. However, due to the large volume and complex features of hyperspectral corn image data, existing methods often fall short in feature extraction and utilization, leading to low classification accuracy. To address these issues, this paper proposes a spectral-spatial attention transformer network (SSATNet) for hyperspectral corn image classification. Specifically, SSATNet utilizes 3D and 2D convolutions to effectively extract local spatial, spectral, and textural features from the data while incorporating spectral and spatial morphological structures to understand the internal structure of the data better. Additionally, a transformer encoder with cross-attention extracts and refines feature information from a global perspective. Finally, a classifier generates the prediction results. Compared to existing state-of-the-art classification methods, our model performs better on the hyperspectral corn image dataset, demonstrating its effectiveness. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Classifier (UML) ; Feature extraction ; Precision agriculture ; Spatial analysis ; Remote sensing ; Computer vision ; Geography ; Agriculture Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Smart Agriculture and AI
Authors: Gbolagade Olajide | Khagendra Baral | Sophia Ezendu | Ademola Soyemi | Tibor Szilvási
Source: Journal of Catalysis (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Application of machine learning interatomic potentials in heterogeneous catalysis Chemistry ; Catalysis ; Heterogeneous catalysis ; Computational chemistry ; Chemical physics ; Nanotechnology ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Fanyong Yan | Ruixue Bai | Juanru Huang | Xihui Bian | Yang Fu
Source: TrAC Trends in Analytical Chemistry (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Machine learning-assisted carbon dots synthesis and analysis: State of the art and future directions State (computer science) ; Nanotechnology ; Computer science ; Artificial intelligence ; Materials science ; Programming language Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Digambar S. Sawant | S. B. Kulkarni | Deepak P. Dubal | G. M. Lohar
Source: Battery energy (article, 2025-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transition Metal Molybdates Emerging Materials for High‐Performance Supercapacitors: A Machine Learning Analysis ABSTRACT Transition metal molybdates (AMoO 4 where A = Ni, Co, Mn, Fe, and Zn) have attracted much attention as promising electrode materials for energy storage devices due to their multi‐electron redox capability, higher electrical conductivity, good chemical and thermal stability, and stable crystal structure to get superior electrochemical performance. Transition metal molybdates and their graphene‐based composites possess multidimensional morphology for supercapacitors. The morphology‐dependent supercapacitor behavior has been reviewed in the present article. The formation mechanism of AMoO 4 nanostructures in the form of 1D, 2D, and 3D has been identified and respective supercapacitor behavior is outlined. The density functional theory based on the calculated electronic properties of AMoO 4 has been discussed. Additionally, the application of machine learning techniques in predicting and analyzing the relationships of AMoO 4 has been discussed for the first time. By leveraging ML algorithms, we identify key parameters influencing their energy storage capabilities, providing insights into the rational design of molybdate‐based composites. Integrating experimental results with ML‐driven optimization offers a novel pathway for accelerating the development of next‐generation energy storage devices. In conclusion, future perspectives and challenges have been discussed. Supercapacitor ; Transition metal ; Materials science ; Transition (genetics) ; Metallurgy ; Chemistry ; Capacitance ; Physical chemistry Supercapacitor Materials and Fabrication ; Polyoxometalates: Synthesis and Applications ; Advancements in Battery Materials
Authors: Mahish Guru | Jan Bohlen | Roland C. Aydin | Noomane Ben Khalifa
Source: Acta Materialia (article, 2025-05-28)
Field: Materials Science — Biomaterials

Machine learning pipeline for Structure–Property modeling in Mg-alloys using microstructure and texture descriptors Identifying the relationships between material structure and mechanical properties has been crucial for accelerating the exploration of the material design space for advanced alloys. However, traditional approaches for magnesium (Mg) alloys often fall short in providing quantitative and broadly applicable structure–property linkages. To address this challenge, a comprehensive machine learning pipeline is presented for structure–property modeling in extruded Mg-alloys, leveraging both microstructure and texture descriptors derived from experimental data. The pipeline encompasses a robust workflow for data extraction from optical microscopy and X-ray diffraction, advanced image processing and deep learning techniques for microstructure binarization and grain statistics, and the computation of statistical descriptors including n-point spatial correlations, gram matrices for microstructure, and generalized spherical harmonics (GSH) for texture. Dimensionality reduction techniques such as principal component analysis (PCA), isomap, and autoencoders are employed to manage the high-dimensionality of the descriptor space. Subsequently, non-linear regression models—Gaussian Process, XGBoost, and Multi-Layer Perceptron regressors—are evaluated to predict mechanical properties, specifically strain hardening exponent (n) and yield stress (σ<sub>y</sub>). Our results demonstrate that XGBoost consistently outperforms other regressors, achieving a notably low mean absolute percentage error (MAPE) of 6.67% for strain hardening exponent and 7.01% for yield stress, using a combination of PCA-reduced 3-point spatial correlations and isomap-reduced gram matrices as microstructure descriptors, and isomap-reduced GSH coefficients as texture descriptors at a 150μm length scale. Shapley Additive exPlanations (SHAP) analysis further reveals that texture descriptors and aspect ratio distribution are the most influential features in predicting mechanical properties. This established ML framework for structure–property modeling in Mg-alloys, surpasses state-of-the-art benchmarks and provides a valuable template for materials design and discovery. Materials science ; Microstructure ; Pipeline (software) ; Texture (cosmology) ; Property (philosophy) ; Metallurgy ; Artificial intelligence ; Mechanical engineering ; Computer science ; Engineering ; Image (mathematics) Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Aluminum Alloy Microstructure Properties
Authors: Yu Shu | Naihua Miao | R. J. Li | Yucheng Lin | Siyu Han | Jian Zhou | Zhimei Sun
Source: Journal of Materials Informatics (review, 2025-05-28)
Field: Materials Science — Materials Chemistry

Machine learning-enabled optoelectronic material discovery: a comprehensive review The development of advanced optoelectronic materials constitutes a pivotal frontier in modern energy and communication technologies, facilitating critical energy-photon-electron interconversion processes that underpin sustainable energy infrastructures and high-performance electronic devices. However, the discovery and optimization of novel optoelectronic materials face substantial hurdles arising from complicated structure-property interdependencies, prohibitive development costs, and protracted innovation cycles. Conventional empirical approaches and computational simulations usually exhibit limited efficacy in addressing the escalating demands for materials with superior stability, economic viability, and customizable electronic properties. The integration of machine learning (ML) with high-throughput screening has emerged as a transformative strategy to address these challenges. By rapidly processing large multidimensional datasets and predicting critical material properties such as electronic structure, thermodynamic stability, and charge transport behaviors, ML offers unprecedented capabilities in the efficient and rational design of high-performance optoelectronic materials. This review provides a comprehensive overview of cutting-edge ML-driven methodologies in efficient optoelectronic materials discovery with emphasis on critical workflows, data integration strategies, and model frameworks. We also discuss the challenges and prospects for ML applications, particularly in data standardization, model interpretability and closed-loop experimental validation. We further propose the potential of artificial intelligence and autonomous laboratories to build a powerful discovery pipeline to advance the development of high-performance optoelectronic materials. Materials science ; Computer science ; Optoelectronics ; Nanotechnology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chalcogenide Semiconductor Thin Films
Authors: M Abishek | Sabindra Kachhap
Source: Fuel (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

Sustainable synthesis of copper oxide nanoparticles using Spondias mombin and biodiesel production from Guizotia abyssinica: Engine performance, emission characteristics, and machine learning-based optimization Biodiesel ; Copper ; Copper oxide ; Nanoparticle ; Biodiesel production ; Materials science ; Chemistry ; Organic chemistry ; Nanotechnology Copper-based nanomaterials and applications ; Moringa oleifera research and applications
Authors: Samuel P. Niblett | Panagiotis Kourtis | Ioan-Bogdan Magdău | Clare P. Grey | Gábor Cśanyi
Source: Journal of Chemical Theory and Computation (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Transferability of Data Sets between Machine-Learned Interatomic Potential Algorithms The emergence of Foundational Machine Learning Interatomic Potential (FMLIP) models trained on extensive data sets motivates attempts to transfer data between different ML architectures. Using a common battery electrolyte solvent as a test case, we examine the extent to which training data optimized for one machine-learning method may be reused by a different learning algorithm, aiming to accelerate FMLIP fine-tuning and to reduce the need for costly iterative training. We consider several types of training configurations and compare the benefits they bring to feedforward neural networks (the Deep Potential model) and message-passing networks (MACE). We propose a simple metric to assess model performance and demonstrate that MACE models perform well with even the simplest training sets, whereas simpler architectures require further iterative training to describe the target liquids correctly. We find that configurations designed by human intuition to correct systematic deficiencies of a model often transfer well between algorithms, but that reusing configurations that were generated automatically by one MLIP does not necessarily benefit a different algorithm. We also compare the performance of these bespoke models against two pretrained FMLIPs, demonstrating that system-specific training data are usually necessary for realistic models. Finally, we examine how training data sets affect a model's ability to generalize to unseen molecules, finding that model stability is conserved for small changes in molecule shape but not changes in functional chemistry. Our results provide insight into how training set properties affect the behavior of an MLIP and principles to enhance training sets for molecular liquid models with minimal computational effort. These approaches may be used in tandem with FMLIPs to dramatically accelerate the rate at which new chemical systems can be simulated. Transferability ; Computer science ; Interatomic potential ; Algorithm ; Data mining ; Machine learning ; Molecular dynamics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Catalysis and Oxidation Reactions