jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cristiano Malica | Kostya S. Novoselov | Amanda S Barnard | Sergei V. Kalinin | Steven R. Spurgeon | Karsten Reuter | M. Alducin | Volker L. Deringer | Gábor Csányi | Nicola Marzari | Shirong Huang | Gianaurelio Cuniberti | Qiushi Deng | Pablo Ordejón | Ivan Cole | Kamal Choudhary | Kedar Hippalgaonkar | Ruiming Zhu | O. Anatole von Lilienfeld | Mohamed Hibat-Allah | Juan Carrasquilla | Giulia Cisotto | Alberto Zancanaro | Wolfgang Wenzel | Andrea C. Ferrari | A. Ustyuzhanin | Stephan Roche
Source: Journal of Physics Materials (article, 2025-03-19)
Field: Materials Science — Materials Chemistry

Artificial intelligence for advanced functional materials: exploring current and future directions Abstract This perspective addresses the topic of harnessing the tools of artificial intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in energy storage, biomedicine, composites, nanoelectronics or quantum technologies. It gives a current view of experts in the field, insisting on challenges and opportunities provided by the development of large materials databases, novel schemes for implementing AI into materials production and characterization as well as progress in the quest of simulating physical and chemical properties of realistic atomic models reaching the trillion atoms scale and with near ab initio accuracy. Current (fluid) ; Computer science ; Cognitive science ; Psychology ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Chatrawee Scheiger | Jonas F. Pöhls | Mersad Mostaghimi | Lena Pilz | Mariana Kozłowska | Yidong Liu | Lars Heinke | Carlos César Bof Bufon | R. Thomas Weitz | Wolfgang Wenzel | Christof Wöll
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Dirac-cone induced metallic conductivity in Cu<sub>3</sub>(HHTP)<sub>2</sub>: high-quality MOF thin films fabricated <i>via</i> ML-driven robotic synthesis Metal-organic frameworks have garnered interest for over 25 years in energy and electronics, yet their adoption in devices has been hindered by low electrical conductivity, largely attributed to activated transport. Our study demonstrates a significant shift, revealing metallic conductivity in Cu<sub>3</sub>(HHTP)<sub>2</sub> thin films-240 S m<sup>-1</sup> at room temperature and 300 S m<sup>-1</sup> at 100 K, a departure from its presumed semiconductive nature. Achieved through robotic, AI-based layer-by-layer assembly in a self-driving laboratory, this method produces SURMOFs with minimal defects, optimized <i>via</i> rapid surrogate characterization techniques. Our research, supported by both electronic structure calculations and experimental verification, identifies a persistent Dirac cone in the hexagonal <i>D</i><sub>6h</sub> symmetry of 2D sheets as crucial for the observed metallic behavior. Notably, even with ABAB stacking in the bulk, this Dirac cone feature maintains metallic conductivity, enhancing at lower temperatures. This breakthrough not only clarifies the conduction mechanism in Cu<sub>3</sub>(HHTP)<sub>2</sub> but also highlights the SDL's potential in developing high-quality MOF thin films for future applications. Our findings indicate that tailoring the Dirac cone's energy could lead to a new class of highly conductive, metallic MOFs. Materials science ; Conductivity ; Metal-organic framework ; Cone (formal languages) ; Metal ; Thin film ; Optoelectronics ; Nanotechnology ; Composite material ; Chemical engineering ; Metallurgy ; Chemistry ; Organic chemistry ; Physical chemistry ; Computer science ; Engineering ZnO doping and properties ; Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Haowen Zhong | Yilan Liu | Haibin Sun | Yuru Liu | Rentao Zhang | Baochen Li | Shuicheng Yan | Yuqing Huang | Fei Yang | Frankie S. Mak | Klement Foo | Sen Lin | Tianshu Yu | Peng Wang | Xiaoxue Wang
Source: Nature Communications (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning Predicting organic reaction feasibility and robustness against environmental factors is challenging. We address this issue by integrating high throughput experimentation (HTE) and Bayesian deep learning. Diverging from existing HTE studies focused on niche chemical spaces, in this work, our in-house HTE platform conducted 11,669 distinct acid amine coupling reactions in 156 working hours, yielding the most extensive single HTE dataset at a volumetric scale for industrial delivery. Our Bayesian neural network model achieved a benchmark for prediction accuracy of 89.48% for reaction feasibility. Furthermore, our fine-grained uncertainty disentanglement enables efficient active learning, reducing 80% of data requirements. Additionally, our uncertainty analysis effectively identifies out-of-domain reactions and evaluates reaction robustness or reproducibility against environmental factors for scaling up, offering a practical framework for navigating chemical spaces and designing highly robust industrial processes. Robustness (evolution) ; Computer science ; Bayesian probability ; Throughput ; Deep learning ; Artificial intelligence ; Machine learning ; Biology ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Pavan Hiremath | Subraya Krishna Bhat | Jayashree Purkayastha | P. Krishnananda Rao | Krishnamurthy D. Ambiger | Murthy BRN | S. V. Udaya Kumar Shetty | Nithesh Naik
Source: Journal of Composites Science (article, 2025-07-14)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Polymer Nanocomposite Tensile Strength Through Gaussian Process Regression and Monte Carlo Simulation with Enhanced Model Reliability This study presents a robust machine learning framework based on Gaussian process regression (GPR) to predict the tensile strength of polymer nanocomposites reinforced with various nanofillers and processed under diverse techniques. A comprehensive dataset comprising 25 polymer matrices, 22 surface functionalization methods, and 24 processing routes was constructed from the literature. GPR, coupled with Monte Carlo sampling across 2000 randomized iterations, was employed to capture nonlinear dependencies and uncertainty propagation within the dataset. The model achieved a mean coefficient of determination (R2) of 0.96, RMSE of 12.14 MPa, MAE of 7.56 MPa, and MAPE of 31.73% over 2000 Monte Carlo iterations, outperforming conventional models such as support vector machine (SVM), regression tree (RT), and artificial neural network (ANN). Sensitivity analysis revealed the dominant influence of Carbon Nanotubes (CNT) weight fraction, matrix tensile strength, and surface modification methods on predictive accuracy. The findings demonstrate the efficacy of the proposed GPR framework for accurate, reliable prediction of composite mechanical properties under data-scarce conditions, supporting informed material design and optimization. Monte Carlo method ; Ultimate tensile strength ; Reliability (semiconductor) ; Materials science ; Polymer nanocomposite ; Polymer ; Composite material ; Computer science ; Statistical physics ; Biological system ; Statistics ; Mathematics ; Physics ; Thermodynamics ; Biology Machine Learning in Materials Science ; Polymer crystallization and properties ; Polymer Nanocomposites and Properties
Authors: Fang Wang | Shenhao Jiang | Jun Li
Source: Applied Sciences (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

The AI-Driven Transformation in New Materials Manufacturing and the Development of Intelligent Sports The advancement of materials science has had a profound, even revolutionary, impact on sports. Materials are used in the sports field, equipment, and sportswear, each with distinct functionality and safety requirements. Additionally, diverse sport-related data require physical devices for collection, analysis, and storage, which can be crucial in athlete selection, performance assessment, strategy planning, and training optimization. Artificial intelligence, with its strong cognitive abilities, learning capacity, large-scale data processing, and adaptability, can effectively enhance efficiency, reduce errors, and lower costs. The integration of advanced materials and artificial intelligence (AI) has significantly enhanced the efficiency and precision of research and development in sports-related technologies, while also facilitating the innovation of training methodologies through intelligent data analytics. This convergence has initiated a transformative phase in the digitalization of the sports industry. Anchored in both theoretical analysis and practical implementation, this study seeks to construct a systematic cognitive framework that elucidates the interrelationship between material science and AI technologies. The aim is to assist sports professionals in understanding and leveraging this technological shift to support strategic decision-making and to foster sustainable, high-quality development within the field. Manufacturing engineering ; Computer science ; Engineering Machine Learning in Materials Science
Authors: Linjing Hao | S.Q. Wang | Peng Li | Yijun Cao | Baolin Xing
Source: Reviews in Inorganic Chemistry (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Coal-based carbon/graphene quantum dots: formation mechanisms and applications Abstract The Carbon/graphene quantum dots (CQDs/GQDs) have received increasing attention as emerging zero-dimensional (0D) carbon nanomaterials. The CQDs/GQDs were endowed with significant potential in environmental and energy fields, due to the unique photoluminescence, good biocompatibility, excellent electron transfer capability, and low toxicity. Compared with graphite and hydrocarbons, coal and its derivatives are considered as alternative carbon sources for preparing CQDs/GQDs, as their low cost and abundant reserves. Thereby, the important issue of realization the deep processing and functional applications of coal is cracking the C–O bonds that connected the aromatic ring clusters in coal and its derivatives to obtain CQDs/GQDs. This article briefly reviews the synthesis methods and physicochemical properties of coal-based CQDs/GQDs in recent years, focusing on the formation mechanism of coal-based CQDs/GQDs and their functionalization. In addition, their applications in sensing, photocatalysis, electrochemistry and biomedicine are discussed, as well as the prospects for future research. Hopefully, this paper will provide significant support for the design of efficient coal-based carbon nanomaterials and sustainable options for overcoming bottlenecks in coal applications. Chemistry ; Graphene ; Quantum dot ; Carbon quantum dots ; Coal ; Carbon fibers ; Nanotechnology ; Organic chemistry ; Composite number ; Composite material Carbon and Quantum Dots Applications ; Graphene research and applications ; Catalytic Processes in Materials Science
Authors: Dong Chen | Chun‐Long Chen | Guo-Wei Wei
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Category-specific topological learning of metal–organic frameworks Metal-organic frameworks (MOFs) are porous, crystalline materials with high surface area, adjustable porosity, and structural tunability, making them ideal for diverse applications. However, traditional experimental and computational methods have limited scalability and interpretability, hindering effective exploration of MOF structure-property relationships. To address these challenges, we introduce, for the first time, a category-specific topological learning (CSTL), which combines algebraic topology with chemical insights for robust property prediction. The model represents MOF structures as simplicial complexes and incorporates elemental categorizations to enable balanced, interpretable machine learning study. By integrating category-specific persistent homology, CSTL captures both global and local structural characteristics, rendering multi-dimensional, category-specific descriptors that support a predictive model with high accuracy and robustness across eight MOF datasets, outperforming all previous results. This alignment of topological and chemical features enhances the predictive power and interpretability of CSTL, advancing understanding of structure-property relationships of MOFs and promoting efficient material discovery. Metal-organic framework ; Topology (electrical circuits) ; Chemistry ; Mathematics ; Combinatorics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods ; Supramolecular Self-Assembly in Materials
Authors: Jie Tang | Ruirui Li | S. Mahmood | Jiying Li | Shun Yao
Source: Chemosensors (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Carbon Nanotube-Based Chemical Sensors: Sensing Mechanism, Functionalization and Applications Carbon nanotubes (CNTs) have opened new routes in the field of chemical sensing due to their unparalleled electrical conductivity, high surface area, and versatile functionalization capabilities. This review systematically examined the latest advancements in CNT-based chemical sensors, with a focus on their sensing mechanism, functionalization strategies, and applications. A spotlight was cast on the wide-ranging applications of CNT-based chemical sensors, spanning environmental analysis, drug detection, healthcare, food quality control, gases detection, strain sensing, etc. Finally, through a comprehensive SWOT analysis, the strengths, weaknesses, opportunities, and existing threats, along with emerging trends of CNTs in the sensing field, were elucidated. This review systematically summarized the applications of CNTs across six major fields, highlighting more than 60 CNT-based sensing materials. We aim to provide a forward-looking perspective on how CNTs will continue to shape the future of chemical sensing. Surface modification ; Carbon nanotube ; Chemical sensor ; Chemical modification ; Carbon fibers ; Chemical process Carbon Nanotubes in Composites ; Mechanical and Optical Resonators ; Analytical Chemistry and Sensors
Authors: S. S. Millavithanachchi | Mylange Dona Kasundi Mekhala Gunasena | Chanaka Galpaya | H. V. V. Priyadarshana | Amalka Indupama Samarathunga | Ashan Induranga | W. A. C. N. Kariyawasam | D.V.S. Kaluthanthri | Kaveenga Rasika Koswattage
Source: Nanomaterials (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Green Synthesis, Optimization, and Characterization of CuO Nanoparticles Using Tithonia diversifolia Leaf Extract Green synthesis of copper oxide (CuO) nanoparticles offers a sustainable alternative to conventional chemical methods that often involve toxic reagents and harsh conditions. This study investigates the use of <i>Tithonia diversifolia</i>, an invasive species in Sri Lanka, as a bioreductant for the eco-friendly fabrication of CuO nanoparticles. Using copper sulfate (CuSO<sub>4</sub>·5H<sub>2</sub>O) as a precursor, eight treatments were conducted by varying precursor concentration, temperature, and reaction time to determine optimal conditions. A visible color change in the reaction mixture initially indicated nanoparticle formation. Among all the conditions, treatment T4 (5 mM CuSO<sub>4</sub>, 80 °C, 2 h) yielded the most favorable results in terms of stability, morphology, and crystallinity. UV-Vis spectroscopic analysis confirmed the synthesis, with absorbance peaks between 265 and 285 nm. FTIR analysis revealed organic functional groups and characteristic metal-oxygen vibrations in the fingerprint region (500-650 cm<sup>-1</sup>), confirming formation. SEM imaging showed that particles were mainly spherical to polygonal, averaging 125-150 nm. However, dynamic light scattering showed larger diameters (~240 nm) due to surface capping agents. Zeta potential values ranged from -16.0 to -28.0 mV, indicating stability. XRD data revealed partial crystallinity with CuO-specific peaks. These findings support the potential of <i>T. diversifolia</i> in green nanoparticle synthesis, suggesting a low-cost, eco-conscious strategy for future applications. Tithonia ; Crystallinity ; Nanoparticle ; Reagent ; Materials science ; Zeta potential ; Absorbance ; Copper ; Fourier transform infrared spectroscopy ; Chemical engineering ; Nuclear chemistry ; Nanotechnology ; Chemistry ; Organic chemistry ; Metallurgy ; Composite material ; Chromatography ; Botany Nanoparticles: synthesis and applications ; Laser-Ablation Synthesis of Nanoparticles ; Magnesium Oxide Properties and Applications
Authors: H.L. Andersen | Matilde Saura‐Múzquiz | Cecilia Granados‐Miralles | Rebekka Klemmt | Espen Drath Bøjesen | Mogens Christensen
Source: CrystEngComm (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Crystal/magnetic structure and cation inversion in hydrothermally synthesized MnFe<sub>2</sub>O<sub>4</sub>, CoFe<sub>2</sub>O<sub>4</sub>, NiFe<sub>2</sub>O<sub>4</sub>, and ZnFe<sub>2</sub>O<sub>4</sub> nanoparticles: a neutron powder diffraction study Joint PXRD and NPD analysis of crystal and magnetic structures of MnFe 2 O 4 , CoFe 2 O 4 , NiFe 2 O 4 and ZnFe 2 O 4 nanoparticles reveal how cation type affects spinel inversion, crystallite size, and magnetism. Materials science ; Crystallography ; Crystal structure ; Inversion (geology) ; Inorganic chemistry ; Chemistry ; Geology Magnetic Properties and Synthesis of Ferrites ; X-ray Diffraction in Crystallography ; Iron oxide chemistry and applications
Authors: Lixin Ma | Ruiyun Zhou | Limei Yin | Li Sun | En Han | Junwen Bai | Jianrong Cai
Source: Foods (review, 2025-08-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Simultaneous Detection of Food Contaminants Using Surface-Enhanced Raman Scattering (SERS): A Review Surface-enhanced Raman scattering (SERS) technology has garnered significant attention for the detection of trace food contaminants, due to its exceptional sensitivity, non-destructive nature, and molecular fingerprinting capabilities. Currently, SERS applications in the simultaneous detection of multiple contaminants have advanced rapidly. SERS-based simultaneous detection strategies are generally categorized into label-free and labeled detection methods. Labeled detection can be further divided into SERS encoding detection and spatial isolation detection, with Raman reporter molecules playing a key role in SERS encoding. This article reviews the strategies, principles, common Raman reporter molecules, and practical applications of SERS-based simultaneous detection. Additionally, this article explores the challenges and future directions of SERS technology in contaminant detection, with an emphasis on the development of stable, intelligent substrates; improvements in analytical algorithms; and the creation of portable, on-site detection platforms. This study seeks to offer valuable insights into the development of SERS-based methods for simultaneously detecting multi-contaminants. Raman scattering ; Contamination ; Raman spectroscopy ; Nanotechnology ; Materials science ; Environmental chemistry ; Environmental science ; Chemistry ; Biology ; Optics ; Physics ; Ecology Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Matthias Kellner | Jacob B. Holmes | Ruben Rodriguez-Madrid | Francesca Viscosi | Yuxuan Zhang | Lyndon Emsley | Michele Ceriotti
Source: The Journal of Physical Chemistry Letters (article, 2025-08-18)
Field: Chemistry — Spectroscopy

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critically on accurate and fast predictions of chemical shieldings, and machine learning (ML) models for shielding predictions are being increasingly used as scalable and efficient surrogates for demanding ab initio calculations. However, the prediction accuracies of current ML models still lag behind those of the DFT reference methods they approximate. Here, we introduce ShiftML3, a deep-learning model that improves the accuracy of predictions of isotropic chemical shieldings in molecular solids, and does so while also predicting the full shielding tensor. On experimental benchmark sets, we find root-mean-squared errors with respect to experiment for ShiftML3 that approach those of DFT reference calculations, with RMSEs of 0.53 ppm for <sup>1</sup>H, 2.4 ppm for <sup>13</sup>C, and 7.2 ppm for <sup>15</sup>N, compared to DFT values of 0.49, 2.3, and 5.8 ppm, respectively. Chemical shift ; Isotropy ; Ab initio ; Anisotropy ; Electromagnetic shielding ; Tensor (intrinsic definition) ; Chemistry ; Benchmark (surveying) ; Computational chemistry ; Materials science ; Statistical physics ; Physics ; Physical chemistry ; Mathematics ; Optics Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Ran An | Hongshan Wang | Congwei Xie | Mengfan Wu | Dongdong Chu | Wenqi Jin | Junjie Li | Shilie Pan | Zhihua Yang
Source: Small (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

New Ways to Discover Novel Nonlinear Optical Materials: Scaling Machine Learning with Chemical Descriptors Information The efficient experimental exploration of innovative nonlinear optical materials has long been a challenging task due to the vast chemical space and the lack of suitable theoretical prediction frameworks. Herein, a novel theoretical design paradigm is proposed to accelerate the discovery of novel materials with strong second harmonic generation intensity. This challenge is addressed through several key technologies. 1) A high-precision machine learning model is proposed on the maximum nonlinear optical dataset. 2) Descriptors information paves the way to systematically offer valuable chemical insights for designing chemical structures. 3) A flexible and fast chemical space construction and exploration method is proposed. Accordingly, a nonlinear optical crystal is successfully synthesized through the constructed "machine to knowledge" theoretical framework. This novel compound exhibits a stronger second-harmonic generation response and wider optical transmission range. This work introduces novel theoretical design concepts and provides innovative chemical insights into optical materials or other functional materials. Chemical space ; Computer science ; Nonlinear optical ; Key (lock) ; Nonlinear system ; Scaling ; Range (aeronautics) ; Second-harmonic generation ; Artificial intelligence ; Space (punctuation) ; Transmission (telecommunications) ; Machine learning ; Nanotechnology ; Materials science ; Drug discovery ; Chemistry ; Physics ; Mathematics ; Optics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films