jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yoshihisa Osano | Tatsuyuki Nakatani
Source: Journal of Photopolymer Science and Technology (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Raman Spectral Data–Driven Analysis of Diamond-like Carbon Films Deposited Using the Radio-frequency Plasma-enhanced Chemical Vapor Deposition Method Herein, spectral data–driven approaches were applied to analyze the carbon structure of diamond-like carbon (DLC) films deposited by the radio-frequency plasma-enhanced chemical vapor deposition and predict their properties. The correlation between the carbon structure and the film properties was further elucidated by combining an automated five-peak Raman deconvolution with heatmap visualization. Furthermore, the accuracy of film density predictions using partial least squares regression was profoundly improved through a variable selection method based on five-peak deconvolution analysis. These results demonstrate the effectiveness of this spectral data–driven approach in the research and development of DLC films, paving the way for their further improvements and broad applications Materials science ; Chemical vapor deposition ; Raman spectroscopy ; Carbon film ; Plasma ; Plasma-enhanced chemical vapor deposition ; Diamond ; Diamond-like carbon ; Radio frequency ; Carbon fibers ; Combustion chemical vapor deposition ; Deposition (geology) ; Chemical engineering ; Analytical Chemistry (journal) ; Optoelectronics ; Nanotechnology ; Thin film ; Composite material ; Environmental chemistry ; Optics ; Computer science ; Chemistry ; Telecommunications ; Composite number Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Ion-surface interactions and analysis
Authors: Sadegh Kaviani
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Insights into the Electronic Structure of Il@Pvdf Complexes: A Dft–Ml Approach for High-Performance Gel Polymer Electrolytes Electrolyte ; Polymer ; Materials science ; Polymer electrolytes ; Electronic structure ; Chemical engineering ; Polymer chemistry ; Composite material ; Chemistry ; Engineering ; Physical chemistry ; Computational chemistry ; Ionic conductivity Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Anjali Krushna Kadao | Patil Manisha Prashant | Sunaina Sardana
Source: International Journal of Engineering Science and Information Technology (article, 2025-05-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-Assisted 3D-Printed Biomaterial Supercapacitors for Green Energy Storage Advancements of biomaterial-based supercapacitors have been fuelled by the growing demand for sustainable and high-performance energy storage solutions. This work suggests the use of artificial intelligence to develop an AI-assisted 3D printed biomaterial supercapacitor, namely comprising electrode materials optimised by artificial intelligence (AI), bio-based electrolytes, and intelligent performance monitoring to increase efficiency and sustainability. It is an AI-driven approach that selects and optimises the biomaterials: high conductivity, low internal resistance, and excellent charge retention. Porous electrodes can be deliberately engineered on microscales by advanced 3D printing techniques; these perform well in facilitating fast ion diffusion and high energy storage capacity. This is achieved through experimental results of a 45% increase in capacitance, 68% reduction in charge transfer resistance, and 18% improvement in cycle stability on conventional supercapacitors. Moreover, AI-powered predictive maintenance increases the life of the device by 60%, thereby reducing unplanned failure by 60%. The involvement of biodegradable and non-toxic inclusion of materials encourages environmental sustainability, and thus, this supercapacitor is a green alternative for next-generation energy storage applications. This solution is suitable for wearable electronics, renewable energy systems, as well as smart devices, with high efficiency, low environmental impact and intelligent monitoring capability. The energy storage technology presents instances where AI, biomaterials, and 3D printers have the potential to transform the energy storage technology into a scalable, eco-friendly, and intelligent supercapacitor for future energy demands, according to this study. Supercapacitor ; Biomaterial ; 3d printed ; Energy storage ; Materials science ; Nanotechnology ; Engineering ; Manufacturing engineering ; Chemistry ; Physics ; Capacitance Supercapacitor Materials and Fabrication
Authors: Oleg I. Afanasyev | Fedor S. Kliuev | Alexandra I. Balalaeva | Denis Chusov
Source: ChemRxiv (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Rethinking Reaction Discovery: From Serendipity to Data-Driven Chemistry Serendipity ; Scientific discovery ; Field (mathematics) ; Chemical reaction ; Artificial life ; Chemical products Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Maxim Moraru | Weiyi Xia | Zhuo Ye | Feng Zhang | Yan Yao | Ying Wai Li | Cai‐Zhuang Wang
Source: ArXiv.org (preprint, 2025-06-26)
Field: Materials Science — Materials Chemistry

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design exa-AMD is a Python-based application designed to accelerate the discovery and design of functional materials by integrating AI/ML tools, materials databases, and quantum mechanical calculations into scalable, high-performance workflows. The execution model of exa-AMD relies on Parsl, a task-parallel programming library that enables a flexible execution of tasks on any computing resource from laptops to supercomputers. By using Parsl, exa-AMD is able to decouple the workflow logic from execution configuration, thereby empowering researchers to scale their workflows without having to reimplement them for each system. Workflow ; Scalability ; Workflow technology ; Workflow management system ; Resource (disambiguation) ; Execution model Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Catalytic Processes in Materials Science
Authors: Sreenivas Raguraman | Adam J. Griebel | Maitreyee Sharma Priyadarshini | Paulette Clancy | Timothy P. Weihs
Source: Nature Reviews Materials (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Author Correction: A call to elevate the role of processing in AI-driven materials design Design elements and principles ; Materials processing Machine Learning in Materials Science ; Nanoporous metals and alloys ; Material Selection and Properties
Authors: Enoch Sin-Hang Ho | Connor Barlow | Ashton Roma | Mattew J. Lutz | Natalie K. Green | Shea Smith | David J. Michaelis | Jeremy A. Johnson
Source:  (article, 2025-08-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Prediction of Optical Nonlinearity in Crystalline Materials Nonlinear optical (NLO) crystals are capable of light frequency conversion through NLO processes, such as optical rectification and second-harmonic generation. The strength of the NLO responses is related to the nonlinear electric susceptibility χ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n)</sup>. We developed a computational approach to calculate the 2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order electric susceptibility χ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</sup> and nonlinear polarization (P<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NL</sup>) for over seventy thousand structures extracted from the organic Cambridge structural database. The calculated values are consistent to the reported values and the THz generation from measurements, validating the accuracy of our approach. Optical rectification ; Rectification ; Nonlinear system ; Nonlinear optical ; Nonlinear optics ; Sum-frequency generation ; Terahertz radiation Nonlinear Optical Materials Research ; Crystallography and molecular interactions ; Crystal Structures and Properties
Authors: Tian-Wei Hua | Gui‐Xiang Huang | Chen Qian | Hao Zeng | Jun Jiang | Han‐Qing Yu
Source: ChemRxiv (preprint, 2025-02-09)
Field: Materials Science — Materials Chemistry

Data-driven recursive kinetic modeling for chemical reactions Kinetic energy ; Chemistry ; Computer science ; Computational chemistry ; Physics ; Classical mechanics Machine Learning in Materials Science ; Advanced Control Systems Optimization
Authors: Emina Hadzialic | Alexander Ryzhov
Source: Open MIND (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Interpretable data-driven battery model based on tensor trains Train ; Battery (electricity) ; Tensor (intrinsic definition) ; Control theory (sociology) ; Key (lock) Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced Battery Technologies Research
Authors: Ke-Wei Chen | Cuizhen Wang | Hsu-Nan Huang | Xing Wu | Jianan Chen | Jian‐Tai Qiu | Kin Kuan HOI | Tianyu Sun | Jian‐bo Wang | Yun‐Dong Wu
Source: CCS Chemistry (article, 2025-05-08)
Field: Chemistry — Inorganic Chemistry

Converting Dimeric Enzymes into Efficient Monomeric Catalysts Using Artificial Intelligence-Assisted Models Monomer ; Catalysis ; Enzyme ; Chemistry ; Combinatorial chemistry ; Biochemistry ; Organic chemistry ; Polymer Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation ; Enzyme Catalysis and Immobilization
Authors: Kai Zhao | Xiting Peng | Ran Tao | Yuchen Su | Shiyue Huang | Hang Fu | Xiaonan Wang | Shanying Hu
Source: Green Chemistry (article, 2025-12-19)
Field: Chemistry — Physical and Theoretical Chemistry

Toward comprehensive scientific information on plastic-related chemicals powered by artificial intelligence A series of AI-based methods were developed to construct a comprehensive plastic-related chemical database, supporting the advancement of scientific understanding of plastic pollution. Construct (python library) ; Applications of artificial intelligence ; Expert system ; Information system History and advancements in chemistry ; Effects and risks of endocrine disrupting chemicals ; Microplastics and Plastic Pollution
Authors: Shengli Jiang | Michael A. Webb
Source: ChemRxiv (preprint, 2025-08-13)
Field: Materials Science — Materials Chemistry

Data-Driven and Physics-Guided Design of Viscosity-Modifying Polymers Viscosity ; Polymer physics ; Polymer ; Statistical physics ; Physics ; Polymer science ; Materials science ; Thermodynamics ; Nuclear magnetic resonance Machine Learning in Materials Science
Authors: Tongying Liu | Minghuan Mao
Source: Academic Journal of Science and Technology (article, 2025-11-26)
Field: Chemistry — Organic Chemistry

The Research on the Synthesis Method of Sulphonamide and the Scheme for Improving the Synthesis Technology Sulphonamides, as one of the earliest classes of synthetic antibacterial agents, remain essential in pharmaceutical and chemical industries due to their broad-spectrum activity and versatile applications. This study reviews the classical multi-step synthesis of sulphanilamide, analyzing its reaction mechanisms, operational limitations, and environmental challenges, including high energy consumption, low yield, and the formation of toxic by-products. Through laboratory validation, the traditional route was confirmed to be feasible but inefficient, motivating the exploration of innovative synthesis technologies. Based on a comparative literature review, this paper examines three contemporary optimization strategies: continuous flow technology (CFT), green chemistry (GC), and electrochemical synthesis (ECS). Continuous flow technology improves reaction control, safety, and scalability; green chemistry promotes atom economy and environmental sustainability; and electrochemical synthesis introduces a waste-free, catalyst-efficient approach suitable for industrial application. Case studies from leading enterprises demonstrate that these methods significantly enhance product yield, reduce carbon emissions, and lower resource consumption. Despite these advancements, standardization, high equipment cost, and interdisciplinary technical barriers remain key challenges. The study concludes that integrating automation, artificial intelligence, and modular electrochemical systems may further advance sulphonamide synthesis toward sustainable industrial production. Modular design ; Green chemistry ; Atom economy ; Continuous flow ; Resource (disambiguation) ; Electrochemistry ; Scheme (mathematics) ; Industrial ecology ; Chemical synthesis Sulfur-Based Synthesis Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation ; Microwave-Assisted Synthesis and Applications
Authors: Ye Yu | Zikai Xie | Man Luo | Adam Redfearn | Arianna Brandolese | Chenxi Sheng | Professor Andrew P Dove | Xiaolong Zhang | Libin Chen | Jun Jiang
Source: ChemRxiv (article, 2025-08-14)
Field: Chemistry — Organic Chemistry

Optimizing toward discovery: AI-driven exploration of Lewis acid–base catalysts for PET glycolysis Catalysis ; Glycolysis ; Yield (engineering) ; Lewis acids and bases Advanced Polymer Synthesis and Characterization ; Supramolecular Chemistry and Complexes ; Carbon dioxide utilization in catalysis
Authors: Dr.Panjab Chavan
Source: International Journal of Science Engineering and Technology (article, 2025-04-15)
Field: Chemistry — Physical and Theoretical Chemistry

International Conference on Artificial Intelligence in Mathematical and Chemical Sciences (ICAIMCS-2025) Souvenir Book April 29 2025 SOUVENIR BOOK Editorial Board Chief Editor Dr.Panjab Chavan Principal Associate Editor Dr. S. B. Chavhan Head PG Department of Mathematics Editors (Mathematical Sciences) Dr. M. R. Fegade Dr. O. K. Berdewad Mr. P.V.Takey Editors (Chemical Sciences) Dr. ... Read More » Artificial intelligence ; Computer science ; Library science History and advancements in chemistry ; Advanced Data Processing Techniques
Authors: Jonathan
M. Mauß (21701748) | Klara S. Kley (21701751) | Rohini Khobragade (20290336) | Nguyen-Khang Tran (21701754) | Jacopo de Bellis (21701757) | Ferdi Schüth (1534537) | Matthias Scheffler (104879) | Lucas Foppa (2364046)
Source: Figshare (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Modeling Time-On-StreamCatalyst Reactivity in theSelective Hydrogenation of Concentrated Acetylene Streams under IndustrialConditions via Experiments and AI Describing heterogeneous catalysis is complicated by the intricate interplay of processes that govern catalyst performance. The evolving chemical environment and the kinetics of catalyst’s structural changes during reactions often lead to unknown local geometries and chemistry, which can shift reactivity over time. Here, we perform systematic experiments and apply a focused artificial-intelligence (AI) approach to model the measured time-on-stream-dependent reactivity of palladium-based bimetallic catalysts. These materials are synthesized via mechanochemistry and applied in the selective hydrogenation of concentrated acetylene streams(>14.0 vol %)under industrially relevant pressures (10 bar), resulting from a hypothetical electric plasma-assisted methane-to-ethylene process. Unlike the well-established hydrogenation of diluted acetylene (0.1 to 2.0 vol %) streams of naphtha steam cracking, the hydrogenation of concentrated acetylene streams remains largely underexplored due to the harsh reaction conditions and the explosive nature of acetylene. This precludes <i>operando</i> characterization or atomistic simulations to investigate catalyst time-on-stream behavior under realistic conditions. Our AI approach first uses subgroup discovery to identify descriptions of materials and reaction conditions resulting in noticeable acetylene conversion. Then, it models time-dependent selectivity focused on high acetylene conversion via the sure-independence-screening-and-sparsifying operator symbolic-regression approach. AI identifies key experimental and theoretical physicochemical descriptive parameters correlated with the reactivity, which highlight the critical interplay between the material structure and the chemical potential of the reaction mixture. The AI models enable the design of bimetallic and trimetallic catalysts, which are experimentally validated. Acetylene ; Reactivity (psychology) ; Naphtha ; Bimetallic strip ; Catalysis ; Selectivity ; Chemical reaction ; Reaction mechanism Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Xiaohu Ge | Yundao Jing | Nihong An | Yueqiang Cao | Jing Zhang | Gang Qian | Xinggui Zhou | De Chen | Weikang Yuan | Xuezhi Duan
Source: Angewandte Chemie (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Layered Double Hydroxides as Building Blocks for Precise Catalysis Abstract Layered double hydroxides (LDHs) are structurally tunable, 2D materials that serve as versatile precursors for the design of heterogeneous catalysts. Their unique lamellar architecture, topotactic transformation ability, and compositional flexibility enable the fabrication of monometallic, bimetallic, and intermetallic catalysts with atomically controlled active sites. These LDH‐derived catalysts feature high metal dispersion, strong metal–support interactions, and defect‐rich interfaces, which are key attributes for precise catalysis. Recent advances have demonstrated their broad applicability in electrocatalysis (e.g., oxygen evolution and CO 2 reduction), selective hydrogenation, direct and oxidative dehydrogenation, and syngas conversion. Across these reactions, LDH‐derived systems offer enhanced activity, selectivity, and stability by tailoring active site geometry, electronic structure, and interfacial properties. This review summarizes the advances in synthesis, structure–function relationships, and catalytic applications of LDH‐based catalysts. Despite significant progress, challenges remain in understanding transformation mechanisms, capturing dynamic active sites under working conditions, and establishing predictive design rules. Future efforts combining operando spectroscopy, theory, and data‐driven approaches are expected to unlock the full potential of LDH‐derived materials for precise catalysis in sustainable energy and chemical transformations. Catalysis ; Layered double hydroxides ; Electrocatalyst ; Lamellar structure ; Flexibility (engineering) ; Oxygen evolution ; Intermetallic ; Heterogeneous catalysis ; Fabrication Layered Double Hydroxides Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Gabrielly Cristina De Jesus Lima | Maria Elisa Philippsen Missner | Afonso Henrique da Silva Júnior | Gabriel Garbin Togni | Rodrigo Schlindwein | Cátia Rosana Lange de Aguiar | Carlos Rafael Silva de Oliveira
Source:  (review, 2025-01-01)
Field: Chemistry — Spectroscopy

PERSPECTIVES ON THE APPLICATION OF AEROGELS IN TEXTILE MATERIALS FOR OIL ADSORPTION AND THEIR AI-ASSISTED IMPROVEMENT: A CONCISE REVIEW Adsorption ; Textile ; Materials science ; Chemical engineering ; Nanotechnology ; Process engineering ; Chemistry ; Composite material ; Engineering ; Organic chemistry Aerogels and thermal insulation ; Surface Modification and Superhydrophobicity
Authors: Lin Huang (288599) | Chao Zhang (51048) | Yun Fu (210223) | Yibin Jiang (702035) | Enyu He (19648031) | Ming-Qiang Qi (17132305) | Ming-Hao Du (8932217) | Xiang-Jian Kong (1697383) | Jun Cheng (194158) | Leroy Cronin (538206) | Cheng Wang (102692)
Source: Figshare (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Natural-Language-InterfacedRobotic Synthesis forAI-Copilot-Assisted Exploration of Inorganic Materials The automation of chemical synthesis presents opportunities to enhance experimental reproducibility and accelerate discovery. Traditional closed-loop approaches, while effective in specific domains, are often constrained by rigid workflows and the requirement for specialized expertise. Here, we introduce a chemical robotic explorer integrated with an artificial intelligence (AI) copilot to enable a more flexible and adaptive synthesis, simplifying the process from inspiration to experimentation. This modular platform uses a large language model (LLM) to map natural language synthetic descriptions to executable unit operations, including temperature control, stirring, liquid and solid handling, filtration, etc. By integrating AI-driven literature searches, real-time experimental design, conversational human–AI interaction, and feedback-based optimization, we demonstrate the capabilities of AI in successfully synthesizing 13 compounds across four distinct classes of inorganic materials: coordination complexes, metal–organic frameworks, nanoparticles, and polyoxometalates. Notably, this approach enabled the discovery of a previously unreported family of Mn–W polyoxometalate clusters, showing the potential of AI-enhanced robotics as a generalizable and adaptable platform for material innovation. Executable ; Modular design ; Automation ; Workflow ; Robotics Polyoxometalates: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Vanderson Dornelas | Sergio A. Oliveira | Marcelo A. Savi
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Multiphysics Description of Smart Materials Using the Data-Driven Prismatic Model Multiphysics ; Smart material ; Work (physics) ; Finite element method Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Yoshitaka Adachi | Fei Sun | Akinori Yamanaka
Source: Materia Japan (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Super Thermal Field Materials Informatics: Data-Driven Discovery and Optimization Field (mathematics) ; Thermal ; Work (physics) ; Thermal conductivity Thermal properties of materials ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Anupama Chalimeswamy | S. Vimal Kumar | Ashok Shettar | C Y
Source: Journal of Orthopaedics (article, 2025-12-03)
Field: Materials Science — Polymers and Plastics

Programmable materials: A comprehensive review on shape memory polymers for orthopedic applications Shape-memory polymer ; Shape-memory alloy ; Scaffold ; Prospectus ; Polymer ; Smart material Polymer composites and self-healing ; Shape Memory Alloy Transformations ; Hydrogels: synthesis, properties, applications