jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Beemkumar Nagappan | Kanu Priya | Kulmani Mehar | Praveen Priyaranjan Nayak | Shailesh Kumar | Madhu Jain | A Shwetha | Aseel Samrat
Source: Journal of Science Advanced Materials and Devices (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Advances in thermoelectronic materials and devices for self-sustaining wearable and IoT systems Thermoelectric devices that facilitate the conversion of low-grade thermal gradients into electrical energy are increasingly recognized as essential elements for self-sustaining wearable electronics and autonomous Internet of Things (IoT) infrastructures. This review provides a comprehensive evaluation of recent advancements in thermoelectric materials, flexible device architectures, and system-level power management methodologies that have been documented over the past five years. Principal areas of emphasis encompass nanostructuring, band engineering, and defect modulation strategies that augment the thermoelectric figure of merit (ZT) and power factor under low-ΔT conditions. Innovations in conducting polymers, hybrid nanocomposites, and low-dimensional materials are underscored for their mechanical flexibility, stretchability, and compatibility with scalable processing techniques. Comparative assessments of benchmark materials, including Bi 2 Te 3 alloys, SnSe, Poly(3,4-ethylenedioxythiophene) (PEDOT): poly(styrenesulfonate) (PSS), and Carbon nanotube (CNT)/polymer composites, are presented with direct correlations to device-level performance metrics relevant to wearable applications and distributed sensor networks. In addition to summarizing advancements, this review emphasizes that successful commercialization will depend on the coordinated optimization of high-ZT, low-toxicity materials, robust architectures, and ultra-low-power electronic systems. Challenges such as scalable synthesis, long-term thermomechanical reliability, and sustainable recycling practices are critically scrutinized. Furthermore, the review aligns prospective research trajectories with Sustainable Development Goals 3 (Good Health and Well-being) and 7 (Affordable and Clean Energy) by promoting battery-free, environmentally sustainable wearable and IoT technologies. • Recent breakthroughs push ZT >2.0 in SnSe and >1.2 in Bi 2 Te 3 alloys. • Flexible thermoelectric modules achieve >50 μW·cm -2 under body-heat gradients. • Devices retain >90% efficiency after 10,000 bending cycles, ensuring durability. • Hybrid nanocomposites and polymers enable scalable, wearable thermoelectronics. • AI-driven discovery and sustainable design chart the path to commercialization. Scalability ; Wearable computer ; Wearable technology ; Electronics ; Internet of Things ; Thermoelectric effect ; Commercialization ; Modular design ; Efficient energy use Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Thermal properties of materials
Authors: Isaura Pilar Sánchez | Iván Salgado | R. Pérez | Ileana Fuentes Camargo | Julia L. Rodríguez
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Analysis and Robust Modeling of Radical Polymerization Kinetics: A Case of Study with Vinylpyridines Radical polymerization ; Polymerization ; Bulk polymerization ; Nitroxide mediated radical polymerization ; Radical Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Gräfensteiner, Phillip | Osenberg, Markus | Hilger, André | Bohn, Nicole | Binder, Joachim R. | Manke, Ingo | Schmidt, Volker | Neumann, Matthias
Source: KITopen (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes Cathode ; Nanoporous ; Phase (matter) ; Battery (electricity) ; Mathematical model Machine Learning in Materials Science ; Advancements in Battery Materials ; Low-power high-performance VLSI design
Authors: Yuchen Gao | Yu‐Hang Yuan | Suozhi Huang | Nan Yao | Legeng Yu | Yao‐Peng Chen | Qiang Zhang | Xiang Chen
Source: Angewandte Chemie (article, 2025-01-21)
Field: Materials Science — Materials Chemistry

Frontispiz: A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes In their Research Article (e202416506), Xiang Chen and co-authors developed a knowledge–data dual-driven framework that incorporates domain expertise into artificial intelligence models, achieving notable accuracy in predicting properties such as melting, boiling, and flash points of battery electrolytes. Battery (electricity) ; Dual (grammatical number) ; Electrolyte ; Nanotechnology ; Materials science ; Chemistry ; Computer science ; Physics ; Electrode ; Physical chemistry ; Thermodynamics ; Power (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Sekanina, Štěpán
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-11-22)
Field: Chemistry — Inorganic Chemistry

From Axiomatic Hallucination to Crystalline Order: MOF-ChemUnity as Evidence of Standard Chemistry's Failure The recent development of the AI-driven MOF-ChemUnity tool, a technical achievement recognized by the 2025 Nobel Prize, successfully manages the explosive complexity of Metal-Organic Frameworks (MOFs) data. This work argues that the necessity for such a complex system is a direct symptom of an axiomatic crisis in conventional chemistry, which lacks a unifying principle2. The Granulated Entropic Field (GEP) offers a radical chemical redefinition: MOFs are stable, ultra-low-entropy configurations of Entropic Matter Synthesis (EMS), whose function (e.g., $\text{CO}_2$ separation) is governed by Thermodynamic Necessity. GEP replaces the infinite data chaos managed by MOF-ChemUnity with a single, predictive thermodynamic law, thereby unmasking the AI tool as merely managing the "zombie axioms" of the Standard Model. This approach lays the true, unified foundation for future materials discovery through vertical unification (connecting cosmology, physics, and chemistry) Unification ; Axiom ; Axiomatic system ; Function (biology) ; Work (physics) ; Field (mathematics) ; Explosive material ; Scientific discovery Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Guillaume Deffrennes | Rong‐Jun Xie | Quentin Bizot | Ryo Tamura
Source: SPIRE - Sciences Po Institutional REpository (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

A Data-Driven Study of the Thermodynamic Properties of Mixing for Improved Phase Prediction in Complex Concentrated Alloys International audience Mixing (physics) ; Phase (matter) ; Work (physics) ; Material properties ; Entropy of mixing Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; High Temperature Alloys and Creep
Authors: Trung Quang Nguyen | Minh Quang Bui | Trương Ngọc Minh | Thao Thanh Nguyen
Source: Preprints.org (preprint, 2025-06-13)
Field: Chemistry — Spectroscopy

Sample Preparation for Simultaneous Determination of Organic Compounds by Chromatography Simultaneous determination of multiple organic compounds in complex sample matrices remains a significant analytical challenge due to the diverse chemical properties and low concentration levels of target analytes. This review comprehensively evaluates current sample preparation techniques applied in multi-residue analysis coupled with chromatographic methods such as GC-MS/MS and LC-HRMS. Techniques including QuEChERS, solid-phase extraction (SPE), dispersive SPE, and solid-phase microextraction (SPME) demonstrate extraction recoveries ranging from 70% to 95% for various compound classes, with detection limits often reaching low ng/L or ng/g levels. The choice of sample preparation method is critically influenced by analyte polarity, solubility, and matrix complexity, with over 50% of reported studies focusing on food and environmental water samples. Furthermore, this review highlights the growing potential of artificial intelligence (AI) in enhancing chromatographic data interpretation and compound identification, though emphasizes that effective sample preparation remains foundational for AI-driven analysis. The integration of optimized sample preparation workflows with AI-assisted data processing promises to improve throughput, accuracy, and robustness in multi-residue analysis, supporting regulatory compliance and environmental monitoring efforts worldwide. Chromatography ; Sample (material) ; Sample preparation ; Chemistry Analytical Chemistry and Chromatography
Authors: Jianbo Pang | Yuanchao Yang | Cheng Li | Jin Tian | Pengfei Dang | Fan Ye | Bofeng Shi | Yumei Zhou | Xiangdong Ding | Jun Sun | Dezhen Xue | Turab Lookman
Source: Acta Materialia (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Theory guided data-driven design of superelasticity in TiNi alloys over wide temperature window Pseudoelasticity ; Alloy ; Phase (matter) ; Dopant ; Window (computing) ; Phase transition ; Shape-memory alloy ; Transition temperature Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties ; High Entropy Alloys Studies
Authors: Gonzalo Darío Nuñez Rapan
Source: Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya) (article, 2025-10-24)
Field: Chemistry — Organic Chemistry

Insights Into Pharmaceutical and Fine Chemistry Using Density Functional Theory, Data-Driven Approaches and Novel Descriptors Aquesta tesi integra química quàntica moderna, anàlisi estadística i aprenentatge automàtic per desenvolupar estratègies computacionals que aprofundeixen en la comprensió mecanística i permeten prediccions basades en dades en sistemes químics diversos rellevants per a la química fina i farmacèutica.Una primera línia de recerca va explorar la reacció sense metalls de transició C(sp²)–N de trifluoroborats–iminiums primaris alquil-substituïts (pTIMs) amb N-fluorobenzensulfonimida (NFSI), en col·laboració amb Sandoz, cercant rutes més verdes cap a intermediaris farmacèutics. Les investigacions computacionals van descobrir un escenari mecànic dual amb dues vies paral·leles que expliquen els intermediaris observats, subproductes i estereoselectivitat, oferint una visió unificada consistent amb els experiments.De manera independent, s’ha estudiat la hidroformilació de l’estirè catalitzada per pal·ladi utilitzant formaldehid com a substitut del gas de síntesi, en col·laboració amb el Dr. Godard i IFF. Aquest treball és rellevant acadèmicament i industrialment, donat l’avantatge econòmic del Pd i la seva regioselectivitat diferent respecte dels catalitzadors de Rh. L’estudi va revelar un cicle catalític inesperat de Pd(II) que forma selectivament aldehids lineals, competint amb una via Pd(0)/Pd(II) d’hidrogenació. Models microcinètics parametritzats van identificar passos clau i van aclarir el paper dels additius com a cocatalitzadors i la influència de les condicions de reacció.A més, la tesi investiga com els patrons de fluoració i les arquitectures moleculars afecten la lipofília (logD) i la basicitat (pKa) en derivats fluorats de piridina. Regressions multilineals amb descriptors estructurals i computacionals van establir sòlides relacions quantitatives estructura–propietat (QSPR), oferint claus per modular propietats farmacocinètiques en el disseny de fàrmacs.Finalment, s’han desenvolupat descriptors 3D i basats en grafs per predir activitat i enantioselectivitat en catàlisi organometàl·lica. L’eina oberta NEST quantifica volums ocupats de catalitzadors allargats; 3D-AIMDA capta efectes estereo-electrònics a partir d’anàlisi QTAIM; i un enfocament graf-teòric modelitza barreres d’activació i estats de transició. Modular design Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis ; Organoboron and organosilicon chemistry
Authors: Patil,, Sujata Anandrao
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Applications of AI in Synthesis of ZnO Thin Film Abstract Artificial Intelligence (AI) helps to synthesis and characterization of ZnO thin film using different types of synthesis processes such as Hydrothermal process, Chemical Bath Deposition process, Sol-gel process, Chemical Vapour Deposition process, etc. Artificial intelligence (AI) is used to analyze data from various experiments in the synthesis process of zinc oxide (ZnO) thin films, optimize the experiments, and find the best conditions for films with good properties. This uses AI to predict synthesis processes and improve properties based on experimental results, enabling accurate and efficient production. Thin film ; Characterization (materials science) ; Chemical vapor deposition ; Deposition (geology) ; Zinc ; Hydrothermal circulation ; Chemical bath deposition Machine Learning in Materials Science ; ZnO doping and properties ; Stock Market Forecasting Methods
Authors: Panyalak Detrattanawichai | Zhenzhu Li | Hyunsoo Park | Kinga O. Mastej | Aron Walsh
Source: ChemRxiv (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

Data-driven exploration of AB2X4 (X = O, S, Se, Te) spinel chemical space Spinel ; Workflow ; Chemical space ; Space (punctuation) ; Energy (signal processing) ; Heuristic Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Cheol Hwan Kim | Jae H. Kim | Sung‐Yeob Jeong | Bo Sung Shin
Source: Scientific Reports (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Correction: Atomistic and data-driven modeling of laser-induced graphene formation on sustainable polymer substrates Graphene ; Polymer ; Molecular dynamics ; Multiscale modeling Graphene research and applications ; Laser-Ablation Synthesis of Nanoparticles ; Laser Material Processing Techniques
Authors: Patil, Sujata Anandrao
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Applications of AI in Synthesis of ZnO Thin Film Abstract Artificial Intelligence (AI) helps to synthesis and characterization of ZnO thin film using different types of synthesis processes such as Hydrothermal process, Chemical Bath Deposition process, Sol-gel process, Chemical Vapour Deposition process, etc. Artificial intelligence (AI) is used to analyze data from various experiments in the synthesis process of zinc oxide (ZnO) thin films, optimize the experiments, and find the best conditions for films with good properties. This uses AI to predict synthesis processes and improve properties based on experimental results, enabling accurate and efficient production. Thin film ; Characterization (materials science) ; Chemical vapor deposition ; Deposition (geology) ; Zinc ; Hydrothermal circulation ; Chemical bath deposition Machine Learning in Materials Science ; ZnO doping and properties ; Stock Market Forecasting Methods
Authors: Elmira Kulenova | Assel Botasheva | Ryskul Tursynbek | Gaziza Yelbergenova | Gulzhadra Tagay
Source: Springer Link (Chiba Institute of Technology) (article, 2025-12-01)
Field: Materials Science — Polymers and Plastics

Modeling for sustainable and eco-friendly digital textile manufacturing This study explores the role of artificial intelligence (AI) and mathematical modeling in enhancing sustainability and reducing the environmental footprint of digital textile manufacturing. Special attention is given to AI-driven multiscale modeling, geometric simulations, and mechanics-based fabric movement analysis, which optimize material efficiency and minimize waste. The research highlights how AI-powered virtual clothing design and digital textile production contribute to lower energy consumption, reduced water usage, and minimized chemical waste in the fashion industry. By integrating AI into sustainable manufacturing processes, the fashion industry can transition towards more responsible and resource-efficient production methods. This paper provides both theoretical insights and practical applications demonstrating AI's potential in achieving the Sustainable Development Goals (SDGs) in the textile sector. Sustainability ; Textile ; Clothing ; Ecological footprint ; Textile industry ; Production (economics) ; Footprint ; Carbon footprint ; Sustainable development Textile materials and evaluations ; Crafts, Textile, and Design ; Dyeing and Modifying Textile Fibers
Authors: Dinesh Kumar | Harsh Raj Singh | Rajni Tanwar | Vrinda Gupta
Source: Current Engineering Letters and Reviews (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Nanochemistry Innovations: Revolutionizing Healthcare, Energy, and Environmental Sustainability Introduction: Nanochemistry, the convergence of chemistry and nanotechnology, has emerged as a transformative field addressing pressing global challenges in healthcare, energy, environment, agriculture, and industry. This review explores recent innovations, applications, and ethical considerations that define the current and future trajectory of nanochemistry. Methods: A systematic literature search was conducted across PubMed, Scopus, ScienceDirect, and Google Scholar databases, focusing on studies published in recent years. Peer-reviewed articles highlighting nanomaterial synthesis, nanocatalysis, nanoelectronics, nanomedicine, energy applications, environmental remediation, and agriculture were analyzed. Emphasis was placed on works introducing novel methods, AI-guided material discovery, life-cycle assessments, and case studies relevant to sustainable and safe nanotechnology. Results: Nanochemistry has driven advances in multiple sectors. In medicine, it has enabled targeted drug delivery, advanced imaging, and antimicrobial solutions. In energy, nanostructured materials have improved battery efficiency and solar cell performance. Environmental applications include nanofiltration, photocatalysis, and sustainable nanoremediation strategies. Agriculture benefits from nano-fertilizers, pesticides, and biosensors for precision farming. Industrial domains are utilizing nanomaterials for textiles, packaging, and coatings. Despite progress, concerns regarding toxicity, bioaccumulation, and regulatory gaps remain significant. Discussion: Findings reveal nanochemistry’s potential to revolutionize technology while underscoring the need for responsible development. Gaps persist in large-scale synthesis, cost reduction, and comprehensive safety evaluation. Integration of artificial intelligence, bioinspired approaches, and circular economy models presents promising future directions. Conclusion: Nanochemistry is poised to reshape science and technology by offering sustainable, efficient, and interdisciplinary solutions to global challenges. Realizing its full potential requires continued research, international collaboration, robust governance, and ethical foresight to balance innovation with safety and societal acceptance. Sustainability ; Transformative learning ; Global challenges ; Sustainable development ; Circular economy ; Agriculture Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials ; Nanotechnology research and applications
Authors: Ndenga Lumbu Barack
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Towards a Unified Field Theory of Chemistry: Bridging Quantum, Organic, and Biochemical Reactions through a Single Formalism Bridging (networking) ; Formalism (music) ; Chemical reaction ; Chemical process ; Quantum chemical ; Unified field theory ; Quantum chemistry ; Reaction coordinate Various Chemistry Research Topics ; History and advancements in chemistry ; Chemistry and Chemical Engineering
Authors: Yuheng Zhen
Source: Applied and Computational Engineering (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

The Electronic Properties of Semiconductors and Their Related Applications The 21st century is characterized by its reliance on science and technology. The booming development of high-tech industries such as artificial intelligence, computers, smartphones, and new energy vehicles is inseparable from the development of microelectronics. Human beings are constantly researching ways to reduce the size of microchips, which according to Moore's Law, has profoundly impacted the progress of computing science and driven societal advancements. All of this scientific progress depends on semiconductor materials. Due to their unique electronic properties, semiconductor materials play a crucial role in contemporary technology. As society progresses rapidly, the electronic properties of semiconductors are becoming increasingly understood, and researchers worldwide are devoting more energy to their study. This paper mainly studies and analyzes the electronic properties of semiconductor materials and their main applications. This paper can provide relevant knowledge for those in the semiconductor industry. This paper finds that traditional semiconductor materials, relying on their unique electronic properties, can be applied to a variety of uses. Electronic properties mainly depend on the mobility of charge carriers, and the special p-n junction provides semiconductor materials with unique electronic properties. Semiconductor ; Electronic materials ; Semiconductor materials ; Electronics ; Energy (signal processing) ; Charge (physics) Chemical and Physical Properties of Materials ; Advanced Materials and Semiconductor Technologies ; Copper-based nanomaterials and applications
Authors: Shuangshuang Wang | Xiaoshuang Liu | Wei Feng | Xinyue Dai | Yu Chen
Source: Coordination Chemistry Reviews (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Pyroelectric and thermoelectric biomaterials and nanomedicine Pyroelectricity ; Thermoelectric effect ; Thermoelectric materials ; Nanomedicine ; Biomedicine ; Transformative learning Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Piezoelectric Materials
Authors: Murad Ali Khan | Hye-Jeong Song | Syed Naqvi | Muhammad Faseeh | Hyunseok Ko | Do‐Hyeun Kim
Source: Journal of Asian Ceramic Societies (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Toward efficient dielectric material discovery: a comparative evaluation of Transformer and AutoML-based inverse learning The discovery of dielectric material compositions and processing parameters often relies on trial-and-error experimentation, which is time-consuming, costly, and labor-intensive. To accelerate this process, artificial intelligence (AI) provides data-driven solutions that enhance predictive accuracy while minimizing experimental overhead. This study compares two AI methodologies: Transformer and AutoML-based Inverse Learning, for recommending compositions and processes of dielectric materials using data from the https://www.matcenter.org/Ceramic Materials Information Bank. Results show that while both methods perform reliably, AutoML consistently outperforms Transformer models across all metrics. AutoML achieved RÂ2 scores of 0.970 for component prediction and 0.978 for process prediction, with MAPE values of 1.4% and 1.1%, respectively. In contrast, Transformer models yielded lower RÂ2 scores (0.952 and 0.934) and higher MAPE values (over 2.0%). To enhance model interpretability, SHAP (SHapley Additive exPlanations) analysis was conducted, providing insights into feature contributions and supporting the reliability of predictions. These findings demonstrate AutoML superior ability to handle tabular data and its effectiveness in capturing complex material-process relationships. The study establishes AutoML as a robust framework for accelerating material discovery. It highlights the importance of selecting architectures that are suited to the data structure in AI-driven materials science. Transformer ; Inverse ; Dielectric ; Experimental data ; Artificial neural network ; Reliability (semiconductor) ; Feature (linguistics) Machine Learning in Materials Science ; Dielectric materials and actuators ; Material Selection and Properties