jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chengbo Li | Ying Wang | Qianying Wang | Zhizhi Tan | Haiqing Jia | Y. L. Liu | Li Qian | Nian Ran | Jianjun Liu | Zhixiong Zhang
Source: ACS Applied Materials & Interfaces (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Material Synthesis 2025 (MatSyn25) Data Set for 2D Materials Two-dimensional (2D) materials have shown broad application prospects in fields such as energy, environment, and aerospace owing to their unique electrical, mechanical, thermal, and other properties. With the development of artificial intelligence (AI), the discovery and design of novel 2D materials have been significantly accelerated. However, due to the lack of basic theories of material synthesis, identifying reliable synthesis processes for theoretically designed materials is a challenge. The emergence of large language model offers approaches for the reliability prediction of material synthesis processes. However, its development is limited by the lack of publicly available data sets of material synthesis processes. To address this, we present the Material Synthesis 2025 (MatSyn25), a large-scale open data set of 2D material synthesis processes. MatSyn25 contains 163,240 pieces of synthesis process information extracted from 85,160 high-quality research articles, each including basic material information and detailed synthesis process steps. Based on MatSyn25, we developed MatSyn AI, which specializes in material synthesis, and provided an interactive web platform that enables multifaceted exploration of the data set (https://matsynai.stpaper.cn/). MatSyn25 is publicly available, allowing the research community to build upon our work and further advance AI-assisted materials science. Process (computing) ; Set (abstract data type) ; Aerospace ; Reliability (semiconductor) ; Material Design ; Field (mathematics) ; Material properties ; Materials processing Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Ronnie Cerenio | Rica Angela Olbes | Jhon Lei Tarlino | Rommel D. Bautista | Arlene Gonzales | Jennelyn Raymundo
Source: Student Journal of Technology and Educational Research (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

USE OF ARTIFICIAL INTELLIGENCE IN READING-TO-WRITING TASKS: BASIS IN DESIGNING TRAINING MATERIALS FOR MACRO SKILLS INTEGRATION Macro ; Basis (linear algebra) ; Training (meteorology) ; Component (thermodynamics) ; Applications of artificial intelligence Material Selection and Properties ; Design Education and Practice ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Yohan | Irkham | Irwan Kurnia | Natasha Fransisca | Adisyahputra | Yeni Wahyuni Hartati
Source: Sensing and Bio-Sensing Research (article, 2026-01-31)
Field: Materials Science — Biomaterials

The role of artificial intelligence and the internet of things in chitosan synthesis and electrochemical sensors applications: A brief overview Over the past ten years, chitosan has attracted substantial scientific and industrial interest due to its natural abundance, biodegradability, and versatile functional properties. These attributes have enabled its rapid advancement across multiple sectors, including wastewater treatment, biomedical engineering, and sensor technology. Chitosan, a polysaccharide and polyelectrolyte polymer rich in amine and hydroxyl groups, offers vast potential for chemical modification. AI technologies have been utilized in chitosan synthesis for optimizing particle size, separation, coagulation, solubility, and sensor performance. Additionally, chitosan-based sensors have demonstrated efficacy in potentiometric and voltammetric applications. This review highlights the emerging roles of chitosan compound synthesis strategies for electrochemical sensors, advanced physicochemical characterization approaches, the functional role of chitosan in potentiometric and voltammetric sensing, and recent progress in integrating AI and IoT into both chitosan synthesis and chitosan-based sensor systems. A comparative analysis of recent references highlights gaps in the consistency of AI adoption and indicates a growing trend toward chitosan-based sensor development. This review provides an integrated perspective on how AI and IoT can accelerate innovation in chitosan synthesis and chitosan-based self-powered sensors, while outlining future opportunities for next-generation smart sensing and biosensing technologies. Chitosan ; Potentiometric titration ; Internet of Things ; Potentiometric sensor ; Polyelectrolyte ; Biosensor ; Chemical sensor Nanocomposite Films for Food Packaging ; Water Quality Monitoring and Analysis ; Biosensors and Analytical Detection
Authors: Jidong Hu | Yi Yang | Zuzhang Lin | Mingbin Gao | Ning Cao | Juejun Wang | Huacheng Sun | Jingshu Wu | Yinyin Shi | Shenglun Xiong | Jieyao Lv | Jie Bai | Wenjing HONG
Source: Science China Chemistry (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

Cross-scale transfer from single-molecule intermolecular interactions to viscosity of polymers Intermolecular force ; Supramolecular chemistry ; Polymer ; Rheology ; Viscosity ; Intrinsic viscosity ; Supramolecular polymers Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Block Copolymer Self-Assembly
Authors: Jennifer D'Souza | Soren Auer | Eleni Poupaki | Alex Watkins | Anjana Devi | Riikka L. Puurunen | Bora Karasulu | Adrie Mackus | Erwin Kessels
Source: TU/e Research Portal (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence Scientific reviews are central to knowledge integration in materials science, yet their key insights remain locked in narrative text and static PDF tables, limiting reuse by humans and machines alike. This article presents a case study in atomic layer deposition and etching (ALD/E) where we publish review tables as FAIR, machine-actionable comparisons in the Open Research Knowledge Graph (ORKG), turning them into structured, queryable knowledge. Building on this, we contrast symbolic querying over ORKG with large language model-based querying, and argue that a curated symbolic layer should remain the backbone of reliable neurosymbolic AI in materials science, with LLMs serving as complementary, symbolically grounded interfaces rather than standalone sources of truth. Reuse ; Publishing ; Narrative ; Key (lock) ; Limiting ; Layer (electronics) ; Applications of artificial intelligence ; Sociology of scientific knowledge ; Graph Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Research Data Management Practices
Authors: Pengyu Li | Jiafu Wu | Jing Liu | Jianzhun Jiang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Organic Chemistry

Internal Donors: The Molecular Keys in Ziegler-Natta Catalysts — From Functions and Design to Future Perspectives Sustainability ; Rational design ; Key (lock) ; TRACE (psycholinguistics) ; Foundation (evidence) ; Polyolefin Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; biodegradable polymer synthesis and properties
Authors: Jianghao Cai | Yutian Zhuang | Kang Ma | Yutong Jiang | Xuao Lu | Zhijun Gao | Weijie Yang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery of Dynamic Thermodynamics for Dehydrogenation in Magnesium-Based Hydrogen Storage Materials Enthalpy ; Dehydrogenation ; Work (physics) ; Standard enthalpy of reaction ; Plateau (mathematics) ; Thermal energy storage ; Kinetic energy ; Hydrogen storage Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Magnesium Alloys: Properties and Applications
Authors: Mengmeng Qin | Zhenyu Zhang | Yuliang Zhao | Huan Meng
Source: Accounts of Chemical Research (article, 2026-03-10)
Field: Materials Science — Biomaterials

Enhanced Transcytosis and Retention (ETR) of Drug Delivery Nanocarrier in Solid Tumors ConspectusTranscytosis, traditionally regarded as biological, constitutes an intrinsic and powerful pathway for macromolecule transport across endothelial and epithelial barriers. The emerging concept of Enhanced Transcytosis and Retention (ETR) is distinct from passive extravasation or tissue leakiness. It recasts nanocarrier delivery as an orchestrated chain of interfacial equilibria in which encoded surface chemistry directs receptor recognition, active barrier crossing, and subsequent accumulation within target tissues. Here, we delineate the chemical framework underpinning ETR-mediated delivery, emphasizing that the chemical identity of a nanocarrier, i.e., its surface functional groups, coordination motifs, hydration shell, reactive ligands, surface free energy, and biocorona, dictates a hierarchical sequence of interactions. To enable ETR access, we propose a triadic interaction model among the nanocarrier, an endogenous or engineered protein-based material, and a specific cellular receptor. This architecture represents a fundamental shift from conventional two-entity protein-adsorption frameworks, converting inherently stochastic protein deposition into a chemically programmable, design-driven active transport process. At the inner interface (between nanocarrier and protein material), surface functional groups, roughness, and topology determine the composition, orientation, and reactivity of a given biomolecule, such as endogenous or engineered proteins. At the outer interface (protein-cell receptor), these nanocarrier-protein complexes engage cell receptors through amino acid sequence-specific molecular recognition, topological complementarity, hydrogen-bond cooperativity, and electrostatic complementarity that collectively trigger ETR active access. Such an ETR framework, first exemplified in solid tumors (e.g., pancreatic cancer and triple negative breast cancer), now extends to diverse pathological contexts including the blood-brain barrier and dystrophic muscle. By viewing ETR drug delivery through a chemical lens, this manuscript integrates structure-reactivity principles with biological transport, providing a molecularly actionable framework for otherwise inaccessible tissues. Noteworthy, artificial intelligence (AI) guided protein engineering, using strategies such as point mutagenesis and noncanonical amino-acid substitution, will enable the creation of highly optimized and artificial ligands that assemble ETR-activating units with molecular-level precision. Nanocarriers ; Transcytosis ; Drug delivery ; Chemical biology ; Nanomedicine ; Extravasation ; Targeted drug delivery ; Receptor ; Cell Nanoparticle-Based Drug Delivery ; Cell Adhesion Molecules Research ; Polymer Surface Interaction Studies
Authors: Amir Gizatullin | Tingting Yuan | Sascha Grotjahn | Luigi Cavallo | Burkhard König | Chen Zhu | Magnus Rueping
Source: University of Regensburg Publication Server (University of Regensburg) (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Predictive, Data-Driven Design of Red-Light Photoredox Catalysts for C─Heteroatom Bond Formation Photocatalysis is a powerful tool for the synthesis of organic molecules, yet its widespread application is hindered by the dependence on high-energy light sources and expensive metal-based catalysts, which can limit scalability and environmental sustainability. In this study, we present a modular design strategy for organic dyes engineered for efficient red-light absorption, enabling photocatalytic reactions under low-energy irradiation. Our findings establish a clear relationship between the oxidation potential of the photocatalyst and the nature of its donor moiety, as well as between the reduction potential and the electronic characteristics of its core structure. Moreover, we demonstrate that the E0-0 energy of a photocatalyst can be predicted via multivariate linear regression using the donor's oxidation potential and the core's reduction potential as descriptors. Utilizing this strategy, we synthesized red-light-absorbing photocatalysts that efficiently promote C─heteroatom cross-coupling reactions under mild conditions. This approach overcomes the limitations of blue-light photocatalysis by offering broad substrate compatibility, including π-conjugated aryl bromides and photolabile functional groups, while minimizing undesirable hydrodehalogenation. By reducing reliance on precious metals and improving energy efficiency, our approach provides a scalable alternative to traditional photocatalysis and advances the development of metal-free photocatalysts for sustainable chemistry. Photocatalysis ; Catalysis ; Organic synthesis ; Modular design ; Substrate (aquarium) ; Scalability ; Photoredox catalysis Radical Photochemical Reactions ; Advanced Photocatalysis Techniques ; Catalytic Cross-Coupling Reactions
Authors: Amir Gizatullin | Tingting Yuan | Sascha Grotjahn | Luigi Cavallo | Burkhard König | Chen Zhu | Magnus Rueping
Source: Angewandte Chemie International Edition (article, 2026-01-19)
Field: Chemistry — Organic Chemistry

Predictive, Data‐Driven Design of Red‐Light Photoredox Catalysts for C─Heteroatom Bond Formation Photocatalysis is a powerful tool for the synthesis of organic molecules, yet its widespread application is hindered by the dependence on high-energy light sources and expensive metal-based catalysts, which can limit scalability and environmental sustainability. In this study, we present a modular design strategy for organic dyes engineered for efficient red-light absorption, enabling photocatalytic reactions under low-energy irradiation. Our findings establish a clear relationship between the oxidation potential of the photocatalyst and the nature of its donor moiety, as well as between the reduction potential and the electronic characteristics of its core structure. Moreover, we demonstrate that the E<sub>0-0</sub> energy of a photocatalyst can be predicted via multivariate linear regression using the donor's oxidation potential and the core's reduction potential as descriptors. Utilizing this strategy, we synthesized red-light-absorbing photocatalysts that efficiently promote C─heteroatom cross-coupling reactions under mild conditions. This approach overcomes the limitations of blue-light photocatalysis by offering broad substrate compatibility, including π-conjugated aryl bromides and photolabile functional groups, while minimizing undesirable hydrodehalogenation. By reducing reliance on precious metals and improving energy efficiency, our approach provides a scalable alternative to traditional photocatalysis and advances the development of metal-free photocatalysts for sustainable chemistry. Photocatalysis ; Catalysis ; Organic synthesis ; Modular design ; Substrate (aquarium) ; Scalability ; Photoredox catalysis Radical Photochemical Reactions ; Catalytic Cross-Coupling Reactions ; Advanced Photocatalysis Techniques
Authors: Haotong Liang | Yunlong Sun | Ryan Paxson | Chih‐Yu Lee | Alex T. Hall | Zoey Warecki | John Cumings | Hideomi Koinuma Hideomi Koinuma | A. Gilad Kusne | Mikk Lippmaa | Ichiro Takeuchi
Source: Open MIND (preprint, 2026-02-24)
Field: Materials Science — Materials Chemistry

Autonomous epitaxial atomic-layer synthesis via real-time computer vision of electron diffraction Autonomous science platforms which make decisions on the fly are fundamentally changing the outlook for materials development. AI-driven schemes can effectively reduce the total number of iterations needed to arrive at the best stoichiometry for desired properties or optimum synthesis parameters by significant margins. Here, we demonstrate real-time closed-loop autonomous navigation of a multi-dimensional synthesis parameter space for fabricating phase-pure epitaxial films of a metastable functional oxide phase using pulsed laser deposition. Sequential growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision of electron diffraction images at the unit-cell level. Our scheme regularly resulted in &gt; 30-fold reduction in the number of experiments compared to comprehensive parameter-space mapping. The real-time workflow developed here can be readily extended to other thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous semiconductor manufacturing. Workflow ; Thin film ; Epitaxy ; Electron diffraction ; Setpoint ; Reflection (computer programming) ; Deposition (geology) ; Reduction (mathematics) ; Phase (matter) Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Amir Gizatullin | Tingting Yuan | Sascha Grotjahn | Luigi Cavallo | Burkhard König | Chen Zhu | Magnus Rueping
Source: Angewandte Chemie (article, 2026-01-19)
Field: Chemistry — Organic Chemistry

Predictive, Data‐Driven Design of Red‐Light Photoredox Catalysts for C─Heteroatom Bond Formation Abstract Photocatalysis is a powerful tool for the synthesis of organic molecules, yet its widespread application is hindered by the dependence on high‐energy light sources and expensive metal‐based catalysts, which can limit scalability and environmental sustainability. In this study, we present a modular design strategy for organic dyes engineered for efficient red‐light absorption, enabling photocatalytic reactions under low‐energy irradiation. Our findings establish a clear relationship between the oxidation potential of the photocatalyst and the nature of its donor moiety, as well as between the reduction potential and the electronic characteristics of its core structure. Moreover, we demonstrate that the E 0‐0 energy of a photocatalyst can be predicted via multivariate linear regression using the donor's oxidation potential and the core's reduction potential as descriptors. Utilizing this strategy, we synthesized red‐light‐absorbing photocatalysts that efficiently promote C─heteroatom cross‐coupling reactions under mild conditions. This approach overcomes the limitations of blue‐light photocatalysis by offering broad substrate compatibility, including π‐conjugated aryl bromides and photolabile functional groups, while minimizing undesirable hydrodehalogenation. By reducing reliance on precious metals and improving energy efficiency, our approach provides a scalable alternative to traditional photocatalysis and advances the development of metal‐free photocatalysts for sustainable chemistry. Photocatalysis ; Catalysis ; Organic synthesis ; Substrate (aquarium) ; Modular design ; Photoredox catalysis ; Scalability Radical Photochemical Reactions ; Catalytic Cross-Coupling Reactions ; Advanced Photocatalysis Techniques
Authors: Induja Mishra | Pashupati Nath | Dr. Ajay Pratap Singh | Vinay Kumar | Sameer Chandra | Nilesh Pandey | S. R. Swarna
Source: Indian Journal of Heterocyclic Chemistry (article, 2026-03-01)
Field: Chemistry — Organic Chemistry

Advances in heterocyclic chemistry for high-performance energy materials: A review of green synthesis and functional applications Heterocyclic materials have developed essential structural building blocks for the future generation of sustainable energy devices owing to their basic adaptability and customizable electronic architectures. The combination of heterocyclic materials with modern energy technologies, including sophisticated metal-ion (Li/Na) batteries, fuel cells, hydrogen storage systems, and perovskite solar cells, is systematically observed in this review. This paper highlights the distinctive structure-property correlations of p-conjugated and redox-active heterocycles, predominantly quinones, azoles, pyrroles, and porphyrins in addition to their function in enlightening thermal stability and charge carrier dynamics. Moreover, this work highlights the shift to 'Green-by-Design' approaches, underlining the utilization of biocatalysis, mechanochemical paths, and deep eutectic solvents to diminish the environmental E-factor. Developing 2026 research areas, such as wasteto-energy valorization of furans derived from biomass and artificial intelligence-driven retrosynthetic optimization, are specified a lot of consideration. This assessment offers an approach for fluctuating the global energy environment toward sustainable and uncontaminated systems by assimilating heterocyclic engineering with the circular economy's beliefs. . KEYWORDS :Sustainable heterocyclic chemistry, Green synthesis and E-factor, Structure-property relationships, Redox-active materials, p-conjugated systems, Energy storage and conversion, Perovskite and organic photovoltaics, Waste-to-energy valorization, Artificial intelligence-driven reaction optimization. Eutectic system ; Sustainable energy ; Perovskite (structure) ; Adaptability ; Work (physics) ; Energy transformation ; Energy storage ; Solar energy ; Energy carrier Synthesis and Characterization of Pyrroles ; Catalysis for Biomass Conversion ; Covalent Organic Framework Applications
Authors: Yusuf Uzunoğlu | Yusuf Alaca | Özgür Özdilli | Seyfi Şevik
Source: Applied Thermal Engineering (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Development and simulation analysis of a novel aluminum alloy for heat sink applications using explainable artificial intelligence and the Calphad method Alloy ; Aluminium ; CALPHAD ; Heat sink ; Development (topology) Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Glen R. Jones | Masami Kamigaito | James L. Grace | Greg G. Qiao | Cyrille Boyer | Joji Tanaka | Wei You | Jian Zhu | Vianna F. Jafari | Athina Anastasaki | Jiajia Li
Source: UNC Libraries (article, 2026-02-24)
Field: Chemistry — Organic Chemistry

Beyond Traditional RAFT Polymerization: Emerging Strategies and Future Perspectives; A Third Update Reversible addition-fragmentation chain transfer (RAFT) polymerization has undergone transformative growth since its inception in 1998, emerging as a powerful and versatile tool for precision polymer synthesis. This review highlights the latest developments in non-traditional RAFT polymerization from 2020 to 2025, capturing major innovations in activation techniques and expanding applications. Key emerging directions include the integration of RAFT into smart synthesis platforms powered by artificial intelligence, enabling high-throughput and autonomous polymer discovery, and innovations in RAFT depolymerization that support sustainable plastic recycling. With RAFT increasingly accessible to diverse materials science domains, this review provides a forward-looking perspective on the evolving capabilities and future potential of RAFT polymerization. Raft ; Transformative learning ; Reversible addition−fragmentation chain-transfer polymerization ; Perspective (graphical) ; Emerging technologies Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Synthetic Organic Chemistry Methods