jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nitish Raghavan | Nivedidha Kumaravelu
Source: Open MIND (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Hybrid AI Knowledge Platform for Battery Manufacturing Optimization Abstract Battery research has historically prioritized material discovery and electrochemical characterization, often overlooking the critical manufacturing parameters that determine commercial viability and cell-level performance. Variables such as electrode mass loading, porosity, slurry rheology, negative-to-positive (N/P) ratio, and formation protocols are rarely reported in academic literature, yet they govern the scalability of battery chemistries in gigafactory environments. This knowledge gap creates a persistent disconnect between laboratory-scale materials research and industrial cell production, leading to costly failures during scale-up and prolonged development cycles. Here we present CellIntel, a hybrid artificial intelligence-driven industrial knowledge platform that transforms unstructured scientific literature into structured, manufacturing-aware "cell recipes." By combining deterministic chemical entity extraction with generative reasoning and first-principles physical modeling, CellIntel infers the missing industrial parameters necessary to translate material research into manufacturable cell designs. The platform integrates techno-economic analysis and a live digital twin interface, enabling engineers to benchmark synthesized designs against commercial cells such as Tesla 4680 and Panasonic 2170. Across 1,500 documents spanning lithium iron phosphate (LFP) and nickel-manganese-cobalt (NMC) systems, CellIntel achieves energy density predictions with 6.3% mean absolute percentage error and successfully reconstructs complete manufacturing recipes from papers reporting only material properties. This work demonstrates how AI grounded in physical constraints can bridge the persistent gap between academic battery discovery and industrial cell production, potentially reducing development cycles from years to months. Battery (electricity) ; Scalability ; Bridge (graph theory) ; Benchmark (surveying) ; Knowledge extraction ; Generative grammar ; Lithium battery ; Slurry Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Nitish Raghavan | Nivedidha Kumaravelu
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Bridging the Gap from Material to Cell Abstract Battery research has historically prioritized material discovery and electrochemical characterization, often overlooking the critical manufacturing parameters that determine commercial viability and cell-level performance. Variables such as electrode mass loading, porosity, slurry rheology, negative-to-positive (N/P) ratio, and formation protocols are rarely reported in academic literature, yet they govern the scalability of battery chemistries in gigafactory environments. This knowledge gap creates a persistent disconnect between laboratory-scale materials research and industrial cell production, leading to costly failures during scale-up and prolonged development cycles. Here we present CellIntel, a hybrid artificial intelligence-driven industrial knowledge platform that transforms unstructured scientific literature into structured, manufacturing-aware "cell recipes." By combining deterministic chemical entity extraction with generative reasoning and first-principles physical modeling, CellIntel infers the missing industrial parameters necessary to translate material research into manufacturable cell designs. The platform integrates techno-economic analysis and a live digital twin interface, enabling engineers to benchmark synthesized designs against commercial cells such as Tesla 4680 and Panasonic 2170. Across 1,500 documents spanning lithium iron phosphate (LFP) and nickel-manganese-cobalt (NMC) systems, CellIntel achieves energy density predictions with 6.3% mean absolute percentage error and successfully reconstructs complete manufacturing recipes from papers reporting only material properties. This work demonstrates how AI grounded in physical constraints can bridge the persistent gap between academic battery discovery and industrial cell production, potentially reducing development cycles from years to months. Bridging (networking) ; Scalability ; Battery (electricity) ; Bridge (graph theory) ; Generative grammar ; Benchmark (surveying) ; Slurry Machine Learning in Materials Science ; Advancements in Battery Materials ; Extraction and Separation Processes
Authors: Pengxian Hui | Haohao Ji | Ye Ge | Jin Zhao | Wenlong Liu | Shiwei Wang | D. Liu | Jian ZHANG
Source: Materials & Design (article, 2026-04-01)
Field: Materials Science — Ceramics and Composites

Material extrusion of low-thermal-expansion cordierite ceramics: Process optimization and performance evaluation by data-driven technique Extrusion ; Cordierite ; Process optimization ; Process (computing) ; Manufacturing process Advanced ceramic materials synthesis ; Machine Learning in Materials Science ; Advanced materials and composites
Authors: Priyam Ghosh | Parameswar Krishnan Iyer
Source: ACS Applied Bio Materials (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Design and Photophysical Engineering of Functional Organic Luminogens for Precision Cancer Theranostics Cancer remains a significant global health challenge, necessitating innovative therapeutic strategies. Functional organic luminogens have emerged as a versatile class of biomaterials for cancer theranostics, enabling the integration of diagnostic imaging and therapeutic intervention within a single molecular or supramolecular platform. In this perspective, recent advances in the rational design of these luminogens as next-generation cancer theranostics were discussed. Particular emphasis is placed on emerging organic luminogen systems, including aggregation-induced emission (AIE) small molecules, thermally activated delayed fluorescence (TADF)-based probes, polymer-based nanostructures, organic co-crystals, and charge-transfer (CT) assemblies, with an emphasis on their structure-property-function relationships. Unlike conventional nanoparticle systems, small-molecule luminogens have defined structures, improved biocompatibility, and faster clearance rates, enabling deeper tumor penetration and reduced long-term toxicity. Key molecular design strategies that regulate excited-state dynamics, aggregation behavior, charge transfer, and microenvironment responsiveness are discussed in the context of near-infrared (NIR) and NIR-II imaging, photodynamic and photothermal therapy, and synergistic multimodal treatments. Finally, challenges related to specificity, biosafety, and translational implementation are outlined, while emerging opportunities like data driven molecular discovery and artificial intelligence-assisted discovery are highlighted as future directions for the development of organic luminogens-based biomaterials in precision cancer theranostics. Context (archaeology) ; Photothermal therapy ; Rational design ; Cancer ; Molecular engineering ; Supramolecular chemistry ; Photodynamic therapy ; Cancer therapy Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Supramolecular Chemistry and Complexes
Authors: Kassem Hallal | Rasha Hamdan | Sami Tlais
Source: Journal of Chemical Education (article, 2026-01-19)
Field: Chemistry — Physical and Theoretical Chemistry

Beyond Traditional Texts: Exploring AI as a Tool for Generating Stories and Analogies in Organic Chemistry Education Organic chemistry is widely regarded as a challenging subject, especially for nonchemistry majors who often find it abstract and disconnected from their academic or career interests. This explanatory study investigates the integration of artificial intelligence (AI)-generated stories and analogies─developed by the course instructor─as pedagogical tools to improve student motivation and engagement. A cohort of 52 undergraduate students majoring in pharmacy and biochemistry participated in a semester-long intervention involving AI-generated narrative preclass readings, in-class explanatory activities, and take-home storytelling assignments. Materials were tailored using ChatGPT to reflect students’ disciplinary backgrounds. Student feedback was collected through surveys, concept quizzes, and in-class observations. Student self-reports indicated that AI-generated stories were perceived as more effective than traditional instructional explanations, especially with respect to clarity, enjoyment, relevance, and memorability. Students expressed a clear preference for narrative and analogy-based formats, and many adopted these approaches in their own explanations during discussions. In-class use of AI─by both the instructor and students─enabled real-time clarification of misconceptions and supported peer-generated analogies, fostering active participation and critical thinking. In the take-home assignment on free radical halogenation, students created personalized AI-assisted stories using tailored prompts; nearly all included the full mechanistic sequence, although performance on individual substeps was more moderate, while clarity and coherence were generally strong. These findings offer preliminary evidence supporting the potential pedagogical value of AI-assisted storytelling in chemistry education, given the study’s reliance on a modest sample size and self-reported student perceptions. By enabling differentiated instruction, enhancing engagement, and making abstract content more accessible, tools like ChatGPT can serve as a powerful complement to traditional instructional strategies, especially in classrooms with diverse learners. CLARITY ; Storytelling ; Narrative ; Chemistry education ; Coherence (philosophical gambling strategy) ; Preference ; Higher education Various Chemistry Research Topics ; Machine Learning in Materials Science ; Innovative Teaching Methodologies in Social Sciences
Authors: Agung Rahmadani | Mukhamad Nurhadi | Teguh Wirawan | Wirhanuddin Wirhanuddin | Nabila Nur Agusti | Sin Yuan Lai | Hadi Nur
Source: BULLETIN OF CHEMICAL REACTION ENGINEERING AND CATALYSIS (article, 2026-01-07)
Field: Chemistry — Organic Chemistry

Catalytic Performance of Environmentally Friendly Calcium Sulfate Hemihydrate-supported Metals (Ti, Fe, Cu or Ag) for Oxidation Styrene to Benzaldehyde This paper presents the synthesis and characterization of calcium sulfate hemihydrate (CSH)-supported Metals (Ti, Fe, Cu or Ag) catalysts and their application in the styrene oxidation to benzaldehyde using hydrogen peroxide (H2O2) as an oxidant. The study explores the catalyst's structure-activity relationship, emphasizing the importance of mesoporous materials for enhanced catalytic performance. The CSH-Metals catalysts were synthesized using fish bone-derived CSH as a support, which aligns with green chemistry principles. Characterization techniques, such as FTIR, XRD, SEM, and BET surface area analysis, confirmed the successful impregnation of Metals (Ti, Fe, Cu or Ag) and its catalytic performance. The catalysts exhibited styrene conversion and high selectivity for benzaldehyde, achieving up to 49.5% and 60.2% for CSH-Ti; 12.9% and 84.1% for CSH-Fe, 19.9% and 61.5% for CSH-Cu, and 13.4% and 92.8% for CSH-Ag. The research highlights that the best catalyst’s performance are CSH-Ti for styrene conversion and CSH-Ag for benzaldehyde selectivity. To support performance interpretation, a fuzzy logic analysis was applied to evaluate the influence of seven key parameters on catalytic behavior. The results revealed that ROS (Reactive Oxygen Species) formation activity, type of metal, and metal–intermediate interaction were the most dominant factors affecting performance. This data-driven insight reinforces the chemical reactivity as the primary determinant of catalyst effectiveness, above physical attributes such as surface area or pore structure. Overall, this study introduces a cost-effective, sustainable, and selective catalyst system for styrene oxidation, demonstrating high potential for industrial application in the production of value-added chemicals with minimal environmental impact. Copyright © 2026 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0). Benzaldehyde ; Styrene ; Catalysis ; Reactivity (psychology) ; Hydrogen peroxide ; Mesoporous material ; Selectivity Chemical Synthesis and Reactions ; Mesoporous Materials and Catalysis ; Oxidative Organic Chemistry Reactions
Authors: Elizabeth S. Thrall | Olivia M. Vanden Assem | Julia Schneider | Joshua Schrier | Sebastian Tassoti
Source: Journal of Chemical Education (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Can You Help ChatGPT Get an “A” in Organic Chemistry? Teaching Effective Prompting of Large Language Models for Reaction Prediction As generative artificial intelligence (AI) tools such as large language models (LLMs) become widespread, they are increasingly finding applications in chemical sciences. Although LLMs have achieved impressive performance in many chemistry tasks, optimal performance requires proper use, including appropriate prompting techniques. Chemistry students are not generally taught strategies for effective LLM usage, especially for nonwriting tasks. Here we report an activity that introduces organic chemistry students to the use of LLMs such as ChatGPT for predicting the outcome of chemical reactions, specifically the types of alkene addition reactions taught in introductory organic chemistry courses. This activity exposes students to molecular representations, digitization of chemical reactions, train-test splitting practices for evaluating performance, and generalizable LLM prompting strategies, namely, the Five “S” prompt-writing approach and in-context learning. We tested this activity with chemistry students in the USA and in Austria and evaluated the activity through anonymous pre- and postlab surveys. Survey data revealed that students felt that they achieved their learning goals and that the activity was enjoyable. As chemistry students will inevitably interact with LLMs in their future careers, it is important to teach best practices for the effective and critical use of these tools in the context of chemistry. Context (archaeology) ; Chemistry education ; Science education ; Generative model ; Teaching method ; Active learning (machine learning) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Explainable Artificial Intelligence (XAI)
Authors: Wei Yang | Zihao Liu | Tao Tan | Xiao Hu | Hong Xie | Lulu Li Xin Li | Jianyu Han | Defu Lian | Mao Ye
Source: Open MIND (preprint, 2026-02-10)
Field: Materials Science — Materials Chemistry

AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature This paper presents a large language model (LLM) agent named AgentCAT, which extracts and analyzes catalytic reaction data from chemical engineering papers, %and supports natural language based interactive analysis of the extracted data. AgentCAT serves as an alternative to overcome the long-standing data bottleneck in chemical engineering field, and its natural language based interactive data analysis functionality is friendly to the community. AgentCAT also presents a formal abstraction and challenge analysis of the catalytic reaction data extraction task in an artificial intelligence-friendly manner. This abstraction would help the artificial intelligence community understand this problem and in turn would attract more attention to address it. Technically, the complex catalytic process leads to complicated dependency structure in catalytic reaction data with respect to elementary reaction steps, molecular behaviors, measurement evidence, etc. This dependency structure makes it challenging to guarantee the correctness and completeness of data extraction, as well as representing them for analysis. AgentCAT addresses this challenge and it makes four folds of technical contributions: (1) a schema-governed extraction pipeline with progressive schema evolution, enabling robust data extraction from chemical engineering papers; (2) a dependency-aware reaction-network knowledge graph that links catalysts/active sites, synthesis-derived descriptors, mechanistic claims with evidence, and macroscopic outcomes, preserving process coupling and traceability; (3) a general querying module that supports natural-language exploration and visualization over the constructed graph for cross-paper analysis; (4) an evaluation on $\sim$800 peer-reviewed chemical engineering publications demonstrating the effectiveness of AgentCAT. Correctness ; Abstraction ; Visualization ; Bottleneck ; Knowledge extraction ; Dependency graph ; Data visualization ; Fluid catalytic cracking ; Data structure Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Catalysts for Methane Reforming
Authors: Wei Yang | Zihao Liu | Tao Tan | Xiao Hu | Hong Xie | Lulu Li Xin Li | Jianyu Han | Defu Lian | Mao Ye
Source: arXiv (Cornell University) (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature This paper presents a large language model (LLM) agent named AgentCAT, which extracts and analyzes catalytic reaction data from chemical engineering papers, %and supports natural language based interactive analysis of the extracted data. AgentCAT serves as an alternative to overcome the long-standing data bottleneck in chemical engineering field, and its natural language based interactive data analysis functionality is friendly to the community. AgentCAT also presents a formal abstraction and challenge analysis of the catalytic reaction data extraction task in an artificial intelligence-friendly manner. This abstraction would help the artificial intelligence community understand this problem and in turn would attract more attention to address it. Technically, the complex catalytic process leads to complicated dependency structure in catalytic reaction data with respect to elementary reaction steps, molecular behaviors, measurement evidence, etc. This dependency structure makes it challenging to guarantee the correctness and completeness of data extraction, as well as representing them for analysis. AgentCAT addresses this challenge and it makes four folds of technical contributions: (1) a schema-governed extraction pipeline with progressive schema evolution, enabling robust data extraction from chemical engineering papers; (2) a dependency-aware reaction-network knowledge graph that links catalysts/active sites, synthesis-derived descriptors, mechanistic claims with evidence, and macroscopic outcomes, preserving process coupling and traceability; (3) a general querying module that supports natural-language exploration and visualization over the constructed graph for cross-paper analysis; (4) an evaluation on $\sim$800 peer-reviewed chemical engineering publications demonstrating the effectiveness of AgentCAT. Correctness ; Abstraction ; Visualization ; Bottleneck ; Knowledge extraction ; Dependency graph ; Data visualization ; Fluid catalytic cracking ; Data structure Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Catalysts for Methane Reforming
Authors: Hammad Majeed | Fehar Ahmed | Tehreema Iftikhar | Umair Iftikhar
Source: Toxicology Research (article, 2026-03-16)
Field: Materials Science — Biomaterials

AI based next generation industry 6.0 and industry 7.0 nanotechnology in pharmaceutical sectors with toxicological challenges, safety concerns, sustainable perspectives and strategies to overcome these barriers Abstract Nanoparticle drug delivery systems are reshaping pharmaceutical development by enabling programmable control over biodistribution, cellular uptake &amp; release kinetics beyond the limits of conventional formulations. Advances in lipid, polymeric, inorganic and hybrid nanocarriers now support delivery of small molecules, biologics, nucleic acids and gene editing cargos across oncology, cardiometabolic, infectious and neurodegenerative disease domains. Materials level engineering, spanning interfacial chemistry, supramolecular assembly and stimuli responsive matrices has enabled improved solubilization of poorly bioavailable drugs, enhanced tissue targeting and reduced systemic toxicity. Despite these therapeutic advantages, translation from laboratory systems to commercial pharmaceutical products remains constrained by manufacturing reproducibility, sterilization compatibility, endotoxin control, long term stability and regulatory uncertainty regarding nano specific safety endpoints. Industrial implementation therefore requires tight control of critical quality attributes, integration of continuous manufacturing routes and adoption of predictive modelling tools for formulation design and lifecycle risk assessment. Emerging Industry 6.0 aligned technologies including AI assisted formulation optimization, digital twin process modelling and real time process analytical technologies are beginning to transform nanomedicine from a formulation challenge into a data driven manufacturing discipline. In parallel, safe by design materials engineering and integrative nanotoxicology frameworks are improving regulatory confidence by linking physicochemical properties to biodistribution, clearance and immunological responses. These developments indicate a transition from first generation nanocarriers toward intelligent, scalable pharmaceutical systems capable of supporting precision therapeutics. The convergence of advanced materials chemistry, automated smart/ intelligent manufacturing and predictive safety science is expected to accelerate clinical translation, reduce development risk, and establish nanomedicine as a core platform across the future pharmaceutical landscape. Nanocarriers ; Pharmaceutical industry ; Nanomedicine ; Critical quality attributes ; Pharmaceutical manufacturing ; Quality by Design ; Process (computing) ; Emerging technologies Nanoparticle-Based Drug Delivery ; Inhalation and Respiratory Drug Delivery ; 3D Printing in Biomedical Research
Authors: Karishma Tiwari | Ashish Kumar Singh | Zhang‐Hui Lu
Source: Coordination Chemistry Reviews (article, 2026-03-19)
Field: Chemistry — Inorganic Chemistry

Exploiting single-crystal-to-single-crystal transformations in dynamic MOFs for advanced applications Rational design ; Characterization (materials science) ; Molecular dynamics ; Transformation (genetics) ; Perspective (graphical) ; Density functional theory Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis