jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziyang Gong | Chengwei Zhang | Danyang Song | Wei Xia | Bin Shen | Wei Ke Su | Hongliang Duan | An Su
Source: Chemical Communications (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

ChemReactSeek: an artificial intelligence-guided chemical reaction protocol design using retrieval-augmented large language models We introduce ChemReactSeek, an advanced artificial intelligence platform that integrates retrieval-augmented generation using large language models (LLMs) to automate the design of chemical reaction protocols. The system employs DeepSeek-v3 to extract and structure data from scientific literature, enabling the construction of a specialized knowledge base focused on hydrogenation reactions. By combining FAISS-based semantic search with LLM-driven reasoning, ChemReactSeek generates executable reaction conditions, which we further validate through experiments on heterogeneous hydrogenation. Protocol (science) ; Computer science ; Artificial intelligence ; Natural language processing ; Chemistry ; Combinatorial chemistry Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Chemical Synthesis and Analysis
Authors: Jiayi Zhu | Lihong Lin | H. Niu | Qiaoyan Shang | Qiang Wang | Fangyou Yan | Jin‐Jin Li
Source: Industrial & Engineering Chemistry Research (article, 2025-04-04)
Field: Materials Science — Polymers and Plastics

Data-Driven Prediction of Flory–Huggins Parameter for Quantifying Polymer–Solvent Interaction The Flory–Huggins interaction parameter is an influential thermodynamic parameter for quantifying polymer–solvent interaction. The data-driven quantitative structure–property relationship (QSPR) model provides a rapid and accurate route to obtain the Flory–Huggins parameter of polymer–solvent mixtures. To evaluate the correlation between the polymer–solvent structure and the Flory–Huggins parameter, we developed a QSPR model based on 29 norm descriptors. To overcome the diversity of structural representations of a polymer, a ring repeating unit is adopted for unique structural representation of the polymers. Considering the effects of polymers, solvents, and polymer–solvent interactions on the Flory–Huggins parameters, the descriptors are divided into three parts. The results of statistical parameter analysis indicate that the QSPR model exhibits good prediction performance (R2test = 0.9348, AAEtest = 0.1943) and robustness (Q2LOO-CV = 0.9177). This work offers a quantitative reference and a tool to understand polymer–solvent interactions. Flory–Huggins solution theory ; Solvent ; Polymer ; Thermodynamics ; Materials science ; Chemistry ; Organic chemistry ; Physics Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Polymer composites and self-healing
Authors: Aayush Trivedi | Kushagra Tiwari | Jalaj Kumar | Mehdi Salem | Alankar Alankar
Source: International Journal of Fatigue (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

A crystal plasticity and data-driven approach for analyzing the effect of porosity and surface roughness on fatigue behavior Porosity ; Surface roughness ; Crystal plasticity ; Plasticity ; Surface finish ; Surface (topology) Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Machine Learning in Materials Science
Authors: Cheng Yang | Thérèse Wild | Yulia Rakova | Stephen Maldonado | Matthew S. Sigman | Corey R. J. Stephenson
Source: ACS Central Science (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Data-Driven Workflow for the Development and Discovery of <i>N</i> -Oxyl Hydrogen Atom Transfer Catalysts <i>N</i>-oxyl species are promising hydrogen atom transfer (HAT) catalysts to advance C-H bond activation reactions. However, because of the complex structure-activity relationship within the <i>N</i>-oxyl structure, catalyst optimization is a key challenge, particularly for simultaneous improvement across multiple parameters. This paper describes a data-driven approach to optimize <i>N</i>-oxyl hydrogen atom transfer catalysts. A focused library of 50 <i>N</i>-hydroxy compounds was synthesized and characterized by three parameters-oxidation peak potential, HAT reactivity, and stability-to generate a database. Statistical modeling of these activities described by their intrinsic physical organic parameters was used to build predictive models for catalyst discovery and to understand their structure-activity relationships. Virtual screening of 102 synthesizable candidates allowed for rapid identification of several ideal catalyst candidates. These statistical models clearly suggest that <i>N</i>-oxyl substructures bearing an adjacent heteroatom are more optimal HAT catalysts compared to the historical focus, phthalimide-<i>N</i>-oxyl, by striking the best balance among all three target experimental properties. Workflow ; Catalysis ; Hydrogen atom ; Chemistry ; Computer science ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Database ; Organic chemistry ; Group (periodic table) Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Pavel A. Martynenko
Source: Computational and Theoretical Chemistry (article, 2025-07-03)
Field: Chemistry — Organic Chemistry

Comparison of quantum chemical approach and mixed approach using AI in study of reactions involving silanediamides Chemistry ; Quantum chemical ; Computational chemistry ; Quantum ; Organic chemistry ; Quantum mechanics ; Molecule Organoboron and organosilicon chemistry ; Boron Compounds in Chemistry ; Machine Learning in Materials Science
Authors: Muaz | Saira Riaz | Mubashir Hassan
Source: Mechanics Exploration and Material Innovation (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

Mechanochemical Approaches for Advancing Lithium-Ion Battery Materials: Synthesis, Processing, and Performance Enhancement The growing demand for sustainable and high-performance energy storage solutions underscores the importance of advancing lithium-ion battery (LIB) materials. Mechanochemistry has emerged as a transformative tool in this domain, offering a solvent-free, energy-efficient approach to material synthesis, processing, and recycling. This review explores the principles, techniques, and advantages of mechanochemical methods for LIB development, including the synthesis of cathode and anode materials, the fabrication of solid-state electrolytes, and the recycling of critical components. Mechanochemical approaches enable precise control over particle size, morphology, and structural properties, resulting in enhanced electrochemical performance and sustainability. Challenges such as scalability and contamination are addressed alongside opportunities for integrating artificial intelligence and hybrid techniques to optimize processes. Additionally, the review highlights the application of mechanochemistry in emerging battery technologies, such as solid-state and sodium-ion systems, emphasizing its potential to drive the next generation of sustainable energy storage. This comprehensive analysis underscores mechanochemistry's pivotal role in overcoming the limitations of conventional methods, aligning with global efforts toward a greener energy future. Lithium (medication) ; Lithium-ion battery ; Battery (electricity) ; Materials science ; Ion ; Nanotechnology ; Computer science ; Psychology ; Chemistry ; Physics ; Power (physics) ; Psychiatry Polyoxometalates: Synthesis and Applications ; Catalysis and Oxidation Reactions ; Advancements in Battery Materials
Authors: Raúl Arteche Díaz | Hongliang Xin
Source: Chinese Journal of Chemical Engineering (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Knowledge graphs in heterogeneous catalysis: Recent advances and future opportunities Knowledge graph ; Catalysis ; Computer science ; Nanotechnology ; Biochemical engineering ; Chemistry ; Data science ; Engineering ; Materials science ; Organic chemistry ; Artificial intelligence Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Cognitive Computing and Networks
Authors: Jonathan M. Mauß | Klara S. Kley | Rohini Khobragade | N. V. Tran | Jacopo De Bellis | Ferdi Schüth | Matthias Scheffler | Lucas Foppa
Source: ACS Catalysis (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Modeling Time-On-Stream Catalyst Reactivity in the Selective Hydrogenation of Concentrated Acetylene Streams under Industrial Conditions 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 ; Catalysis ; Reactivity (psychology) ; Chemistry ; Bimetallic strip ; Naphtha ; Chemical engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Partha Paul | Vikas Singh Thakur | Swastika Banerjee
Source: Advanced Theory and Simulations (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

DFT‐D3 for Solid‐State Materials: A Data‐Driven Perspective on Accuracy Abstract Dispersion interactions are essential for accurate modeling of materials, yet standard Density Functional Theory (DFT) lacks explicit treatment. Grimme's DFT‐D3 method adds empirical dispersion corrections and performs well for molecular systems, but its reliability in solids remains uncertain. Here, DFT‐D3, including zero‐damping and Becke–Johnson variants, is systematically assessed across more than 2,000 lithium‐, potassium‐, magnesium‐, zinc‐, copper‐, and manganese‐based solid‐state binary compounds using a homegrown automated workflow integrating VASP , pymatgen , and a custom D3 interface. It is found that D3 often introduces artificial secondary minima in potential energy surfaces, particularly in metallic and densely packed solids, leading to unphysical stabilization and significant energy errors. The successor scheme D3S is also examined, and although it reduces dispersion errors relative to D3, it still produces artificial stabilization in ionic and metallic systems, indicating that its molecular‐level improvements do not fully extend to solids. Comparisons with many‐body dispersion (MBD@rsSCS) calculations show that these artifacts arise from abrupt coordination‐number‐dependent variations in the coefficients. Since the errors lack consistent correlation with structural or chemical descriptors, they remain difficult to predict. Overall, DFT‐D3 is reliable for covalent frameworks and low‐metal‐content intercalation systems but requires caution in dense, metal‐rich materials to avoid misleading overbinding. Maxima and minima ; Dispersion (optics) ; Workflow ; Reliability (semiconductor) ; Perspective (graphical) ; Energy (signal processing) ; Binary number ; Correctness Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Beemkumar Nagappan | Kulmani Mehar | S Prashanth | Divyesh Rameshbhai Vaghela | Sikata Samantaray | Chandrahas Sahu | Aseel Smerat | K. Kamakshi Priya
Source: Carbon Trends (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Advanced functionalization strategies of diamond and diamond-like carbon for emerging applications in sensing, electronics, and energy conversion • Functionalization enhances diamond/DLC utility in energy, sensing, and quantum tech • Review links synthesis methods to performance across key application sectors • Highlights role of doping, 2D hybridization, and defect engineering strategies • Identifies challenges in scalability, surface stability, and green synthesis • Proposes AI-driven design and integration with on-chip and biocompatible systems This comprehensive review aims to rigorously assess how sophisticated functionalization methodologies enhance the structural, electronic, and interfacial properties of diamond and diamond-like carbon (DLC) materials, thereby facilitating their integration into high-performance sensors, electronic devices, and energy conversion systems. In particular, we investigate heteroatom doping, covalent grafting, nanostructuring, and hybridization with two-dimensional (2D) materials, thereby establishing explicit correlations between processing techniques and performance metrics. Notable findings documented in the literature indicate that boron doping reduces the resistivity of diamond to approximately 10⁻²Ω·cm while preserving electrochemical stability, nitrogen-vacancy (NV) centers permit nanotesla-level quantum magnetometry with coherence durations surpassing 100µs, nanostructuring amplifies the electroactive surface area by as much as tenfold and diminishes the oxygen evolution overpotential by approximately 100mV, and diamond/DLC–graphene composites achieve specific capacitance values exceeding 250F g⁻¹ with sheet resistances below 50Ω sq⁻¹. These advancements have led to significant improvements in electrochemical biosensors, high-frequency field-effect transistors, flexible supercapacitors, and robust fuel cell electrodes. Furthermore, the review delineates ongoing challenges associated with lattice distortions, surface instability, and scalable manufacturing, while proposing future directions focused on Artificial Intelligence (AI)-assisted material design, eco-friendly synthesis routes, and standardized benchmarking protocols to expedite industrial implementation. Surface modification ; Diamond ; Boron nitride ; Electrochemical energy conversion ; Supercapacitor ; Heteroatom ; Overpotential ; Energy transformation ; Graphene Diamond and Carbon-based Materials Research ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: A. Garcia | Ken Sakaushi
Source: ACS electrochemistry. (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Pitfalls in Artificial Intelligence Powered Discovery Due to Electrocatalyst Evaluation Methodologies The explosive increase in popularity and increased accessibility to artificial intelligence and data science tools have opened the door for exploring previously deemed “too large” experimental spaces efficiently in experimental chemistry. This is of special interest for the field of electrocatalysis, where the development of new materials has relied largely on trial-and-error approaches that are not time- and resource-efficient. By leveraging these approaches, we can more effectively find promising electrocatalyst compositions and synthesis conditions that result in lower overpotentials and higher electrocatalyst durability. In this Technical Note, we use oxygen evolution electrocatalysts as a model to highlight potential pitfalls in the use of data-driven strategies for electrocatalyst development, focusing on the important choice of optimization metrics, normalization of data, and common errors that may appear during these kinds of approaches. Electrocatalyst ; Computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shuo Feng | Jun Jiang | Zhenyu Li
Source: The Journal of Chemical Physics (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Precision, intelligence, and a new paradigm for chemical research Chemists have long struggled to precisely regulate and create substances, often relying on trial-and-error methods that are inefficient for complex, high-dimensional research challenges. However, recent advancements in computational and experimental techniques, particularly those with artificial intelligence (AI), are providing new avenues for precision and intelligent chemistry. This perspective highlights the synergistic integration of accurate theoretical simulations, advanced experimental characterization, and AI-driven models, creating a closed-loop system to accelerate chemical discovery and material design. At the core of this framework is an iterative process: precise computational and experimental data lead to advanced intelligent models, which guide the design of optimized reaction parameters or chemical components, and direct robotic platforms that perform reproducible, high-throughput experiments. These experimental data, in turn, provide continuous feedback to refine intelligent models, ultimately enabling precise control of reaction conditions and material properties. To fully realize this vision, we advocate the development of key infrastructures: a multidisciplinary, multimodal, and standardized AI-ready chemical database as a data foundation; a knowledge and logic-enhanced large chemical model for intelligent prediction and design; distributed, full-process robotic laboratories for automated experimentation; and a cloud platform for resource sharing and collaboration. Together, these components constitute a vision for robotic chemist cloud facilities, which will empower researchers with unparalleled capabilities to seamlessly integrate precision and intelligence. This integrated approach promises to accelerate discovery and represents a paradigm shift in chemical research. Computer science ; Process (computing) ; Key (lock) ; Resource (disambiguation) ; Artificial intelligence ; Cloud computing ; Data science ; Human–computer interaction ; Systems engineering ; Engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Umar Farooq | Mohammad Ehtisham Khan | Akbar Mohammad | Nazim Hasan | Abdullah Alamri | Mukul Sharma
Source: Catalysts (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Sustainable Synthesis of Copper Oxide Nanoparticles: Data-Driven Photocatalysis, Pt-Free Hydrogen Production, and Antibacterial Assessment This study reports the green synthesis of copper oxide nanoparticles (CuO NPs) using Oxystelma esculentum extract as a reducing and stabilizing agent. The state-of-the-art analysis confirmed their spherical morphology, with an average particle size ranging from 20 to 25 nm, while XRD indicated a crystalline structure consistent with the standard JCPDS card. The photocatalytic degradation of norfloxacin (NOR) was optimized using Response Surface Methodology (RSM), which identified the optimal conditions as a reaction time = 47.51 min, CuO-NPs dose = 48.46 mg, NOR dose = 35.90 ppm, and pH = 5.23. Under these optimized conditions, the CuO NPs achieved an initial degradation efficiency of 90%. In addition to photocatalytic degradation, the hydrogen (H2) evolution performance of the CuO NPs was evaluated, yielding a H2 production rate of 19.52 mmol g−1 h−1 under visible light. Moreover, the antimicrobial activity of the CuO NPs was assessed, showing significant antibacterial effects with inhibition zones of 8 mm and 9 mm against Klebsiella and Bacillus species. The CuO NPs also exhibited potent anticancer activity with an IC50 value of 15.3 ± 1.40 μM against the HeLa cell line and notable antifungal activity with inhibition rates ranging from 70% to 90% against various fungal species. Photocatalysis ; Norfloxacin ; Copper ; Antibacterial activity ; HeLa ; Particle size ; Nanoparticle ; Hydrogen production ; Copper oxide Copper-based nanomaterials and applications ; Advanced Photocatalysis Techniques ; Nanoparticles: synthesis and applications
Authors: Nelson Oshogwue Etafo | Nathan Akache-Abutu | Daniel Gbenga Adekanmi | Sreedeep Dey | Muyideen Olaitan Bamidele | Micheal Bola Bamikale | Timothy Adewale Adeyi | Adrian David Cheok | Samuel Oghenenyerovwo Enochoghene | Opeyemi Thomas | Oresegun Olakunle Ibrahim | Abayomi Bamisaye
Source: Sensors and Actuators A Physical (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Luminescent guardians: A comprehensive review of photoluminescent materials for anti-counterfeiting and invisible ink Counterfeit ; Sustainability ; Photoluminescence ; Authentication (law) ; Covert ; Stewardship (theology) ; Supply chain Luminescence and Fluorescent Materials ; Forensic Fingerprint Detection Methods ; Biosensors and Analytical Detection
Authors: Simone Bagatella | Laura Castoldi | Marco Cavallaro | Elisabetta Gariboldi | Raffaella Suriano | Marinella Levi
Source: Journal of Applied Polymer Science (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

<scp>3D</scp>‐Printable Polymer Composites With Unmodified Boron Nitride for Thermal Management in Flexible Electronics ABSTRACT Flexible high‐performance electronics are facing the challenge of managing increasing thermal loads in the latest devices. This study presents a one‐step method for 3D printing flexible boron nitride (BN) polymer composites via vat photopolymerization, enhancing thermal conductivity by 270% to 0.8 W m −1 K −1 and improving dielectric properties (17% lower permittivity and 18% reduced dielectric loss). Unmodified h‐BN microplatelets are integrated in a UV‐curable polymer matrix, simplifying the manufacturing compared to functionalized or hybrid filler systems. Composites with 20 wt% BN achieve feature resolutions of 125 μm with excellent dimensional fidelity, while mechanical properties show an increase in tensile strength of 215% and retain flexibility. The thermal performance of the 3D‐printed structures is assessed using IR thermography under simulated operational conditions, confirming enhanced heat transfer in the BN‐filled structures. Overall, this research presents a practical method for improving thermal management in flexible electronics, offering an easy way to create high‐resolution 3D‐printed polymer composites. The resulting materials strike an optimal balance between thermal conductivity, dielectric properties, and mechanical flexibility, making them perfect for applications that demand exceptional thermal control and mechanical performance, such as artificial intelligence (AI)‐driven systems and flexible wearable technologies. Boron nitride ; Materials science ; Composite material ; Electronics ; Polymer ; Thermal ; Boron ; Thermal stability ; Chemical engineering ; Chemistry ; Organic chemistry Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Dielectric materials and actuators