jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Upasana Roy (4047055) | Karanjeet Kaur (19796766) | Ramesh Hiralal Choudhary (21927163) | Jagdeep Virdi (21927166) | Bahare Askarian (21927169) | Timothy Glass (12094199) | Prasad Calyam (3587390) | Suchithra Rajendran (6596417) | Justin R. Walensky (1472710) | Fabrice Gallou (1520266) | Sachin Handa (1950661)
Source: Figshare (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Empowering MicellarCatalysis and Representation Learningwith Limited Data Availability: Surfactant Design Principle Can BoostYield Predictions Traditional trial-and-error methods for reaction optimization are time- and resource-consuming. The ability to accurately predict reaction yields under mild and safer conditions using artificial intelligence (AI) can enhance resource-efficient synthesis and significantly advance synthetic chemistry. This study introduces the development of an AI-driven model designed to predict reaction yields in micellar catalysis, utilizing representation learning and predictive analytics to minimize waste and promote efficient micellar methodologies. Despite facing challenges due to limited data availability, we trained the model using the sparse data for micellar catalysis and curated closely related information from traditional organic solvents, guided by the surfactant PS-750-M’s design principles and its intrinsic polarity alignment with organic solvents. The model employs a hybrid representation learning framework, integrating autoencoders for dimensionality reduction with a gradient-boosting regressor for predictive tasks. This approach demonstrated high predictive accuracy, with experimental validation of yields that closely matched the predicted values. These findings emphasize the transformative potential of AI in synthetic micellar chemistry, facilitating resource-efficient amide couplings as a model chemical transformation. This work establishes a solid foundation for the integration of advanced AI strategies into micellar catalysis, addressing existing data limitations and paving the way for future advancements in economical and efficient chemical design. Data-driven ; Representation (politics) ; Dimensionality reduction ; Curse of dimensionality ; Experimental data ; Yield (engineering) ; Micellar solutions ; Synthetic data ; Artificial neural network Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Asymmetric Hydrogenation and Catalysis
Authors: Muhammad Ramadhan | Khadijah Zai
Source: Research Square (preprint, 2025-11-25)
Field: Chemistry — Analytical Chemistry

Data-Driven Benchmarking of Raw Material Quality for Risk-Based QC Optimization in Pharmaceutical Manufacturing Benchmarking ; Pharmaceutical manufacturing ; Control chart ; Raw material ; Statistical process control ; Quality (philosophy) ; Raw data ; Product (mathematics) ; Process (computing) Spectroscopy and Chemometric Analyses ; Advanced Statistical Process Monitoring ; Fault Detection and Control Systems
Authors: Jonathan M. Mauß | Klara S. Kley | Rohini Khobragade | N. V. Tran | Jacopo De Bellis | Ferdi Schüth | Matthias Scheffler | Lucas Foppa
Source: ChemRxiv (preprint, 2025-02-03)
Field: Materials Science — Materials Chemistry

Modelling the Time-Dependent Reactivity of Catalysts by Experiments and Artificial Intelligence Reactivity (psychology) ; Computer science ; Catalysis ; Artificial intelligence ; Chemistry ; Organic chemistry ; Medicine Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Neal Munyebvu | Steve Dunn | Philip D. Howes
Source: ChemRxiv (preprint, 2025-04-10)
Field: Materials Science — Materials Chemistry

A multi-objective platform for autonomous targeting and optimization of colloidal quantum dots The optimization of colloidal quantum dot (CQD) materials, synthesis routes and processing methods are complex challenges that are ripe for automation and artificial intelligence (AI) to have a great impact. These optimization challenges are seldom oriented to a single target, therefore it is vital that autonomous systems can handle multiple objectives. In this work, we present an autonomous CQD synthesis system that successfully performs multi-objective optimization (MOO) via Bayesian optimization-based algorithms. We demonstrate the efficacy of the system through three distinct synthesis challenges, based on one, two and three objective optimization problems, in the synthesis of cesium lead halide perovskite CQDs. Objectives included maximizing fluorescence brightness, minimizing particle size dispersity, and targeting of specific energy bandgaps and particle diameters. The tri-objective challenge achieved simultaneous targeting of specific CQD sizes and band gaps independently via reaction tuning and halide doping, while minimizing the particle size dispersity. To the best of our knowledge, this is the first demonstration of AI-assisted multi-objective targeting and dynamic synthesis of targeted colloidal CQDs using exciton energy analysis of absorption spectra infer both size and bandgap. This work presents an accessible, automated, and data-driven platform for CQD discovery and optimization—both for single and multiple objectives—highlighting the promise of widespread integration of AI-guided strategies into CQD R&D. Quantum dot ; Nanotechnology ; Quantum ; Colloid ; Computer science ; Materials science ; Engineering ; Physics ; Quantum mechanics ; Chemical engineering Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Masayuki Okada | W. Zhu | Yoshifumi Amamoto | Jun Kikuchi
Source: ACS Materials Letters (article, 2025-11-06)
Field: Materials Science — Biomaterials

Data-Driven Formulation Based on Integrated Symbolic Regression of Hydrogel Swelling and Molecular Interactions Hydrogels offer promising solutions across various fields. However, understanding complex behaviors like swelling and ligand interaction requires multiperspective data, from functional groups to molecular dynamics. Single-perspective analyses often fall short, especially when ligand-induced selective adsorption occurs. This study presents a data-driven approach integrating TD-NMR, 1H–15N HSQC, 1H–13C HNCO with isotope-labeled peptides, RDKit descriptors, and DSC data. Using symbolic regression, we derived highly accurate, interpretable equations (e.g., swelling ratio accuracy = 1.0 on test set). This methodology reveals fundamental hydrogel mechanisms and provides a framework for rational design. Swelling ; Self-healing hydrogels ; Adsorption ; Ligand (biochemistry) ; Rational design Supramolecular Self-Assembly in Materials ; Hydrogels: synthesis, properties, applications ; Molecular spectroscopy and chirality
Authors: Світлана Кирилаха
Source: Digital Library NAES of Ukraine (National Academy of Educational Sciences of Ukraine) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Chapter VIII. Artificial Intelligence for Improving the Mechanical Properties of Materials Метою даної роботи є перевірка ефективності алгоритму машинного навчання на основі даних з відкритих баз, зокрема Material Project. Використовуваний штучний інтелект, заснований на лінійній регресії, застосовує трансферне навчання для адаптації до фізико-механічних моделей високоентропійних сплавів. Для реалізації використовуються відкриті Python-модулі, що забезпечують гнучкість у дослідженнях. Точність моделювання фізико-механічних властивостей матеріалу залежить від кількості елементів у складі та здатності комбінувати матеріали в одну ланку. Для перевірки результатів експериментів у практиці на основі літературних джерел показано метод визначення твердості за Вікерсом, що дозволило оцінити коректність запропонованого підходу до прогнозування властивостей високоентропійних сплавів. Applications of artificial intelligence ; Artificial neural network ; Work (physics) ; Expert system Material Properties and Failure Mechanisms ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: César García-Gascón | Pablo Castelló-Pedrero | Javier Bas-Bolufer | Juan Antonio García-Manrique | Francisco Chinesta
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-driven Methodologies for Advancing Composite Material Analysis and Applications Composite number ; Work (physics) ; Process (computing) ; Welding ; Raw material Machine Learning in Materials Science ; Composite Material Mechanics ; Material Selection and Properties
Authors: Neeraj R Prasad | Slok S. Sapakal | Anil A Powar | Saurabh Prasad | Rutikesh Gurav | Avinash Dasharath Harale
Source:  (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

AI Driven Nanotechnology- Advances in Imaging, Materials Design, and Nanomedicine Nanomedicine ; Key (lock) ; Applications of artificial intelligence ; Biomedicine Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Nanoporous metals and alloys
Authors: César García-Gascón | Pablo Castelló-Pedrero | Javier Bas-Bolufer | Juan Antonio García-Manrique | Francisco Chinesta
Source: Open MIND (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-driven Methodologies for Advancing Composite Material Analysis and Applications Composite number ; Work (physics) ; Process (computing) ; Welding ; Raw material Machine Learning in Materials Science ; Composite Material Mechanics ; Material Selection and Properties
Authors: Yu-Hang Yuan | Yu-Chen Gao | Xiang Chen | Qiang Zhang
Source: Springer Link (Chiba Institute of Technology) (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Overcoming data scarcity challenges in AI-driven energy chemistry research Scarcity ; Energy (signal processing) ; Environmental impact of the energy industry ; Work (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Світлана Цехмістренко | Володимир Бітюцький | Ірина Ластовська
Source: Digital Library NAES of Ukraine (National Academy of Educational Sciences of Ukraine) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Chapter XV. Integration of artificial intelligence into chemical and biotechnological research У роботі досліджує роль штучного інтелекту (ШІ) у хімічних та біотехнологічних дослідженнях, його застосування в аграрному секторі та освіті. Використання алгоритмів машинного навчання дозволяє оптимізувати синтетичні шляхи, моделювати біохімічні реакції та прогнозувати властивості нових сполук. У біотехнології ШІ сприяє аналізу генетичних даних, персоналізованій медицині, ферментаційним процесам і створенню біорозкладних матеріалів. В агротехнологіях інтелектуальні системи забезпечують прецизійне землеробство, моніторинг стану ґрунтів, прогнозування врожайності та здоров'я тварин. Освітня сфера отримує переваги від адаптивних навчальних платформ, віртуальних лабораторій і персоналізованого навчання. Інтеграція ШІ у науку, виробництво та освіту є ключовою для технологічного розвитку та післявоєнного відновлення України. Запровадження інтелектуальних систем підвищує ефективність досліджень, сприяє раціональному використанню ресурсів і вдосконаленню навчального процесу. Applications of artificial intelligence ; Expert system ; Artificial life ; Data integration Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Artificial Intelligence in Healthcare and Education
Authors: Moosavi, Seyyed Arash | Jalili Torkamani, Mehrdad | Raeisdana, Ali | Farahi Jahromi, Mahmood | Moosavi, Seyyed Sina
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-01)
Field: Chemistry — Spectroscopy

Aerogel Catalysts: A Comprehensive Review Aerogels, first discovered in 1931, represent a unique class of ultralow-density materials with hierarchical porosity, high surface area, and tunable physical properties, making them ideal scaffolds for advanced catalysis. This comprehensive review critically examines aerogel catalysts, addressing inconsistencies in terminology and classification by proposing density-based categories (e.g., ultralight to heavy aerogels) and microstructure-focused definitions aligned with IUPAC standards. We explore fundamental principles, including mechanical fragility mitigated through multiscale reinforcements, thermal stability enhanced via compositional and architectural engineering, and optical properties optimized for photocatalysis through bandgap tuning and hybridization. Synthesis and fabrication methods are detailed, emphasizing the sol-gel process under mild conditions, drying techniques (supercritical, freeze, ambient), and material classes spanning silica, carbon, metals, and hybrids. Characterization techniques such as microscopy, spectroscopy, and surface area analysis are discussed, alongside functionalization strategies like active site engineering and data-driven multiscale modeling. Applications are highlighted in environmental catalysis (e.g., pollutant degradation, VOC removal), energy conversion and storage (e.g., HER, OER, ORR, batteries, fuel cells), and chemical synthesis (e.g., hydrogenation, oxidation, biomass conversion). Recent advances incorporate enzymes, nanostructures, semiconductors, and innovative fabrication like 3D printing and microfluidics for multifunctional hybrids. This review underscores aerogels' potential to drive efficient, sustainable processes while identifying challenges in scalability, stability, and standardization. Future outlook emphasizes opportunities in bio-, nano-, and photo-catalysis, providing a foundational framework for researchers to advance next-generation catalytic technologies. Aerogel ; Characterization (materials science) ; Process (computing) ; Terminology ; Fabrication ; Aerospace Aerogels and thermal insulation ; Mesoporous Materials and Catalysis ; Magnesium Oxide Properties and Applications
Authors: Xiumin Liu | Xinyue Zhang | Yabei Wu | Xuehai Ju | Caichao Ye | Wenqing Zhang
Source: Physical Review Materials (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

OPECM: A comprehensive dataset for organic polymer energy conversion materials enabling data-driven interpretative analysis Polymer ; Energy (signal processing) ; Energy transformation ; Organic polymer Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Fuel Cells and Related Materials
Authors: Yann Garcia | NCMS 2025- 16th National Conference on Mössbauer Spectroscopy
Source: Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mössbauer spectroscopy applications in chemistry Mössbauer spectroscopy finds nowadays a number of applications in various research fields including biology, physics, planetology, materials science, metallurgy, mineralogy but also chemistry. Indeed, Mössbauer spectroscopy can deliver useful information on electronic properties of matter, structural insights, dynamics and solid-state effects of chemical systems. Most of the time, the new generation of inorganic chemists is not aware of the potentiality of the technique, due to its fast disappearance from chemistry school programs, which is very surprising at the time of artificial intelligence and computer search softwares. It is even considered as a complex physical technique without any meaning for chemists in general. Recently, Garcia, Wang and Zhang took up the challenge to write a book on Mössbauer spectroscopy for chemists, which is proposed to conference participants.1 The book offers a wide range of chemical applications and concepts as well as numerous examples. It will be beneficial to to readers working in different areas of chemistry and material science covered by Mössbauer spectroscopy such as energy (batteries), environment, life sciences, molecular electronics, optoelectronic and thermoelectric applications, catalysis, etc. It is intended to researchers in academia and industry, but also occasional readers and newcomers to the field. In this lecture, I will review several examples of transition metal complexes studied by 57Fe and 119Sn Mössbauer spectroscopy and focus in particular on complexes displaying the spin crossover phenomenon. Spectroscopy ; Molecular spectroscopy ; Spin crossover ; Electron spectroscopy ; Meaning (existential) ; Range (aeronautics) Magnetism in coordination complexes ; Crystallography and Radiation Phenomena ; Organometallic Complex Synthesis and Catalysis
Authors: Caleb
T. Kozuszek (22795055) | Cole C. Sorensen (12505526) | Frank A. Leibfarth (675326)
Source: Figshare (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Understanding and EnhancingStereoselective PolymerizationUsing a Data Science Approach Controlling the tacticity of synthetic polymers results in the transformation of simple chemical building blocks into valuable materials with emergent physical properties. Mechanistic insight into stereoselective polymerizations enables hypothesis-driven improvements to catalysts and gives access to polymers with systematic differences in tacticity for structure–property studies. Studying the mechanism of stereoselective polymerization, especially of heteroatom-containing monomers that polymerize through ionic intermediates, is hindered by the challenges of using traditional physical–organic or computational approaches. Here, we use a combination of experiments and computationally derived molecular descriptors to identify quantitative relationships between catalyst structure and stereoselectivity through a data science approach. Stereoselective polymerization of benzyl vinyl ether derivatives with a structurally diverse library of imidodiphosphorimidate (IDPi) catalysts resulted in 40 experimental data points, which were correlated to computationally derived molecular descriptors by using multivariate linear regression analysis. The regression model identified the dihedral angle of the 1,1’-binaphthyl-2,2’-diol (BINOL) subunit of the IDPi to be strongly correlated to isotacticity, which led us to reconsider the long-standing hypothesis for the conformation of the propagating polymer chain-end during cationic vinyl ether polymerization. We anticipate that the specific insights of this study will inform the next generation of catalysts for stereoselective cationic polymerization and that the data-driven approach to understand the mechanism for stereoselective polymerizations demonstrated herein will be an invaluable tool in catalyst design and discovery for polymer chemistry broadly. Stereoselectivity ; Polymerization ; Cationic polymerization ; Tacticity ; Polymer ; Catalysis ; Monomer ; Vinyl ether Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Carbon dioxide utilization in catalysis
Authors: Caleb T. Kozuszek | Cole C. Sorensen | Frank A. Leibfarth
Source: Journal of the American Chemical Society (article, 2025-12-05)
Field: Chemistry — Organic Chemistry

Understanding and Enhancing Stereoselective Polymerization Using a Data Science Approach Controlling the tacticity of synthetic polymers results in the transformation of simple chemical building blocks into valuable materials with emergent physical properties. Mechanistic insight into stereoselective polymerizations enables hypothesis-driven improvements to catalysts and gives access to polymers with systematic differences in tacticity for structure-property studies. Studying the mechanism of stereoselective polymerization, especially of heteroatom-containing monomers that polymerize through ionic intermediates, is hindered by the challenges of using traditional physical-organic or computational approaches. Here, we use a combination of experiments and computationally derived molecular descriptors to identify quantitative relationships between catalyst structure and stereoselectivity through a data science approach. Stereoselective polymerization of benzyl vinyl ether derivatives with a structurally diverse library of imidodiphosphorimidate (IDPi) catalysts resulted in 40 experimental data points, which were correlated to computationally derived molecular descriptors by using multivariate linear regression analysis. The regression model identified the dihedral angle of the 1,1'-binaphthyl-2,2'-diol (BINOL) subunit of the IDPi to be strongly correlated to isotacticity, which led us to reconsider the long-standing hypothesis for the conformation of the propagating polymer chain-end during cationic vinyl ether polymerization. We anticipate that the specific insights of this study will inform the next generation of catalysts for stereoselective cationic polymerization and that the data-driven approach to understand the mechanism for stereoselective polymerizations demonstrated herein will be an invaluable tool in catalyst design and discovery for polymer chemistry broadly. Stereoselectivity ; Polymerization ; Cationic polymerization ; Polymer ; Tacticity ; Monomer ; Catalysis ; Vinyl ether Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Weiqing Min | Zhiming Guo | Hui Hong | Hong Zhang
Source: Food Chemistry (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

AI for food chemistry: Artificial intelligence (AI)-enabled efficient analysis methods and applications in food chemistry Food chemistry ; Applications of artificial intelligence ; Food processing ; Food products ; Food industry Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: V.P. Ananikov
Source: Вестник Российской академии наук / Herald of the Russian Academy of Sciences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

DIGITAL TECHNOLOGIES IN CHEMISTRY AND MATERIALS SCIENCE The article considers the problems and prospects of digitalization of chemical processes and technologies on the background of the global chemical landscape. Special attention is paid to such issues as low speed of data processing, the problem of lost data, knowledge transfer in micro- and low-tonnage chemistry technologies, and the digital cycle of chemical reaction production. Key areas are the application of specialized digital algorithms needed to solve specific problems in chemistry, accelerating data analysis, automating chemical research, creating new approaches to design materials for catalysis, and developing digital tools for science and educational programs. Important practical application is the use of artificial intelligence for scaling of chemical processes and technology transfer into industrial solutions. Key (lock) ; Chemical technology ; Chemical process ; Emerging technologies ; Chemical industry ; Digital data Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Pavel A. Dub | Thomas P. Hughes | Thomas J. L. Mustard
Source: ChemRxiv (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

A Software Framework for Physics- and AI-Driven Homogeneous Catalyst Design and Reactivity Optimization Catalysis ; Homogeneous ; Workflow ; Software ; Homogeneous catalysis ; Key (lock) ; Heterogeneous catalysis Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions