jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicholas J. Galan | Britney Baez | Ju-An Kim | Beibei Yao | Nduka D. Ogbonna | Isaiah T. Dishner | Jared I. Bowman | Jesse McGaha | Ankit Shrivastava | Stephen Dewitt | Sheng Dai | Craig Bridges | Jeffrey C Foster | Tomonori Saito
Source: ChemRxiv (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

AI-guided Autonomous Workflows Accelerate Kinetic Analysis of Polymer Depolymerization Deep understanding of polymerization and depolymerization kinetic behavior enables precision synthesis and manufacturing. However, traditional kinetic experiments are bottlenecked by labor-intensive sample collection and analysis practices and suffer reproducibility challenges due to human error and bias. Here, we introduce an artificial intelligence (AI)-driven automated kinetics platform to facilitate kinetic analysis and produce large volumes of high-quality kinetic data with minimal human intervention. Our autonomous workflow, encompassing automated synthesis, analysis, and cloud computing capabilities navigated complex parameter space using an importance-guided Bayesian optimization algorithm, effectively reducing sampling burden while maintaining accuracy in calculating apparent reaction rate constants. Kinetic data from these experiments supported the development of a stochastic simulation model to extract valuable mechanistic information. These capabilities were exploited for “scale-up” synthesis by conducting reactions in parallel, facilitating reproducible access to highly precise polymeric building blocks and showing that depolymerization provides a powerful alternative to polycondensation for the synthesis of telechelics. This work demonstrates how the throughput and accuracy of kinetic experiments can be significantly enhanced by synergizing AI and automation, highlighting the significant potential of these tools to transform the practice of fundamental science. Depolymerization ; Workflow ; Kinetic energy ; Throughput ; Work (physics) ; Polymerization ; Polymer Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Catalysis and Oxidation Reactions
Authors: Ning Han | Heng Guo | Bingqi Zhang | Zeai Huang | Ying Zhou | Anke Weidenkaff | Guoxing Chen
Source: Nature Reviews Materials (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Artificial intelligence as a driver of sustainable materials and circularity Sustainability ; Applications of artificial intelligence ; Sustainable development ; Corporate sustainability Machine Learning in Materials Science ; Sustainable Industrial Ecology ; Chemistry and Chemical Engineering
Authors: Sandeep Singh | Aditya Kumar Singh | Arti Kashyap
Source: Journal of Magnetism and Magnetic Materials (article, 2026-03-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Geometric pattern and chemical environment in high magnetization materials: A data-driven study Magnetization ; Magnetic domain ; Ferromagnetism ; Magnetic field ; Magnetic anisotropy Magnetic Properties of Alloys ; Machine Learning in Materials Science ; Magnetic Properties and Applications
Authors: Shiyun Zhang | Yibo Yao | Hai Long | Dingwen Tao | Guangming Tan | Wei-Hua Wang | Yuan-Chao Hu
Source: arXiv (Cornell University) (preprint, 2026-03-14)
Field: Materials Science — Materials Chemistry

Research Paradigm of Materials Science Tetrahedra with Artificial Intelligence The classical material tetrahedron that represents the Structure-Property-Processing-Performance-Characterization relationship is the most important research paradigm in materials science so far. It has served as a protocol to guide experiments, modeling, and theory to uncover hidden relationships between various aspects of a certain material. This substantially facilitates knowledge accumulation and material discovery with desired functionalities to realize versatile applications. In recent years, with the advent of artificial intelligence (AI) techniques, the attention of AI towards scientific research is soaring. The trials of implementing AI in various disciplines are endless, with great potential to revolutionize the research diagram. Despite the success in natural language processing and computer vision, how to effectively integrate AI with natural science is still a grand challenge, bearing in mind their fundamental differences. Inspired by these observations and limitations, we delve into the current research paradigm dictated by the classical material tetrahedron and propose two new paradigms to stimulate data-driven and AI-augmented research. One tetrahedron focuses on AI for materials science by considering the Matter-Data-Model-Potential-Agent diagram. The other demonstrates AI research by discussing Data-Architecture-Encoding-Optimization-Inference relationships. The crucial ingredients of these frameworks and their connections are discussed, which will likely motivate both scientific thinking refinement and technology advancement. Despite the widespread enthusiasm for chasing AI for science, we must analyze issues rationally to come up with well-defined, resolvable scientific problems in order to better master the power of AI. Tetrahedron ; Enthusiasm ; Natural (archaeology) ; Artificial general intelligence ; Order (exchange) ; Paradigm shift ; Scientific discovery Machine Learning in Materials Science ; Material Selection and Properties ; Catalysis and Oxidation Reactions
Authors: Yali Li | Laura Tolosa | Francklin Rivas Echeverria | Ronald Marquez
Source: Journal of Chemical Education (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Translating UNESCO Artificial Intelligence Guidelines to Chemical Education and Its Intersection with Sustainable Development Goals As the utilization of artificial intelligence (AI) and generative AI (GenAI) is expanding in the educational field, it presents profound implications for STEM disciplines, particularly chemistry and chemical engineering. This Perspective explores the integration of AI in education, drawing from UNESCO guidelines and global recommendations from 2022 to 2025, underscoring the imperative of a human-centered pedagogical approach. The analysis highlights the transformative potential of AI in educational practices, focusing on enhanced personalized learning, teacher training, and academic management, all of which are seen as possibly contributing to advancing sustainable development goal 4 (SDG 4, quality education). It also discusses the risk of epistemic drift, where reliance on opaque algorithms may detach scientific inquiry from a causal understanding. We show examples of prompt engineering techniques for scientific illustration generation in the fields of chemistry and physical chemistry, and discuss its advantages, and limitations. Furthermore, the rapid development of AI technologies has outpaced the policy debates in most academic institutions, creating a significant policy gap in higher education. This is coupled with global disparity, where most academic institutions in high-income countries have implemented AI-driven tools by 2025, while access in low-income regions remains constrained. We argue that to harness the potential benefits of AI, the chemical education community must move beyond technical adoption to foster critical AI chemical literacy. This involves targeted investments in digital infrastructure and the development of assessments that prioritize human reasoning over algorithmic output. We conclude that the responsible integration of AI requires a shift from a content delivery model to a knowledge creation model guided by the high-level ethical frameworks proposed by UNESCO. Transformative learning ; Sustainable development ; Intersection (aeronautics) ; Generative grammar ; Quality (philosophy) ; Perspective (graphical) ; Science education ; Sociology of scientific knowledge Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Science Education and Pedagogy
Authors: Zhicong Chen | Yingli Liu | Weian Zhao | Yang Xiao | Pengcheng Li | Jie Li | Pengjie Zhang
Source: Journal of Solid State Chemistry (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Data-driven prediction of solid state reaction pathways in the synthesis of ferrites Thermogravimetric analysis ; Ferrite (magnet) ; Reaction mechanism ; Reaction rate ; Chemical reaction ; Solid-state ; Barium ferrite ; Chemical synthesis Machine Learning in Materials Science ; Magnetic Properties and Synthesis of Ferrites ; X-ray Diffraction in Crystallography
Authors: Seong‐Hoon Jang | Di ZHANG | Xue Jia | Hung Ba Tran | Linda Zhang | Ryuhei Sato | Yusuke Hashimoto | Toyoto Sato | K. Konno | Shin-ichi Orimo | Hao Li
Source: arXiv (Cornell University) (preprint, 2026-03-14)
Field: Materials Science — Materials Chemistry

Digital Hydrogen Platform (DigHyd): A Rigorously Curated Database for Hydrogen Storage Materials Empowered by AI-Assisted Literature Mining Solid-state hydrogen storage materials are promising candidates for safe and compact hydrogen storage; however, data-driven discovery in this field remains limited by the availability of large-scale, well-curated datasets. Here, we present the Digital Hydrogen Platform (DigHyd: www.dighyd.org), a rigorously curated database comprising $&gt;4,000$ experimental literature sources and $&gt;30,000$ data entries on hydrogen storage materials, constructed through AI-assisted literature mining combined with human-in-the-loop validation. In addition to gravimetric hydrogen storage density ($w$), DigHyd also covers thermodynamic parameters, specifically the enthalpy ($ΔH$) and entropy ($ΔS$) changes associated with hydrogenation reactions, primarily defined as $M + \frac{1}{2} {\rm H}_2 \rightleftarrows M{\rm H}$. These parameters were obtained by manually analyzing multi-temperature pressure-composition-temperature (PCT) data using van't Hoff analysis. By focusing on $ΔH$ and $ΔS$ rather than fixing equilibrium pressure at a single temperature, DigHyd enables flexible evaluation of equilibrium behavior under application-specific operating conditions. Statistical analyses reveal distinct distributions of thermodynamic parameters across material classes, together with broad compositional variability within representative hydride systems. Furthermore, both physically interpretable symbolic regression and black-box XGBoost models achieve comparable predictive performance for $w$ and equilibrium pressure at room temperature ($P_{\rm eq,RT}$), demonstrating internal consistency and learnable composition-property relationships within the curated dataset. Overall, DigHyd provides a rigorously curated thermodynamic dataset that serves as a reliable basis for data-driven analyses of hydrogen storage materials and supports systematic exploration of structure-property relationships. Hydrogen storage ; Consistency (knowledge bases) ; Hydrogen ; Enthalpy ; Field (mathematics) ; Gravimetric analysis Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion
Authors: Yu Chen | Xinyu Tan | Tao Wu | Bencan Tang | Zhenghui Wen
Source: CCS Chemistry (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Intelligent Flow Chemistry: A New Paradigm for Artificial Intelligence-Driven Synthesis and Discovery Flow (mathematics) ; Key (lock) ; Field (mathematics) ; Automation ; Feature (linguistics) Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Geoffrey Howland
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

The Lyric Engine: A Systematic Method for AI-Assisted Song Construction from Dense Source Material The Lyric Engine: A Systematic Method for AI-Assisted Song Construction from Dense Source Material This paper is part of the HOWL research archive—a collection of physics papers exploring integer fraction derivations across multiple domains using exact arithmetic and automated comparison. Falsification Criteria All papers in this archive are subject to falsification through direct comparison to published experimental measurements. Each derived value is tested against independent data with explicit PASS/FAIL criteria. Any derived value that fails its comparison is documented and published alongside the successes. Research Context This archive documents an ongoing research program in integer fraction physics. The methodology is: derive values from gauge group integers using exact fraction arithmetic, compare to published measurements, and document all results including failures. The archive spans multiple physics domains connected through the soliton boundary framework described in the constituent papers. Package Contents manuscript.md: The complete derivation and supporting analysis. README.md: Navigation, dependencies, and citation (Registry: HOWL-LYRIC-1-2026). Dependencies: HOWL-DISC-1-2026 Motto: Derive. Compare. Publish.Status: Complete Context (archaeology) ; Integer (computer science) ; Fraction (chemistry) ; Citation ; Value (mathematics) ; Subject (documents) ; Boundary (topology) Machine Learning in Materials Science ; Artificial Intelligence in Games ; Mathematics, Computing, and Information Processing
Authors: Shannon Marie Stephens
Source: Open MIND (article, 2026-02-16)
Field: Chemistry — Organic Chemistry

Computational Organic Chemistry Through Data Driven and Quantum Chemical Methods Computer-aided calculations are frequently used for modeling chemical reactions. Both predictive and quantum mechanical models give insight into reaction mechanisms and stereochemical outcomes. C–H borylation reactions offer a synthetic route toward late-stage functionalization (LSF) of advanced natural product intermediates and bioactive medicinal compounds. Borylation reactions allow for the direct functionalization of C–H bonds into C-B bonds via boron functional handles, such as a boronic ester. A commonly used reaction with boronic esters is the Suzuki-Miyaura cross-coupling, which readily forms new carbon-carbon bonds. Despite its utility, there have been obstacles in correctly predicting the site of borylation prior to performing these reactions. Site-selectivity prediction models are increasingly gaining popularity in the organic chemistry sphere and have been used to address this hurdle. This work will cover our random forest model developed to predict site-selective borylations as well as provide insights gathered from SHAP (SHapley Additive exPlanations) analysis and t-SNE modeling of our dataset. This dissertation will include reaction mechanism studies using quantum mechanical methods for reactions involving borylated olefin systems. The first study focuses upon reactivity and stability trends when synthesizing boron-containing bicyclo[2.2.2]octenes via [4+2] cycloadditions. A second study involves the formation of δ-silyl-homoallylic alcohols via allylboration with α-silyl-substituted boron reagents and aldehydes. Computational investigations illuminate stereochemical outcomes based upon steric hindrance and unique hydrogen bonding amongst the reactants within the transition states. Further computational studies were conducted to probe mechanisms for oxidations that use Bobbitt’s salt as a chemical oxidant. The results were used to formulate probable reaction pathways and mechanisms for the experimental formation of disulfides from thiols as well as diazines from hydrazides. These studies provided unique insights into chemical reactivity of Bobbitt’s salt with sulfur and nitrogen-containing systems. Finally, simulated sunlight exposure experiments involving terrestrial dissolved organic matter (tDOM) are analyzed using the Neo4j graph database via a temporal graph network. This study helped form a better understanding of the organic reactions that fluvial tDOM undergoes, thus improving our understanding of tDOM’s life cycle as it travels into contributing waterways, and eventually into the ocean, where it makes the bulk of oceanic carbon stores. Borylation ; Steric effects ; Organic synthesis ; Transition state ; Reagent ; Reactivity (psychology) ; Reaction mechanism ; Surface modification Organoboron and organosilicon chemistry ; Machine Learning in Materials Science ; Catalytic C–H Functionalization Methods
Authors: Apoth3osis R&d | Richard Goodman | Stephanie Goodman
Source: Open MIND (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Formal Chemistry Definitions and Auditable Certificate Pipelines as Infrastructure for Trustworthy AI-Driven Materials Search Trustworthiness ; Certificate ; Pipeline transport ; Key (lock) ; Public key infrastructure Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Jianyu Li | Ertao Hu | Wei Wei | Feifei Shi
Source: Advanced Functional Materials (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

From Wafers to Electrodes: Transferring Automatic Optical Inspection (AOI) for Multiscale Characterization of Smart Battery Manufacturing ABSTRACT The growing demand for scalable, reliable and cost‐effective battery manufacturing calls for fast, tight‐tolerance, and fully automatic characterization tools to understand battery materials. A major challenge lies in the characterization and control of electrode and cell‐level imperfections across the battery production chain. While laboratory‐based methods remain dominant in understanding material imperfections, their low throughput and high operational cost limit industrial scalability. Notably, automatic optical inspection (AOI) has been widely adopted in semiconductor manufacturing for inline inspection and control of imperfections. In this review, we discussed the critical challenges in transferring AOI techniques to battery manufacturing, which will bridge the knowledge gap between battery material characterization and semiconductor quality inspection. After discussing the imperfections and artificial intelligence (AI)‐driven optical techniques in the semiconductor industry, this review comprehensively assesses the electrode‐level and cell‐level imperfections in battery manufacturing. Offline and online battery characterization strategies, along with AI‐driven techniques in battery manufacturing, are discussed. We believe that digital twins (DTs), integrated with inline AOI and AI techniques, will have great potential for smart manufacturing of future batteries. Battery (electricity) ; Characterization (materials science) ; Throughput ; Semiconductor device fabrication ; Wafer ; Reliability (semiconductor) ; Bridge (graph theory) ; Semiconductor Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Biosensors and Analytical Detection
Authors: Won-Bum Park | Young-Joon Park | Juhun Lee | Jaewon Kim | Jieon Lee | Youn‐Bae Kang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

An Unsupervised Data-Driven Analysis of Calcination Reactions between Refractory Sagger and Cathode Precursors for Li-ion Batteries Corrosion ; Cathode ; Cluster analysis ; Calcination ; Oxide ; Partial pressure Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Petchi Raman Mariappan | Nazar Riswana Barveen | Chih-Yu Kuo | Yu‐Wei Cheng
Source: Chemical Engineering Journal (article, 2026-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of three-dimensional SERS-active biopolymer platform: Towards highly sensitive molecular detection and artificial intelligence-enhanced multi-biomarker diagnosis Photothermal therapy ; Substrate (aquarium) ; Raman scattering ; Multiplex ; Scalability ; Nanoparticle ; Proof of concept Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Laser-Ablation Synthesis of Nanoparticles
Authors: Ziyu Wan | Xinyi Xie | Chunmei Tang | S. Q. Ye | Baoyin Yuan | Ning Wang
Source: Renewable and Sustainable Energy (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

AI guided the development of materials science: a perspective Perspective (graphical) ; Development (topology) ; Key (lock) ; Context (archaeology) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nanoporous metals and alloys
Authors: Di Zhang | Yuanzheng Chen | Chuanyu Liu | Yan Liu | Hongliang Xin | J.S. Peng | Pengfei Ou | H. C. Li
Source: Angewandte Chemie (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Inside Back Cover: Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models (Angew. Chem. 16/2026) Catalysis ; Applications of artificial intelligence ; Artificial neural network Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Nanoporous metals and alloys
Authors: Stephen D. Turner
Source: Front Matter (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

AI-Assisted Customer Screening for DNA Synthesis Orders <ns4:p>A new preprint shows AI can handle legitimacy verification at a fraction of the cost.</ns4:p> Fraction (chemistry) ; Component (thermodynamics) ; Key (lock) ; Overhead (engineering) ; Preprint Machine Learning in Materials Science ; Genomics and Rare Diseases ; Biomedical Text Mining and Ontologies
Authors: Xiemeng Zhu | Juhong Yu | Y. Liu | Yangyang Song | Liang Zhang | Shiyu Du
Source: AI & Materials (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Application of artificial intelligence combined with density functional theory in materials Artificial neural network ; Field (mathematics) ; Feature (linguistics) ; Possibility theory Machine Learning in Materials Science ; Nanoporous metals and alloys ; Advanced Physical and Chemical Molecular Interactions
Authors: Céleste Constans | Carlotta Hatzipetros | Eluney Montreal Martinez | Sander Okkersen | Laurence Pobst | Lucy Širicová | Paul Stein | Max Thompson | Ellie Merrett Wade | Rokas Zabielskas
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-22)
Field: Chemistry — Analytical Chemistry

The Applications and Limitations of Artificial Intelligence in Chemical Identification via Spectroscopy Identification (biology) ; Spectroscopy ; Pattern recognition (psychology) ; Applications of artificial intelligence ; Artificial neural network Spectroscopy and Chemometric Analyses ; Molecular spectroscopy and chirality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Emil Obeid | Bachar Mourched | Younes Khaled | Cordier Anne | César Steil | P. Vernoux
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multiscale Insights into Ceria-Based Catalysts for Soot Oxidation: Structural and Electrical Properties, Catalytic Performance, Coupled with Data-Driven and Thermo-Mechanical Analysis Soot ; Catalysis ; Diesel particulate filter ; Diesel exhaust ; Particle size ; Scanning electron microscope ; Redox ; Particle (ecology) ; Catalytic oxidation Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Chemical Looping and Thermochemical Processes
Authors: O.S. Vodennikova | S.A. Vodennikov
Source: Metal and Casting of Ukraine (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

NanoArt and Metallurgy: Using Artificial Intelligence to Process Metal and Alloy Structures Process (computing) ; Alloy ; Metal working ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Metallurgy and Material Forming
Authors: Hieu-Chi Dam | Hiori Kino | Takashi Miyake
Source: Journal of the Physical Society of Japan (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

From Data to Discovery: Challenges and Solutions in AI-Driven Materials Science Field (mathematics) ; Key (lock) ; Work (physics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques