jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elizabeth
A. Recker (13528797) | Xiaofeng Chen (392939) | Ji-Won Kim (174475) | Zachariah A. Page (2561827)
Source: Figshare (article, 2025-08-08)
Field: Chemistry — Organic Chemistry

One-Vat Multimaterial 3D Printing: The Devil is inthe Details Nature combines different materials in a single structure to achieve functions that no single material could accomplish alone, an approach that inspires efforts to build synthetic systems with precisely tailored properties. Vat photopolymerization (VPP) enables fast, high-resolution 3D printing, but most printed parts still use only one material. This Outlook highlights emerging strategies for single-vat multimaterial VPP, where light selectively activates different chemical reactions to build complex structures with multiple materials. Key advances will depend on expanding resin chemistry beyond standard acrylates, improving reaction selectivity, and using grayscale and multiwavelength light control to define where and how materials form. Standardized mechanical, thermal, and interface testing methods are essential for ensuring reliable results. With advances in chemistry, optics, and data-driven design, multimaterial VPP could unlock transformative applications in medicine, manufacturing, and aerospace. Key (lock) ; Transformative learning ; Scale (ratio) ; Control (management) ; Interface (matter) Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies ; 3D Printing in Biomedical Research
Authors: Tinghan Yang | Hongyu Tan | Yiwen Ren | Xianshuo Wu | Suhao Hu | Yuhan Du | Lingjie Sun | Fangxu Yang | Wenping Hu
Source: Chemical Research in Chinese Universities (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Advances in Organic Two-dimensional Materials for X-Ray Detection and Their Applications in Flexible Electronics Electronics ; Organic electronics ; Detector ; Transistor ; Photonics ; Absorption (acoustics) ; Aerospace ; Wearable computer Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Xiang Yin | Jinyu Rong | Sijie Lin
Source: Nano LIFE (article, 2025-03-13)
Field: Materials Science — Materials Chemistry

(Invited) Opinion: From Data Integration to Intelligent Prediction– Advancing Nanomaterials Innovation and Sustainability Through Machine Learning The rapid advancement of artificial intelligence (AI) is revolutionizing research paradigms in nanotechnology, playing an increasingly pivotal role in material discovery, synthesis, application, and risk assessment. This opinion provides a comprehensive analysis of the current development in “AI + nanotechnology” interdisciplinary field. First, we evaluated the current state of available data in nanomaterials research and proposed future directions for advancing data standardization. Second, we systematically categorized the nano-descriptors and AI algorithms commonly employed in current studies while identifying potential areas for improvement. Finally, we highlighted the current applications and future prospects of AI in material discovery and synthesis, as well as in risk prediction and sustainable-by-design. Sustainability ; Computer science ; Knowledge management ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Nanotechnology research and applications
Authors: Daniel Hervitz | Yibin Jiang | Daniel Salley | Mark McNulty | Philip J. Kitson | Leroy Cronin
Source: ACS Applied Materials & Interfaces (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Automated Digital Discovery and Synthesis of CuO-Based Nanoparticle Heterostructures for Catalysis The discovery and synthesis of composite nanomaterials often rely on molecular self-assembly and crystallization, posing significant challenges due to the vast chemical space and the irreproducibility of experimental methods. We present a programmable robotic platform, controlled by the universal Chemical Description Language (χDL), that enables the solid-phase synthesis of composite nanomaterials. In addition to synthesis, the platform validates its catalytic performance through an automated workflow. This platform enables open-ended exploration of composition-morphology-activity relationships, with high accuracy and reproducibility, while also reducing synthesis time and cost. In this study, we are moving beyond the colloidal, plasmonic-focused systems previously explored in robotic platforms to the discovery, synthesis, and catalytic properties of CuO-based nanomaterials, such as CuO-Au and CuO-Ag<sub>2</sub>O NP heterostructures that show good reproducibility across repeated syntheses. Remarkably, even at very low metal loadings, as confirmed by ICP (Au wt % = 0.06%, Ag wt % = 0.03%), the heterostructures exhibited enhanced photodegradation efficiency of the dye Methyl Green (MG) compared with pristine CuO. The degradation yield increased from 45 ± 2% for pristine CuO to 57 ± 3% for CuO-Au and 65 ± 2% for CuO-Ag<sub>2</sub>O, as observed through real-time UV-vis spectroscopy. Additionally, a kinetic assay of the synthesis process provided insights into the self-assembly mechanism, highlighting the interactions between the core material (CuO NPs) and the surface coatings (Au or Ag<sub>2</sub>O). This work demonstrates a shift from traditional manual experimentation to programmable, data-driven workflows, highlighting both the progress and the remaining challenges in the automation of solid-phase nanomaterial synthesis in the field of materials science. Nanomaterials ; Heterojunction ; Catalysis ; Nanoparticle ; Flexibility (engineering) ; Photodegradation ; Automation Copper-based nanomaterials and applications ; Quantum Dots Synthesis And Properties ; ZnO doping and properties
Authors: Grace Guinan | Michelle A. Smeaton | Brian C. Wyatt | Steven Goldy | Hilary Egan | Andrew Glaws | Garritt J. Tucker | Babak Anasori | Steven R. Spurgeon
Source: ArXiv.org (preprint, 2025-11-11)
Field: Materials Science — Materials Chemistry

Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti$_3$C$_2$T$_X$ MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures--from isolated vacancies to nanopores--revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials. Crystallographic defect ; Hierarchy ; Point (geometry) ; Topology (electrical circuits) ; Dimension (graph theory) ; MXenes ; Cluster analysis MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Daniel Hervitz (22414233) | Yibin Jiang (702035) | Daniel Salley (4915312) | Mark McNulty (22414236) | Philip. J Kitson (22414239) | Leroy Cronin (538206)
Source: Figshare (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

AutomatedDigital Discovery and Synthesis of CuO-BasedNanoparticle Heterostructures for Catalysis The discovery and synthesis of composite nanomaterials often rely on molecular self-assembly and crystallization, posing significant challenges due to the vast chemical space and the irreproducibility of experimental methods. We present a programmable robotic platform, controlled by the universal Chemical Description Language (χDL), that enables the solid-phase synthesis of composite nanomaterials. In addition to synthesis, the platform validates its catalytic performance through an automated workflow. This platform enables open-ended exploration of composition-morphology-activity relationships, with high accuracy and reproducibility, while also reducing synthesis time and cost. In this study, we are moving beyond the colloidal, plasmonic-focused systems previously explored in robotic platforms to the discovery, synthesis, and catalytic properties of CuO-based nanomaterials, such as CuO-Au and CuO-Ag<sub>2</sub>O NP heterostructures that show good reproducibility across repeated syntheses. Remarkably, even at very low metal loadings, as confirmed by ICP (Au wt % = 0.06%, Ag wt % = 0.03%), the heterostructures exhibited enhanced photodegradation efficiency of the dye Methyl Green (MG) compared with pristine CuO. The degradation yield increased from 45 ± 2% for pristine CuO to 57 ± 3% for CuO-Au and 65 ± 2% for CuO-Ag<sub>2</sub>O, as observed through real-time UV–vis spectroscopy. Additionally, a kinetic assay of the synthesis process provided insights into the self-assembly mechanism, highlighting the interactions between the core material (CuO NPs) and the surface coatings (Au or Ag<sub>2</sub>O). This work demonstrates a shift from traditional manual experimentation to programmable, data-driven workflows, highlighting both the progress and the remaining challenges in the automation of solid-phase nanomaterial synthesis in the field of materials science. Nanomaterials ; Heterojunction ; Catalysis ; Nanoparticle ; Automation ; Flexibility (engineering) ; Composite number Copper-based nanomaterials and applications ; Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: S. C. Jiang | Ying Yang | Ziang Liu | Rui Yang | He Yan | Xiuwen Cheng
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerating Catalytic Process Optimization for Water Treatment via Automated Knowledge Extraction and Machine Learning Pipeline (software) ; Process (computing) ; Knowledge acquisition ; Parsing ; Work (physics) ; Process optimization ; Data acquisition ; Data collection Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Sung‐Ryong Kim | Fernando Gomes de Souza | Fabíola da Silveira Maranhão | Viviane Silva Valladão | Carolina Delfino | Daniele Silvéria Brandão e Silva | Gabriel Silva | Bruna Tebaldi | Suelen Cunha | Luis Escobar | Thiago do Nascimento Peçanha | R. dos Santos | Jean Dutra | Jessica Cardoso
Source: Brazilian Journal of Experimental Design Data Analysis and Inferential Statistics (article, 2025-07-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structural Design Strategies for Thermally Conductive and Electromagnetic Interference Shielding Nanocomposites The development of thermally conductive and electromagnetic interference (EMI) shielding nanocomposites is becoming increasingly critical for next-generation electronic devices, where effective heat dissipation and high signal integrity are essential. This report summarizes a talk by Professor Sung-Ryong Kim from the Korea National University of Transportation, emphasizing structural design strategies—such as layered architectures, segregated structures, three-dimensional (3D) interconnected networks, and stretching-induced alignment—to enhance both thermal conductivity and EMI shielding effectiveness in polymer-based nanocomposites. By integrating nanofillers such as graphene fluoride (GF), MXene, and carbon nanotubes, Professor Kim’s work highlights how rational structural design can yield ultrathin films with thermal conductivity values up to 210 W/m·K and EMI shielding effectiveness (SE) reaching 62 dB under optimal filler contents. The strategies address not only GHz and THz frequency shielding demands but also practical industry concerns like cost, scalability, and mechanical robustness. Reflecting on potential future directions, Professor Kim underscored the promise of integrating artificial intelligence (AI) to expedite predictive material optimization, and he expressed enthusiasm for collaborating with computational scientists to guide advanced composites research. This convergence of data-driven modeling, sustainable polymer matrices, and industrial manufacturing constraints points the way to commercializable, high-performance thermoconductive EMI shielding solutions. Electromagnetic shielding ; Electromagnetic interference ; Electrical conductor ; Nanocomposite ; Materials science ; Interference (communication) ; Composite material ; Electronic engineering ; Electrical engineering ; Engineering Electromagnetic wave absorption materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Thermal properties of materials
Authors: Tsz Wai Ko | Ruizhao LIU | Adesh Rohan Mishra | Zihan Yu | Ji Qi | Shyue Ping Ong
Source: ArXiv.org (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

A Fast, Accurate, and Reactive Equivariant Foundation Potential Electrostatics govern charge transfer and reactivity in materials. Yet, most foundation potentials (FPs) either do not explicitly model such interactions or pay a prohibitive scaling penalty to do so. Here, we introduce charge-equilibrated TensorNet (QET), an equivariant, charge-aware architecture that attains linear scaling with system size via an analytically solvable charge-equilibration scheme. We demonstrate that a trained QET FP matches state-of-the-art FPs on standard materials property benchmarks but delivers qualitatively different predictions in systems dominated by charge transfer. The QET FP reproduces the correct structure and density of the NaCl-CaCl2 ionic liquid, which charge-agnostic FPs miss. We further show that a fine-tuned QET captures reactive processes at the Li/Li6PS5Cl solid-electrolyte interface and supports simulations under applied electrochemical potentials. These results remove a fundamental constraint in the atomistic simulation of accurate electrostatics at scale and establish a general, data-driven framework for charge-aware FPs with transformative applications in energy storage, catalysis, and beyond. Electrostatics ; Scaling ; Charge (physics) ; Property (philosophy) ; Constraint (computer-aided design) ; Scale (ratio) ; Interface (matter) ; Topology (electrical circuits) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Mohammad Jellur Rahman
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Tin Oxide Thin Films for the Future: A Paradigm Shift in Property Engineering for Advanced Functional Devices Paradigm shift ; Tin ; Materials science ; Property (philosophy) ; Tin oxide ; Oxide ; Nanotechnology ; Engineering physics ; Computer science ; Engineering ; Metallurgy ; Physics ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Transition Metal Oxide Nanomaterials
Authors: Yangchao Liao | Wei Chen | Zhiping Xu
Source: Physical Review Materials (article, 2025-12-05)
Field: Materials Science — Ceramics and Composites

Microstructure-guided glass ceramics design via physics transfer Glass ceramics are functional materials created through controlled crystallization of base glasses, combining properties of both glasses and ceramics. Typically with crystallinity below 0.9, their crystal sizes, from nanometers to micrometers, are governed by processing conditions and nucleating agents. Smaller crystals enhance optical transparency, while larger ones improve mechanical strength, making the optimization of this tradeoff a critical challenge in composition and process design. We address this challenge quantitatively using continuum-level multiphysics modeling for both electromagnetic wave propagation and material fracture, which generate guiding curves for optimal property selection. Integrating these models incorporating microstructure effects and experimental data where the chemistry is encoded, we establish a comprehensive composition-processing-microstructure-properties map. Crucially, the one-to-one mapping from the microstructure space to the performance enables a physics transfer approach, supporting data-driven glass development of glass ceramics with tailored optical and mechanical properties, for reduced experimental costs and accelerated processes of material design. Microstructure ; Multiphysics ; Ceramic ; Nanometre ; Crystallization ; Crystallinity ; Glass-ceramic ; Crystal (programming language) Glass properties and applications ; Microwave Dielectric Ceramics Synthesis ; Luminescence Properties of Advanced Materials
Authors: Fuyu Han | Yupeng Wu | Linchuan Cong | Fangbing Liu | Xinxin Li | Ciduo Wang | Kewei Ma | Xiang‐Gao Meng | Nan Lin | Haibo Lin
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Electrochemistry

Unlocking Aqueous Electrosynthesis of Λ5-Iodane: Dft-Guided Catalyst Screening Meets Multivariate Optimization Electrosynthesis ; Multivariate statistics ; Catalysis ; Aqueous solution ; Computer science ; Chemistry ; Chemical engineering ; Materials science ; Engineering ; Physical chemistry ; Organic chemistry ; Machine learning ; Electrochemistry Electrochemical Analysis and Applications ; Oxidative Organic Chemistry Reactions ; Radical Photochemical Reactions
Authors: Ryota Shimizu
Source: Vacuum and Surface Science (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

AI-Robot-Driven Autonomous Synthesis of Functional Inorganic Thin Films The field of materials exploration is rapidly expanding to meet the social demand for functional materials. Autonomous materials research, which employs artificial intelligence (AI)-based decision-making in combination with automated synthesis and measurements carried out by robots, presents a promising avenue. Recently, significant progress has been made in the development of solid-state systems, in parallel with liquid systems where materials are more easily handled. Here, I present our recent research on the autonomous synthesis of functional inorganic oxide thin films. Through iterative operations of automated thin film deposition (utilizing robots), measurement of electrical conductivity (also performed by robots), and the application of Bayesian optimization (AI) for decision-making, we achieved approximately a tenfold increase in throughput. Furthermore, I will present trends in other countries and discuss future prospects. Robot ; Thin film ; Materials science ; Computer science ; Chemical engineering ; Nanotechnology ; Artificial intelligence ; Engineering Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Dawei Feng
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Chemistry — Organic Chemistry

<i>(Invited)</i> Thermodynamics-Informed Approach for Organic Molecular Additives in Halide Redox Flow Batteries With the growing integration of renewable energy sources, the need for efficient, large-scale energy storage solutions becomes increasingly vital for grid stability. Redox flow batteries (RFBs) are a leading candidate for this role, offering scalability and flexibility. This talk presents two innovative approaches to advancing RFB technology for grid energy storage. In the first approach, we perform extensive manual data collection through high-throughput synthesis and testing of aqueous organic redox flow batteries. This method enables the rapid identification and optimization of redox-active molecules that exhibit improved energy density and enhanced cycling stability, addressing the key challenges of large-scale energy storage. In the second approach, we employ thermodynamic analysis to guide the development of halide-based flow batteries with exceptional stability and energy density. By introducing organic molecular additives, such as ZIT (Zwitterionic Trapper), we significantly enhance the cycling stability and energy performance of these systems. Through the combination of data-driven experimentation and physics-informed predictive models, these strategies provide new insights and practical pathways for designing efficient and long-lasting redox flow batteries, paving the way for their role in sustainable grid energy storage. Halide ; Redox ; Flow (mathematics) ; Thermodynamics ; Chemistry ; Materials science ; Chemical engineering ; Chemical physics ; Physics ; Mechanics ; Inorganic chemistry ; Engineering Chemical Synthesis and Reactions ; Oxidative Organic Chemistry Reactions
Authors: Sung Cheol Kim | Ju Hyeon Park | Sang Ouk Chin | Jaehoon Kim | Dokyoung Kim
Source: Advanced Composites and Hybrid Materials (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

Recent advances in anti-counterfeiting technology using fluorescent hybrid materials Counterfeiting has rapidly evolved with advances in manufacturing and digital replication technologies, posing serious challenges across various sectors, including pharmaceuticals, electronics, textiles, finance, and food. Traditional anti-counterfeiting methods such as holograms, barcodes, and radio frequency identification tags have become increasingly vulnerable to high-resolution reproduction techniques, necessitating innovative solutions. Fluorescent materials have emerged as particularly promising candidates due to their tunable optical responses, hidden features under normal light, and highly complex, multi-layered security signals that are difficult to replicate. In this review, we comprehensively summarize recent progress in fluorescence-based anti-counterfeiting technologies, classifying them into three major categories: organic, inorganic, and nanomaterial systems. Organic materials, including aggregation-induced emission luminogens, spiropyrans, single-benzene-based fluorophores, polymers, hydrogels, and proteins, offer versatile molecular design, high responsiveness to external stimuli, and biocompatibility, making them suitable for on-dose pharmaceutical security. Inorganic systems, such as metal complexes, metal–organic frameworks, and crystalline materials, provide long lifetimes, excellent thermal and photochemical stability, and multiparameter readouts. Nanomaterials, including quantum dots, carbon dots, nanoparticles, and nanoclusters, leverage size-dependent emission, surface functionalization, and multimodal properties to enable advanced and dynamic security patterns. Beyond the materials themselves, integration with printing, coating, fiber embedding, smartphone-based readers, and artificial intelligence-assisted detection highlights the translational potential of these approaches for real-world deployment. Emerging directions, such as multimodal fluorescence, physically unclonable functions, edible and biocompatible tags, and environmentally sustainable systems, further expand the scope of application. Collectively, this review provides a forward-looking framework that not only summarizes the current state of the art but also outlines future strategies for developing programmable, robust, and user-friendly fluorescent anti-counterfeiting technologies. Scope (computer science) ; Biocompatible material ; Leverage (statistics) ; Emerging technologies ; Identification (biology) Luminescence and Fluorescent Materials ; Carbon and Quantum Dots Applications ; Molecular Sensors and Ion Detection