jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yijing Song | Dongqing Liu | Haifeng Cheng | Jia Yan | Mei Zu | Tingting Shi | Kaixuan Ding | Wenxia Zhang | Zhuo Chen | Yan Huang | Zhen Meng
Source: Materials & Design (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Active learning-enhanced yttrium oxyhydride thin films for photoinduced insulator-to-metal transitions Yttrium oxyhydride thin films show promise for photoinduced insulator-to-metal transition (PIMT) applications, exhibiting notable resistance reduction under illumination. However, the limited resistance modulation in polycrystalline films, primarily due to grain boundary scattering, restricts their full potential. High-quality epitaxial YH x O y films remains particularly challenging due to the complex, high-dimensional parameter space that governs the growth conditions. Herein, an initial dataset was constructed utilizing Optimal Latin hypercube sampling (OLHS) and magnetron sputtering technology, in order to systematically explore the five-dimensional parameter space and complete the experimental iterations. A closed-loop optimization framework was subsequently developed through Bayesian optimization, incorporating adaptive acquisition strategies that facilitated dynamic transitions between exploration and exploitation phases, coupled with an efficient Pareto frontier calculation approach—the Convex Hull Approximation Method (CHAM). The active learning-assisted multi-objective optimization strategy demonstrated superior experimental efficiency compared to conventional random sampling approaches, achieving an approximately 20-40 fold reduction in all experimental iterations. Optimized YH x O y thin films exhibited well-defined epitaxial orientation on CaF 2 (111) substrates, with a six-order-of-magnitude resistance modulation and metallic behavior (d ρ /dT > 0) under illumination, demonstrating robust photoinduced insulator-to-metal transition behavior. This material exhibits considerable potential for integration into advanced optoelectronic devices, smart window technologies, and next-generation non-volatile memory systems. • Active learning reduced experimental iterations by 20-40 times using dynamic acquisition strategy. • Convex Hull Approximation Method enables fast Pareto front area calculation. • Quasi-epitaxial growth enabling robust photoinduced insulator-to-metal transitions. • Optimized films show 6-order-of-magnitude resistance reduction and metallic state under illumination. Materials science ; Yttrium ; Thin film ; Metal ; Optoelectronics ; Nanotechnology ; Engineering physics ; Metallurgy ; Oxide Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Oleksandr Stroyuk | Oleksandra Raievska | Dietrich R T Zahn | Christoph J. Brabec
Source: Nano Futures (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Accelerated discovery of multinary chalcogenide quantum dots: combining aqueous chemistry with high-throughput experimentation and machine learning Abstract Multinary chalcogenide quantum dots (MCQDs) exhibit unprecedented variability in composition and properties, size tunability, and high tolerance to multiple alloying, doping, and deviations from stoichiometry. This variability enables the synthesis of hundreds of thousands of MCQDs, characterized by a wide range of composition- and size-dependent spectral and photophysical properties, with a high potential for optoelectronic applications. At that, the whole compositional richness of MCQDs can be readily accessed using sustainable aqueous chemistry. The present Perspective focuses on the challenges of navigating the vast compositional space of MCQDs to discover new optoelectronic materials for the absorption, emission, and conversion of light. We argue that the exploration of the compositional versatility of MCQDs requires accelerated research, going beyond the conventional intuition-driven experiments. The acceleration can be achieved by high-throughput parallelized experimentation that yields extensive datasets and enables machine-learning-driven data analysis and automation of the targeted discovery of new MCQDs. Chalcogenide ; Quantum dot ; Quantum ; Range (aeronautics) ; Perspective (graphical) ; Quantum computer Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications ; Machine Learning in Materials Science
Authors: Hyuk Jun Yoo | Daeho Kim | Sehyuk Yim | Sang Soo Han
Source: ChemRxiv (preprint, 2025-07-01)
Field: Materials Science — Materials Chemistry

NanoChef: AI Framework for Simultaneous Optimization of Synthesis Sequences and Reaction Conditions at Autonomous Laboratories We present NanoChef, a deep learning-based framework for the simultaneous optimization of synthesis sequences and reaction conditions in autonomous laboratories. Unlike traditional AI models focused solely on continuous variables, NanoChef incorporates positional encoding and MatBERT embedding to represent reagent sequences as vectorized inputs. This enables joint modeling of categorical and continuous variables in nanoparticle (NP) synthesis. In virtual experiments, NanoChef consistently identified global optima across synthesis-order-sensitive landscapes, requiring fewer than 40 cycles. For real-world Ag NP synthesis with a λmax of 513 nm by UV‒Vis absorption spectroscopy and high monodispersity, the framework outperformed fixed-order methods, achieving a 32% reduction in the full width at half maximum (FWHM) and reaching optimal recipes within 100 experiments. Extending to a three-reagent system, NanoChef newly discovered an oxidant‒last strategy that yielded the most uniform NPs. This work redefines synthesis order as a tunable design variable and demonstrates how lightweight AI architecture can accelerate autonomous chemistry. Computer science Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Cher Tian Ser | Han Hao | Sergio Pablo‐García | Kjell Jorner | Shangyu Li | Robert Pollice | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2025-01-27)
Field: Chemistry — Organic Chemistry

Bulky phosphine ligands promote palladium-catalyzed protodeboronation The Suzuki-Miyaura cross-coupling reaction is plagued by protodeboronation, an undesirable side reaction with water that consumes the boronic acid derivatives required for the cross-coupling reaction. Meticulous mechanistic studies have previously established protodeboronation to be highly sensitive to the nature of the boronic reagent and reaction conditions. Particularly, the presence of bases, which are essential for the Suzuki-Miyaura coupling, is known to catalyze protodeboronation. However, protodeboronation catalyzed by palladium-phosphine complexes, the benchmark catalyst system for Suzuki-Miyaura cross-coupling, has been largely overlooked. We demonstrate, using automated high-throughput experimentation, comprehensive computational mechanistic analyses and kinetic modeling, that protodeboronation is accelerated by palladium(II) complexes bound to bulky phosphine ligands. While sterically hindered ligands are typically used to facilitate difficult cross-couplings, these ligands can instead paradoxically impede cross-coupling product formation, requiring careful and judicious consideration when choosing ligands for Suzuki-Miyaura cross-couplings. Phosphine ; Palladium ; Chemistry ; Catalysis ; Combinatorial chemistry ; Organic chemistry Organoboron and organosilicon chemistry ; Chemical Synthesis and Analysis ; Catalytic Cross-Coupling Reactions
Authors: Gaulthier Rydzek | Katsuhiko Ariga
Source: CrystEngComm (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Wet chemistry nanoarchitectonics for nanocrystal-based films Fabrication of nanocrystal thin films through wet chemical processes and their resulting functional properties are explored. Special attention is given to the dimensionality of pre-formed nanocrystals spanning 0D, 1D and 2D, and its influence on the resulting films. Nanocrystal ; Nanotechnology ; Materials science ; Chemistry X-ray Diffraction in Crystallography
Authors: Santiago Miret | Kin Long Kelvin Lee | Carmelo Gonzales | Sajid Mannan | N. M. Anoop Krishnan
Source: ArXiv.org (preprint, 2025-02-05)
Field: Materials Science — Materials Chemistry

Energy &amp; Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Universal Machine Learning Interactomic Potentials (MLIPs) enable accelerated simulations for materials discovery. However, current research efforts fail to impactfully utilize MLIPs due to: 1. Overreliance on Density Functional Theory (DFT) for MLIP training data creation; 2. MLIPs' inability to reliably and accurately perform large-scale molecular dynamics (MD) simulations for diverse materials; 3. Limited understanding of MLIPs' underlying capabilities. To address these shortcomings, we aargue that MLIP research efforts should prioritize: 1. Employing more accurate simulation methods for large-scale MLIP training data creation (e.g. Coupled Cluster Theory) that cover a wide range of materials design spaces; 2. Creating MLIP metrology tools that leverage large-scale benchmarking, visualization, and interpretability analyses to provide a deeper understanding of MLIPs' inner workings; 3. Developing computationally efficient MLIPs to execute MD simulations that accurately model a broad set of materials properties. Together, these interdisciplinary research directions can help further the real-world application of MLIPs to accurately model complex materials at device scale. Energy (signal processing) ; Regression ; Regression analysis ; Computer science ; Physics ; Machine learning ; Mathematics ; Statistics ; Quantum mechanics Machine Learning in Materials Science
Authors: Faezeh Hajiali | Naoko Ellis | R. Bhushan Gopaluni
Source: npj Computational Materials (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

From biomass waste to CO2 capture: a multi-fidelity machine learning workflow for high-throughput screening of activated carbons Rising atmospheric CO<sub>2</sub> levels threaten climate stability, demanding transformative solutions in carbon capture, utilization, and storage. Porous activated carbons (ACs) derived from sustainable waste sources offer a promising route for cost-effective and eco-friendly carbon capture, thanks to their tunable surface chemistry and high surface areas. However, optimizing ACs for peak CO<sub>2</sub> uptake is often hindered by complex, resource-intensive experimental workflows and the scarcity of high-quality data. This study presents a machine learning-driven framework that combines a multi-headed one-dimensional convolutional neural network (MH1DCNN) with multi-fidelity Bayesian optimization (MFBO) to efficiently navigate large design spaces by balancing exploration of uncertain regions with exploitation of known high-performing candidates. The MH1DCNN captures nonlinear relationships between physicochemical properties and CO<sub>2</sub> uptake, serving as a deployable low-fidelity model. Using 841 literature-reported samples as high-cost, high-fidelity data and MH1DCNN-generated predictions as low-cost, low-fidelity evaluations, MFBO fuses these information sources through a probabilistic surrogate model, enabling rapid and cost-effective optimization. This approach reduces high-fidelity evaluation requirements by over 75% and identifies top-performing candidates using only 13 high-fidelity acquisitions. This scalable, data-driven strategy supports the development of closed-loop experiment-analysis-planning systems for future autonomous laboratories and accelerates sustainable materials discovery. Workflow ; Biomass (ecology) ; Convolutional neural network ; Probabilistic logic ; Artificial neural network ; Scarcity ; Throughput Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Christian Cobos | Xiang Yan | Marisol Martín‐González
Source: Macromolecular Materials and Engineering (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

3D Polymeric Nanonetworks: From Self‐Assembly to Advanced Fabrication ABSTRACT Three‐dimensional polymeric nanonetworks combine high surface area, interconnected porosity, and tunable mechanics to enable advanced functions in catalysis, sensing, energy storage, and biomedicine. While existing reviews focus on individual fabrication techniques, this work provides the first systematic cross‐method comparison and practical decision framework for method selection. We evaluate five fundamentally different fabrication strategies—block copolymer self‐assembly, hyper‐crosslinking, template‐assisted methods, 3D printing, and nanolithography—across four critical metrics: resolution, throughput, scalability, and material compatibility. Unlike method‐specific reviews, this work presents a quantitative decision matrix that operationalizes these metrics for application‐driven method selection, bridging the gap between laboratory capabilities and industrial requirements. We showcase hybrid approaches that integrate multiple techniques (e.g., polymerization‐induced phase separation with vat photopolymerization) to achieve hierarchical structures combining nanoscale precision with manufacturability. The review includes a concise primer on block copolymer self‐assembly fundamentals (χ N , segregation strength; ƒ, volume fraction), updated polymer topology terminology, and quantified performance envelopes for photopolymerization and two‐photon techniques. Critically, we address real‐world translation challenges—scalability bottlenecks, defect control, device integration, and material limitations—that are often overlooked in technique‐focused reviews. Finally, we discuss future trends in eco‐friendly, scalable fabrication and AI‐driven design tools to accelerate the translation of 3D polymeric nanonetworks into practical applications. By providing integrated cross‐method guidance rather than isolated technique descriptions, this review enables researchers to navigate the complex fabrication landscape and select optimal strategies for their specific performance targets. Fabrication ; Bridging (networking) ; Scalability ; Template ; Nanoscopic scale ; Focus (optics) ; Polymer Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Alaaldin Adam | Raymond B. Donatus | Emmanuel Ba’aku AttahDaniel | Hyelalibiya Ataitiya | O. J Ewenifa | Musa Y. Abubakar | Abubakar M. Shittu
Source: Journal of Computational Systems and Applications (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

AI-Driven Computational Insights into Electrochemical Energy Storage: A Review of Emerging Trends in Battery Chemistry Modern battery technologies depend on electrochemical storage systems. However, the performance of such systems is limited by complicated multi-scale processes. These processes include transport of ions, interfacial reactions, and degradation processes. These coupled physicochemical dynamics across scales cannot be captured accurately by traditional computational methods. This review analyzes more than 150 recently published studies (2018-2025) on the seamless integration of artificial intelligence like machine learning and deep learning and physics-informed hybrid models with standard computational chemistry frameworks. As materials discovery and performance optimization are increasingly accelerated by AI-powered methods, this review provides a systematic overview of their applications, successes, and challenges in electrochemical energy storage. AI-powered techniques are speeding the discovery of high-performing electrode materials (Ni-rich cathodes, solid-state electrolytes). AI can increase battery lifetime prediction accuracy by 30-50%. It can also lead to the development of digital twins for real-time monitoring and optimization of systems. Also, the review notes developing trends towards autonomous laboratories and self-optimizing battery systems, where AI connects data-driven insights to fundamental chemical understanding. Our study reveal the immense potential of AI in developing next-generation, sustainable, and circular electrochemical energy storage technologies. Further, they also highlight the challenges and research directions for effective deployment. Battery (electricity) ; Electrochemical energy storage ; Energy storage ; Computational model ; Energy (signal processing) ; Electrochemical energy conversion Machine Learning in Materials Science ; Advanced battery technologies research ; Advanced Battery Technologies Research
Authors: Long Zhao | Hongxiang Zong
Source: Advanced Materials (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

AI‐Driven Decoding of Material Dynamics: From Machine Learning Potentials and Interpretability to Generative Prediction Understanding and predicting the dynamic processes that underpin material performance are crucial for designing next-generation materials capable of meeting the evolving demands of modern technologies. These processes-often occurring at atomic or molecular scales in condensed phases-remain notoriously difficult to probe experimentally. Artificial intelligence (AI) now offers a transformative framework that enables unprecedented realism in modeling, interpreting, and even generating multiscale dynamics under various external conditions. In this Review, we highlight recent advances in AI-based machine learning potentials, AI-guided interpretability, and generative AI for dynamic prediction, and demonstrate their applications to key challenges in materials science, including phase transitions in transforming materials and plastic deformation in metallic structural materials. Finally, we discuss the remaining challenges and outline future opportunities, aiming to inspire the development of AI-powered frameworks that can probe atomic-level dynamics and accelerate materials design. Interpretability ; Generative grammar ; Transformative learning ; Key (lock) ; Decoding methods ; Generative model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Electron Microscopy Techniques and Applications
Authors: Zheng Jie Liew | Ziad Elkhaiary | Alexei A. Lapkin
Source: npj Computational Materials (article, 2025-05-31)
Field: Materials Science — Materials Chemistry

Parameter efficient multi-model vision assistant for polymer solvation behaviour inference Polymer-solvent systems exhibit complex solvation behaviours encompassing a diverse range of phenomena, including swelling, gelation, and dispersion. Accurate interpretation is often hindered by subjectivity, particularly in manual rapid screening assessments. While computer vision models hold significant promise to replace the reliance on human evaluation for inference, their adoption is limited by the lack of domain-specific datasets tailored, in our case, to polymer-solvent systems. To bridge this gap, we conducted extensive screenings of polymers with diverse physical and chemical properties across various solvents, capturing solvation characteristics through images, videos, and image-text captions. This dataset informed the development of a multi-model vision assistant, integrating computer vision and vision-language approaches to autonomously detect, infer, and contextualise polymer-solvent interactions. The system combines a 2D-CNN module for static solvation state classification, a hybrid 2D/3D-CNN module to capture temporal dynamics, and a BLIP-2-based contextualisation module to generate descriptive captions for solvation behaviours, including vial orientation, solvent discolouration, and polymer interaction states. Computationally efficient, this vision assistant provides an accurate, objective, and scalable solution in interpreting solvation behaviours, fit for autonomous platforms and high-throughput workflows in material discovery and analysis. Solvation ; Computer science ; Artificial intelligence ; Inference ; Machine learning ; Chemistry ; Solvent Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Computational Drug Discovery Methods
Authors: WeiNian Wong | Daniel Phillips | Taifur Rahman | Tanja Junkers
Source: Chemical Science (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Automated closed-loop continuous flow block copolymer synthesizer A fully automated continuous flow synthesizer for diblock copolymer (BCP) synthesis was constructed comprising elements of flow chemistry, automation, machine learning and in-line monitoring. A new method using in-line FTIR spectroscopic analysis for accurate determination of monomer conversion (with an error as low as 2% relative to an NMR spectroscopic baseline) is presented, thereby generating a reliable feedback system for reaction self-optimisation using the platform. By employing reversible addition-fragmentation chain transfer (RAFT) polymerization at 100 °C, acrylates and acrylamides of different hydrophilicities (namely methyl acrylate, ethyl acrylate, butyl acrylate, 2-ethylhexyl acrylate, 2-hydroxyethyl acrylate, ethylene glycol methyl ether acrylate, diethylene glycol ethyl ether acrylate, 2-(dimethylamino)ethyl acrylate, acrylamide & <i>N</i>,<i>N</i>-dimethylacrylamide) were polymerized to make mixed BCPs, targeting different degrees of polymerization (15 to 100). Samples were collected automatically, and a BCP material library comprising 95 diblock copolymers (7 sets of double hydrophobic, 7 sets of amphiphilic and 3 sets of double hydrophilic monomer systems) with <i>M</i> <sub>n</sub> ranging from 1800 g mol<sup>-1</sup> to 14 700 g mol<sup>-1</sup>, was obtained in a high-throughput manner, with minimal human intervention throughout the entire process. Copolymer ; Monomer ; Ethylene glycol ; Ether ; Polymerization ; Diethylene glycol Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Caleb Cox | Edward B. Pimentel | Beck Miller | Daniel J. Nesbitt | Justice L. Merrifield | Ethan M. Hartman | Tate A. Hancock | Robert T. Kennedy | Joshua J. Coon | Matthew S. Sigman | Jeffrey D. Martell
Source: ChemRxiv (article, 2025-08-13)
Field: Chemistry — Organic Chemistry

DNA-Scaffolded Ultrahigh-Throughput Reaction Screening Discovering and optimizing reactions is central to synthetic chemistry. However, chemical reactions are traditionally screened using relatively low-throughput methods, prohibiting exploration of diverse chemical space, particularly for reactions involving multiple components with millions of potential combinations. State-of-the-art technologies designed to increase screening throughput are limited to thousands of reactions and often require weeks to months for data collection. Here we report a DNA-encoded combinatorial screening platform, enabling large matrices of reactions to be performed in a single test tube followed by simultaneous reaction analysis using DNA sequencing. Using this platform, a single researcher performed 504,000 reactions in < 3 days, encompassing a variety of reaction conditions and times. We combined this platform with data science tools to design a targeted library covering chemical space broadly, then used the resulting dataset to train a machine learning model for both prediction and interpretation tasks, demonstrating utility for reaction development and mechanistic studies. Overall, this technology discloses fundamentally new opportunities in data-driven discovery, optimization, and mechanistic understanding of synergistic catalytic systems. Throughput ; DNA ; High-throughput screening ; Computational biology ; Computer science ; Nanotechnology ; Chemistry ; Biology ; Genetics ; Materials science ; Operating system Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Advanced Biosensing Techniques and Applications