jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Robin Schoemaker | Chunning Sun | Davide Chiarugi | Theodore Tyrikos‐Ergas | Peter H. Seeberger
Source: ACS Central Science (review, 2025-07-02)
Field: Materials Science — Materials Chemistry

Chemistries Moonshot: An Entirely Recyclable Car Automobiles depend on fossil resources - both to create the device and to power it. The automotive industry has decreased this dependency on fossil fuels by developing more fuel-efficient combustion engines, lightweight designs, and biofuels. The rise of battery electric vehicles (BEVs) offers the chance to reduce the fossil footprint by avoiding fuel combustion and exhaust emission. Disruptive approaches toward a truly sustainable car are far from being market-ready. To reach a completely sustainable car, the automotive industry must address the carbon footprint of material production, which is based in the chemical sector. The automotive and chemical industries have to adopt closed-loop thinking, utilize renewable resources for biodegradables, as well as develop novel materials and designs for efficient recycling. Disruptive approaches can arise from predictive models that can accelerate chemical research and enable the discovery of sustainable materials with desirable recycling properties. Integrating generative artificial intelligence (AI) with high-throughput experimental validation will shorten material development cycles and advance the transition to more sustainable products. Moving toward a fully recyclable car is aligning research and development efforts from the chemical sector to the automotive industry and beyond, presenting a giant leap toward a circular economy. Computer science Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Fuel Cells and Related Materials
Authors: Hun Hee Cho | Tae Hyung Kim | Seung Gyu Hwang | Hongchul Shin
Source: Journal of AI-Driven Communication Engineering (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

AI and Quantum Computing for Advanced Materials Design The AI-driven inverse design paradigm is fundamentally transforming materials discovery research by enabling the computational exploration of novel materials with predefined target properties. This review comprehensively synthesizes recent progress in applying AI methodologies, such as generative models, reinforcement learning, and diffusion models, to diverse material classes including metals, polymers, and proteins. It particularly highlights key advancements, such as the AI-guided discovery of high-entropy alloys with superior mechanical properties and the de novo design of functional polymers and protein-based biomaterials. Furthermore, major remaining challenges are discussed, including the computational-to-experimental validation gap, data scarcity, and the need for physically constrained models. Furthermore, this review explores the emerging frontier of Quantum Machine Learning (QML), which holds the promise of overcoming the limitations of classical computing for particularly complex problems in materials simulation. Finally, the integration of these methodologies into fully autonomous laboratories for closed-loop design, synthesis, and characterization is presented as a transformative route to accelerate the materials discovery cycle. Transformative learning ; Generative grammar ; Key (lock) ; Characterization (materials science) ; Data-driven ; Reinforcement learning Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; High Entropy Alloys Studies
Authors: R ANWAR | Muhammad Yaseen | Waqas Khan | Mohammad Ameen | Osama Khalil | Sidra Parveen | Muhammad Yousaf | Noreen Fatima | Mahnoor Awan
Source: Saudi Journal of Engineering and Technology (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Engineering Next-Generation Hybrid Nanomaterials, From Advanced Sensors to Sustainable Catalytic Processes The hybrid nanomaterials represent a revolutionary type of engineered structure that lies on the boundary of the chemistry and materials science and nanotechnology. With a combination of discrete organic-inorganic, metallic-polymeric, or bio-inspired constituents at the nanoscale, the systems are strongly synergistic in terms of physicochemical properties, and by far outperform their individual components. This structural and functional tunability has provided new opportunities in sensor technology, energy conversion and sustainability in catalysis that has never been seen before. Recent progress in interfacial design, atomic-level assembly, as well as nanoscale characterization, have made it possible to highly tune charge dynamics, surface reactivity, and selective molecular recognition. In sensor devices, the hybrid nanomaterials have excellent sensitivity and signal fidelity due to the property of the designs of heterostructures engineering and quantum confinement. Likewise, their hierarchic structures and functional active sites enable efficient energy capture, pollutant reduction as well as green chemical reactions in catalytic systems at ambient conditions. The overlap between artificial intelligence, computational modeling and green synthesis protocols is also rapidly increasing the rational designing of hybrid nanomaterials to be used in sustainable technology applications. It is a review that critically evaluates the new synthesis strategies, structure-property correlations, and multifunctional uses of next-generation hybrid nanomaterials, and shows the progress along with the unfulfilled opportunities of providing scalable, environmentally responsible production. Lastly, the future directions are suggested to a new paradigm of adaptive, circular-economy-oriented design based on integrating efficiency, durability, and ecological compatibility in the state-of-the-art materials engineering. Nanomaterials ; Rational design ; Hybrid system ; Compatibility (geochemistry) ; Sustainability ; Efficient energy use ; Hybrid material ; Catalysis Machine Learning in Materials Science ; Laser-Ablation Synthesis of Nanoparticles ; Chemical and Physical Properties of Materials
Authors: Naoki Yoshida | Yutaro Iwabuchi | Yasuhiko Igarashi | Yuma Iwasaki
Source: npj Computational Materials (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Networking autonomous material exploration systems through transfer learning Abstract Autonomous material exploration systems that integrate robotics, material simulations, and machine learning have advanced rapidly in recent years. Although their number continues to grow, these systems currently operate in isolation, limiting the overall efficiency of autonomous material discovery. In analogy to how human researchers advance materials science by sharing knowledge and collaborating, autonomous systems can also benefit from networking and knowledge exchange. Here, we propose a framework in which multiple autonomous material exploration systems form a network via transfer learning, selectively utilizing relevant knowledge from other systems in real time. We demonstrate this approach using three distinct autonomous systems and show that such networking significantly enhances the efficiency of material discovery. Our results suggest that the proposed framework can enable the development of large-scale autonomous material exploration networks, ultimately accelerating progress in material development. Limiting ; Analogy ; Knowledge transfer ; Autonomous system (mathematics) ; Complex system ; Variety (cybernetics) Machine Learning in Materials Science ; Advanced Neural Network Applications ; Modular Robots and Swarm Intelligence
Authors: Serguei P. Murzin
Source: Processes (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Artificial Intelligence-Guided Pulsed Synthesis of Zinc Oxide Nanostructures on Thin Metal Shells Zinc oxide (ZnO) nanostructures have been intensively investigated for applications in sensing, photocatalysis, and optoelectronic devices, where functional performance is strongly governed by morphology, crystallinity, and defect structure. Conventional wet-chemical and vapor-phase growth methods often require long processing times or complex chemistries and face reproducibility and compatibility challenges when applied to thin, flexible, or curved metallic substrates. Pulsed high-energy techniques—such as pulsed laser deposition (PLD), high-power impulse magnetron sputtering (HiPIMS), and pulsed laser or plasma processing—offer a versatile alternative, enabling rapid and localized synthesis both from and on Zn-bearing thin shells. These methods create transient nonequilibrium conditions that accelerate oxidation and promote spatially controlled nanostructure formation. This review highlights the emerging integration of artificial intelligence (AI) with pulsed ZnO synthesis on thin metallic substrates, emphasizing standardized data reporting, Bayesian optimization and active learning for efficient parameter exploration, physics-informed and graph-based neural networks for predictive modeling, and reinforcement learning for adaptive process control. By connecting synthesis dynamics with data-driven modeling, the review outlines a path toward predictive and autonomous control of ZnO nanostructure formation. Future perspectives include autonomous experimental workflows, machine-vision-assisted diagnostics, and the extension of AI-guided pulsed synthesis strategies to other functional metal oxide systems. Nanostructure ; Thin film ; Sputter deposition ; Pulsed laser deposition ; Oxide ; High-power impulse magnetron sputtering ; Nanoparticle ; Laser Machine Learning in Materials Science ; ZnO doping and properties ; Laser-Ablation Synthesis of Nanoparticles
Authors: Excellence Amaibiam Okonkwo | David Umahi | Kennedy Izuchukwu Ogunwa | Linda Mgbo Ngumoha
Source:  (article, 2025-05-04)
Field: Materials Science — Materials Chemistry

Advancement of Artificial Intelligence in Chemical Sciences Artificial Intelligence (AI) is now being used in many areas of chemical sciences ranging from pharmaceutical chemistry, nanotechnology, analytical chemistry: faster and more precise compound identification in organic chemistry and easier complex organic synthesis. Integration of AI technologies in chemical sciences has enhanced reaction prediction, material design, drug discovery, and sustainability efforts. AI in chemical sciences has brought about a lot of transformation which is very essential for addressing global health, energy and environment challenges in line with sustainable development goals (SDGs). However, limitations such as data unavailability, difficult model interpretation, unreliability and scalability remain areas for further studies. This paper discusses the recent advancements, applications and future directions of AI in chemical sciences, highlighting its roles in shaping the future of chemical sciences. Identification (biology) ; Scalability ; Sustainability ; Chemical technology ; Chemical industry ; Applications of artificial intelligence Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Bhuvaneshwari S Baranwal | Loyd A. Jones | Ankush Maind | Umesh Balande | Dipratn G. Khandare | Hina Goyal | Leilani Lotti Díaz | Julian Ivanov | Qiongqiong Angela Zhou
Source: ChemRxiv (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

The AI Revolution in Chemistry: Shaping the Future of Materials and Biomedical Sciences Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research, and materials science. Our data-supported trend analysis finds that since 2020, AI-related publications have surged across both academic and industrial domains. Traditional machine learning (ML) methods, such as decision trees, random forests, and support vector machines, remain foundational, particularly in molecular property prediction and classification tasks. However, there is an increased adoption of advanced neural network architectures, including graph neural networks for molecular representation and generative models for de novo compound design. Hybrid and ensemble approaches that integrate traditional algorithms with deep learning are increasingly favored for their robustness and adaptability. Large Language Models (LLMs) like GPT, BERT, and GEMINI are transforming literature mining, hypothesis generation, and experimental planning. Domain-specific models such as AlphaFold and chemical language models enable breakthroughs in protein structure prediction and reaction outcome forecasting. Industrial chemistry and chemical engineering are increasingly contributing to AI-driven academic publications, while biochemistry patents have grown eightfold, reflecting AI’s dual impact on discovery and commercialization. Despite these advances, challenges remain, particularly in data privacy, quality, interpretability of black-box models, and uncertainty quantification. These findings highlight AI’s transition from descriptor-based approaches to end-to-end learning systems, offering essential insights for researchers and policymakers navigating its transformative role in science. Nanotechnology ; Data science ; Engineering ethics ; Chemistry ; Engineering ; Computer science ; Materials science Machine Learning in Materials Science
Authors: Hyuk Jun Yoo | Daeho Kim | Sehyuk Yim | Sang Soo Han
Source: ChemRxiv (preprint, 2025-06-26)
Field: Materials Science — Materials Chemistry

NanoChef: AI Framework for Simultaneous Optimization of Synthesis Sequences and Reaction Conditions in 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 ; Process engineering ; Engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Shan He | Tong Luo | Xiaoe Chen | David James Young | Matt Jellicoe
Source: Nanomaterials (review, 2025-04-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Developments in Automated Reactors for Plasmonic Nanoparticles Automated reactors are transforming nanomaterial synthesis by enabling precise, multistep control over morphology and reaction pathways. This review discusses recent advancements in robotic batch and continuous-flow platforms, highlighting their role in expanding chemical space exploration and adaptive manufacturing. Despite progress, challenges remain in integrating automation for complex, multistep syntheses due to the intricate interplay of chemical and physical processes. Emerging process analytical technologies and advanced control software are enhancing real-time monitoring, adaptive feedback loops, and self-optimizing synthesis strategies. We categorize these developments, emphasizing their impact on plasmonic nanomaterial fabrication and outlining future directions for autonomous synthesis. Automation ; Nanotechnology ; Computer science ; Process (computing) ; Process control ; Chemical process ; Systems engineering ; Engineering ; Materials science ; Mechanical engineering Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials for catalytic reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Annower Molla
Source:  (preprint, 2025-07-21)
Field: Materials Science — Materials Chemistry

Data Driven Strategies for the Discovery and Optimization of Next Generation Energy Materials The world is shifting toward clean and sustainable energy, and energy materials that are efficient, stable, and economically viable are in demand at an unprecedented rate. Traditionally, the search for these materials required 10-20 years of painstaki Energy (signal processing) ; Data science ; Computer science ; Physics Machine Learning in Materials Science
Authors: Mohammad Nazeri | Sheldon Mei | Jeffrey Watchorn | Alex Ray Zhang | Erin Ng | Tao Wen | Abhijoy Mandal | Kevin Golovin | Alán Aspuru-Guzik | Frank Gu
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

RAISE: A self-driving laboratory for interfacial property formulation discovery Surface wettability is a critical design parameter for biomedical devices, coatings, and textiles. Contact angle measurements quantify liquid-surface interactions, which depend strongly on liquid formulation. Herein, we present the Robotic... Wetting ; Property (philosophy) ; Contact angle ; Work (physics) Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Adhesion, Friction, and Surface Interactions
Authors: Xinli Guo | Yanmei Zheng
Source: Journal of Advanced Materials Research (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Inaugural Editorial of the Journal of Advanced Materials Research This editorial introduces the inaugural issue of the Journal of Advanced Materials Research and presents its vision, scope, and mission in advancing sustainable and innovative materials research. Original research ; Basic research ; Research council ; Editorial board Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Nanoporous metals and alloys
Authors: Nagababu Andraju
Source: ChemRxiv (review, 2025-08-12)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Scalable Materials Synthesis and Manufacturing: A Comprehensive Review While artificial intelligence (AI) has transformed materials discovery, the primary bottleneck to technological impact remains the transition from lab-scale synthesis to robust, industrial-scale manufacturing. Most promising materials perish in this depth, which is referred as the "valley of death." The current review consolidates and critically evaluates the emerging ecosystem of AI-driven strategies and frameworks designed specifically to bridge this "lab-to-fab" gap. The review shifts our attention from property prediction to the engineering-driven problems of manufacturability. Further, the review discusses the main obstacles to scaling the production of materials, such as reproducibility, process optimization in the context of uncertainty, and techno-economic viability, as well as the AI approaches being developed to overcome them. This includes Natural Language Processing (NLP) for method extraction, graph neural networks for reaction modeling, reinforcement learning for process control, Bayesian optimization for defining process windows, and integrated AI-Techno-Economic Analysis (TEA) frameworks. The article concludes in a forward-looking roadmap for the future of AI in chemical and materials engineering. The proposed conclusion is defined by a paradigm shift from simply finding new materials to creating viable, economical, and scalable pathways to produce them, thereby enabling a new era of synthesis-aware materials innovation. Scalability ; Computer science ; Manufacturing engineering ; Engineering ; Nanotechnology ; Materials science ; Database Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Process Optimization and Integration
Authors: S. Sudheer Khan | Faizan Ali | Waheed Zaman Khan | Muhammad Adnan | Raja Muhammad Jawad Naveed | Badar Rasool | Ulfat Ayub | Muhammad Adeel | Muhammad Raza Malik
Source: Scholars Journal of Physics Mathematics and Statistics (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

AI-Driven and Quantum-Informed Design of Functional Nanomaterials: Bridging Catalysis, Drug Discovery, and Sustainable Environmental Remediation The convergence of machine intelligence, quantum-accurate simulation, and laboratory automation is reshaping how functional nanomaterials are conceived, validated, and deployed across chemistry, medicine, and environmental engineering. This review synthesizes an end-to-end “data-to-device” framework for the AI-driven and quantum-informed design of nanomaterials that bridges three application pillars: (i) green catalysis for clean energy and circular chemistry, (ii) drug discovery and nano-enabled therapeutics, and (iii) sustainable environmental remediation. We survey inverse-design workflows that combine generative models, uncertainty-aware predictors, Bayesian optimization, and active learning with electronic-structure engines (DFT, GW/BSE), free-energy methods (FEP/TI), and machine-learned interatomic potentials to span accuracy–throughput trade-offs via multi-fidelity strategies. On the materials side, we map tunable design spaces single-atom catalysts, 2D/defect-engineered surfaces, porous frameworks (MOFs/COFs), quantum dots, membranes, and bio-hybrids linking structure, defects, and interfacial physics to catalytic turnover, molecular recognition, transport, and durability. For catalysis, we outline pipelines that couple adsorption-energy maps and microkinetics to target CO₂ reduction, OER/ORR, and selective oxidations; for therapeutics, we integrate target modeling, generative ideation, physics-based ΔG estimation, and ADMET triage with synthesis-aware constraints; for remediation, we align pollutant fingerprints with adsorption, photocatalysis, electrocatalysis, and membrane routes while tracking leaching and secondary byproducts. Throughout, we emphasize rigorous reporting reproducible data splits, calibrated uncertainty, and minimum information for models and experiments together with life-cycle assessment, techno-economic analysis, and green-chemistry metrics (e.g., PMI, E-factor) to ensure net-positive impact. We close with a roadmap for closed-loop, se Bridging (networking) ; Workflow ; Generative grammar ; Interoperability ; Environmental remediation ; Sustainability ; Automation Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Ammonia Synthesis and Nitrogen Reduction