jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jacek Diakun | Rafał Grzejda
Source: Applied Sciences (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Product Design Analysis with Regard to Recycling and Selected Mechanical Properties The design process is a complex task in which different goals and properties have to be achieved. Nowadays, end-of-life issues are increasingly being considered in addition to typical design properties such as durability, appearance or quality. This article presents the product design process in relation to its recycling and mechanical properties. A plate connection in two design versions was chosen as the product: in the form of a multi-bolted connection and a multi-riveted connection. An analysis was conducted for several variants of these connections. Recycling properties were considered using various measures calculated from the Recycling Product Model, a type of product model that includes its recycling properties. Selected mechanical properties were determined using the Finite Element Method. Removing one bolt from the connection resulted in a stiffness reduction of almost 11%, while removing two bolts from the connection resulted in a stiffness reduction of almost 26%. In contrast, the removal of one rivet from the connection led to a stiffness reduction of about 3%, while the removal of two rivets from the connection led to a stiffness reduction of less than 5%. Rivet ; Stiffness ; Connection (principal bundle) ; Structural engineering ; Finite element method ; Reduction (mathematics) ; Product (mathematics) ; Durability ; Engineering ; Product design ; Process (computing) ; Mechanical engineering ; Materials science ; Computer science ; Composite material ; Mathematics Material Selection and Properties ; Mechanical and Thermal Properties Analysis ; Manufacturing Process and Optimization
Authors: Haolin Fan | Zhen Fan | C.S. Liu | Jianhao Zhu | Tom Gibbs | Jerry Ying Hsi Fuh | Wen Feng Lu | Bingbing Li
Source: npj Advanced Manufacturing (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

MetalMind: A knowledge graph-driven human-centric knowledge system for metal additive manufacturing Abstract In the Industry 5.0 era, increasing manufacturing complexity and fragmented knowledge pose challenges for decision-making and workforce development. To tackle this, we present a human-centric knowledge system that integrates explicit knowledge from formal sources and implicit knowledge from expert insights. The system features three core innovations: (1) an automated KG construction pipeline leveraging large language models (LLMs) with collaborative verification to enhance knowledge extraction accuracy and minimize hallucinations; (2) a hybrid retrieval framework that combines vector-based, graph-based, and hybrid retrieval strategies for comprehensive knowledge access, achieving a 336.61% improvement over vector-based retrieval and a 68.04% improvement over graph-based retrieval in global understanding; and (3) an MR-enhanced interface that supports immersive, real-time interaction and continuous knowledge capture. Demonstrated through a metal additive manufacturing (AM) case study, this approach enriches domain expertise, improves knowledge representation and retrieval, and fosters enhanced human-machine collaboration, ultimately supporting adaptive upskilling in smart manufacturing. Knowledge graph ; Knowledge management ; Computer science ; Graph ; Business ; Process management ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Digital Transformation in Industry
Authors: Jiaxuan Qiu | Liwen Zhu | Yufeng Zhang | Zhongzhong Luo | Longlu Wang
Source: Chemical Communications (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

A revolutionary paradigm in chemistry and materials science research: self-driving laboratories A self-driving laboratory (SDL), an automated experimental platform that integrates machine learning algorithms and robotics, has the potential to revolutionize traditional research methods and accelerate progress in chemistry and materials science. However, despite its significant potential, the development and practical application of SDLs have not yet met expectations. To date, only a limited number of SDLs have been successfully constructed and applied in research, mainly due to the challenges involved in fostering interdisciplinary collaboration and achieving high levels of system integration. In this review, we present a comprehensive overview of recent SDL applications in chemistry and materials science. Firstly, SDLs used for reaction optimization are discussed, including the refinement of photocatalytic reaction conditions and the exploration of Pareto Frontiers in homogeneous catalysis. Next, their role in materials synthesis is examined, focusing on the production of optical materials and catalytic materials. Additionally, SDLs' application in mechanistic investigation is introduced, such as the identification and study of molecular electrochemical mechanisms. Finally, we highlight the key challenges that must be considered and overcome to facilitate the advancement and wider implementation of SDLs. Nanotechnology ; Chemistry ; Mechanism (biology) ; Paradigm shift ; Engineering physics ; Engineering ethics ; Materials science ; Engineering ; Epistemology ; Philosophy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Vinamr Jain | Zhilong Wang | Fengqi You
Source: Materials Horizons (article, 2025-11-15)
Field: Materials Science — Materials Chemistry

Machine learning pipelines for the design of solid-state electrolytes The development of solid-state electrolytes (SSEs) is critical for enabling safer, high-energy-density batteries. However, the discovery of new inorganic SSEs is hindered by vast chemical search spaces, complex multi-property requirements, and limited experimental data, especially for multivalent systems. This review presents the first systematic framework mapping five interconnected challenges in SSE discovery to emerging AI solutions, providing a strategic roadmap for practitioners. We comprehensively survey machine learning pipelines from data resources and feature engineering to classical models, deep learning architectures, and cutting-edge generative approaches. Key breakthroughs include: (1) machine learning interatomic potentials enabling microsecond-scale molecular dynamics simulations at near-DFT accuracy, revealing non-Arrhenius transport behavior and overturning established transport mechanisms; (2) advanced neural network architectures achieving unprecedented accuracy in ionic conductivity prediction across diverse chemical spaces, including transformer-based and graph neural network approaches; (3) generative models successfully proposing and experimentally validating novel SSE compositions through diffusion-based design frameworks; and (4) autonomous closed-loop discovery platforms integrating ML predictions with experimental synthesis, achieving order-of-magnitude efficiency gains over traditional approaches. Unlike previous reviews focused on Li-ion systems, we explicitly address the critical data gap for multivalent conductors (Mg<sup>2+</sup>, Ca<sup>2+</sup>, Zn<sup>2+</sup>, Al<sup>3+</sup>) and provide concrete strategies through transfer learning and active learning frameworks. We bridge conventional computational methods (DFT, molecular dynamics) with modern ML techniques, demonstrating hybrid workflows that overcome individual limitations. The review concludes with actionable recommendations for multi-objective optimization, explainable AI implementation, and physics-informed model development, establishing a comprehensive roadmap for the next generation of AI-accelerated solid-state battery materials discovery. Deep learning ; Workflow ; Key (lock) ; Generative grammar ; Artificial neural network ; Bridge (graph theory) ; Feature (linguistics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies
Authors: Pengfei Huang | Wei‐Di Liu | Chenhua Sun | Zekun Li | Yu Wang | Yanan Chen
Source: Materials Genome Engineering Advances (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

Integration of materials science and artificial intelligence: From high‐throughput screening to autonomous laboratories Abstract Traditional methods for material discovery and optimization are time‐consuming and resource‐consuming. Recent advancements in artificial intelligence (AI), particularly machine learning, offer a revolutionary opportunity for accelerating novel material discovery. This review overviews AI enhancement on high‐throughput synthesis and screening methods for faster and more efficient material discovery, focusing on electrocatalysis and energy storage materials. The integration of AI with autonomous laboratories allows real‐time data analysis and closed‐loop optimization, accelerating material characterization and analysis. Despite challenges in data quality and model transparency, integration of AI with experimental workflows significantly advances materials science. Workflow ; Applications of artificial intelligence ; Quality (philosophy) ; Data integration ; Characterization (materials science) ; System integration Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Sarbajit Banerjee | Ying Shirley Meng | Andrew M. Minor | Minghao Zhang | Nestor J. Zaluzec | Maria K. Y. Chan | Gerald T. Seidler | David W. McComb | Joshua Agar | Partha P. Mukherjee | Brent C. Melot | Karena W. Chapman | Beth S. Guiton | Robert F. Klie | Ian McCue | Paul M. Voyles | Ian G. Robertson | Ling Li | Miaofang Chi | Joel F. Destino | Arun Devaraj | Emmanuelle A. Marquis | Carlo U. Segre | Huinan Liu | Judith C. Yang | Kasra Momeni | Amit Misra | Niaz Abdolrahim | Julia E. Medvedeva | Wenjun Cai | Alp Sehirlioglu | Melike Dizbay-Onat | Apurva Mehta | Lori Graham‐Brady | Benji Maruyama | Krishna Rajan | Jamie H. Warner | Mitra L. Taheri | Sergei V. Kalinin | B. Reeja-Jayan | Udo D. Schwarz | Sindee L. Simon | Craig M. Brown
Source: MRS Bulletin (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Materials laboratories of the future for alloys, amorphous, and composite materials Abstract In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a roadmap for realizing this vision; identify technical bottlenecks and barriers to access; and propose pathways to equitable and democratic access to integrated toolsets in a manner that addresses urgent societal needs, accelerates technological innovation, and enhances manufacturing competitiveness. Spanning three important materials classes, this article summarizes the areas of alignment and unifying themes, distinctive needs of different materials research communities, key science drivers that cannot be accomplished within the capabilities of current materials laboratories, and open questions that need further community input. Here, we provide a broader context for the workshop, synopsize the salient findings, outline a shared vision for democratizing access and accelerating materials discovery, highlight some case studies across the three different materials classes, and identify significant issues that need further discussion. Graphical abstract Context (archaeology) ; Salient ; Data science ; Computer science ; Product (mathematics) ; Nanotechnology ; Engineering ethics ; Engineering ; Materials science ; Artificial intelligence Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ion-surface interactions and analysis
Authors: Kunran Yang | Fanxing Li
Source: Energy & Environmental Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Computationally accelerated discovery of mixed metal compounds for chemical looping combustion and beyond High-throughput computational screening and machine learning accelerate the rational design of mixed metal compounds for diverse chemical looping applications, transforming materials discovery from trial-and-error to data-driven approaches. Chemical looping combustion ; Combustion ; Metal ; Work (physics) ; Key (lock) Catalytic Processes in Materials Science ; Thermal and Kinetic Analysis ; Catalysis and Oxidation Reactions
Authors: Emine Sezer | Fulden Ulucan‐Karnak | Sinan Akgöl
Source: TURKISH JOURNAL OF BIOLOGY (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Green carbon dots in the era of AI: sustainable synthesis, intelligent drug delivery, advanced diagnostics, and bioimaging This review provides a holistic roadmap, concluding that the AI- GCD synergy is indispensable for developing the next generation of smart nanomedicines. Future efforts must prioritize addressing scalability, standardization, and regulatory pathways to accelerate successful clinical translation. Sustainable development ; Drug ; Carbon fibers ; Drug development ; Sustainability Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: P. F. Hua | Zhen Huang | Zheyuan Xu | Qiang Zhao | Chuan Ye | Yi‐Feng Wang | Yun‐He Xu | Yao Fu | Hu Ding
Source: Communications Chemistry (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

An active representation learning method for reaction yield prediction with small-scale data Reaction optimization plays an essential role in chemical research and industrial production. To explore a large reaction system, a practical issue is how to reduce the heavy experimental load for finding the high-yield conditions. In this paper, we present an efficient machine learning tool called "RS-Coreset", where the key idea is to take advantage of deep representation learning techniques to guide an interactive procedure for representing the full reaction space. Our proposed tool only uses small-scale data, say 2.5% to 5% of the instances, to predict the yields of the reaction space. We validate the performance on three public datasets and achieve state-of-the-art results. Moreover, we apply this tool to assist the realistic exploration of the Lewis base-boryl radicals enabled dechlorinative coupling reactions in our lab. The tool can help us to effectively predict the yields and even discover several feasible reaction combinations that were overlooked in previous articles. Yield (engineering) ; Scale (ratio) ; Representation (politics) ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Geography ; Cartography Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Tong Wu | Jiawei Zhang | Qingyu Yan | Jingyu Wang | Hao Yang
Source: Membranes (review, 2025-06-11)
Field: Chemistry — Inorganic Chemistry

Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects Organic framework membranes (OFMs) have emerged as transformative materials for separation technologies due to their tunable porosity, structural diversity, and stability, yet their design and optimization face challenges in navigating vast chemical spaces and complex performance trade-offs. This review highlights the pivotal role of machine learning (ML) in overcoming these limitations by integrating multi-source data, constructing quantitative structure-property relationships, and enabling the cross-scale optimization of OFMs. Methodologically, ML workflows-spanning data construction, feature engineering, and model optimization-accelerate candidate screening, inverse design, and mechanistic interpretation, as demonstrated in gas separations and nascent liquid-phase applications. Key findings reveal that ML identifies critical structural descriptors and environmental parameters, guiding the development of high-performance membranes that surpass traditional selectivity-permeability limits. Challenges persist in liquid separations due to dynamic operational complexities and data scarcity, while emerging frameworks offer untapped potential. The integration of interpretable ML, in situ characterization, and industrial scalability strategies is essential to transition OFMs from laboratory innovations to sustainable, adaptive separation systems. This review underscores ML's transformative capacity to bridge computational insights with experimental validation, fostering next-generation membranes for carbon neutrality, water security, and energy-efficient industrial processes. Membrane ; Artificial intelligence ; Computer science ; Biochemical engineering ; Engineering ; Systems engineering ; Industrial engineering ; Machine learning ; Process engineering ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Fuel Cells and Related Materials
Authors: Yong Qiu | Lei Wang | Letian Chen | Yun Tian | Zhen Zhou | Jianzhong Wu
Source: Chemical Science (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

A mechanism-guided inverse engineering framework to unlock design principles of H-bonded organic frameworks for gas separation The diverse combinations of novel building blocks offer a vast design space for hydrogen-bonded organic frameworks (HOFs), rendering them highly promising for gas separation and purification. However, the underlying separation mechanism facilitated by their unique hydrogen-bond networks has not yet been fully understood. In this work, a comprehensive understanding of the separation mechanisms was achieved through an iterative data-driven inverse engineering approach established upon a hypothetical HOF database possessing nearly 110 000 structures created by a materials genomics method. Leveraging a simple yet universal feature extracted from hydrogen bonding information with unambiguous physical meanings, the entire design space was exploited to rapidly identify the optimization route towards novel HOF structures with superior Xe/Kr separation performance (selectivity > 10<sup>3</sup>). This work not only provides the first large-scale HOF database, but also demonstrates the enhanced machine learning interpretability of our model-driven iterative inverse design framework, offering new insights into the rational design of nanoporous materials for gas separation. Mechanism (biology) ; Metal-organic framework ; Inverse ; Nanotechnology ; Gas separation ; Biochemical engineering ; Computer science ; Systems engineering ; Chemistry ; Materials science ; Engineering ; Physics ; Organic chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Supramolecular Chemistry and Complexes
Authors: Li Tan | Weining Song | Nanjiang Chen | Qi Wang | Fu-dong Gao | Yalan Xing | Shengru Wu | Chao Song | Jinshan Li | Yu Liu | Shenghua Li | Congying Wu | Zhenyu Zhang
Source: Applied Sciences (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

The Artificial Intelligence-Driven Intelligent Laboratory for Organic Chemistry Synthesis The deep integration and application of artificial intelligence to organic chemistry are propelling the development of organic chemistry synthesis laboratories toward an intelligent automated laboratory model characterized by “hardware + software + AI”. This paper systematically explores the overall framework of AI-driven intelligent laboratories for organic chemistry synthesis, achieving automation and flexibility through standardized experimental integration workstations and intelligent scheduling and collaborative management of experimental resources. By leveraging multimodal databases, the integration of large models, machine learning, and other AI technologies enables AI-driven closed-loop intelligent chemical experiments, including product prediction, molecular retrosynthetic planning, and synthesis reaction optimization. The paper proposes a cloud-based shared operational model for chemical laboratories, aiming to achieve socialized sharing and intelligent matching of experimental resources, thereby facilitating the accumulation and sharing of chemical experimental data to promote the intelligent development of organic chemistry synthesis experiments. Practical cases of building intelligent chemical laboratories are shared, providing paths for technology implementation in constructing the next generation of automated and intelligent chemical laboratories. Computer science ; Automation ; Workstation ; Flexibility (engineering) ; Software engineering ; Artificial intelligence ; Systems engineering ; Engineering ; Operating system Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Yuzhi Xu | Haowei Ni | Fanyu Zhao | Qiang Gao | Ziqing Zhao | Chia-Hua Chang | Yanran Huo | Shiyu Hu | Yike Zhang | Radu Grovu | Hermione He | John Z. H. Zhang | Yuanqing Wang
Source: AIP Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Molecular dynamics and machine learning unlock possibilities in beauty design—A perspective Computational molecular design—the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches—has been widely applied to create valuable new molecular entities, from small molecule therapeutics to protein biologics. In the small data regime, physics-based approaches model the interaction between the molecule being designed and proteins of key physiological functions, providing structural insights into the mechanism. When abundant data have been collected, a quantitative structure–activity relationship can be more directly constructed from experimental data, from which machine learning can distill key insights to guide the design of the next round of experiment design. Machine learning methodologies can also facilitate physical modeling, from improving the accuracy of force fields and extending them to unseen chemical spaces to more directly enhancing the sampling on the conformational spaces. We argue that these techniques are mature enough to be applied to not just extend the longevity of life but the beauty it manifests. In this Perspective, we review the current frontiers in the research and development of skincare products, as well as the statistical and physical toolbox applicable to addressing the challenges in this industry. Feasible interdisciplinary research projects are proposed to harness the power of machine learning tools to design innovative, effective, and inexpensive skincare products. Toolbox ; Computer science ; Perspective (graphical) ; Human–computer interaction ; Data science ; Molecular machine ; Beauty ; Artificial intelligence ; Machine learning ; Systems engineering ; Nanotechnology ; Engineering ; Materials science Photochromic and Fluorescence Chemistry ; Olfactory and Sensory Function Studies ; Computational Drug Discovery Methods