jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cheng Zhang | Lu Chen | Zhelin Lin | Junyeob Song | Danyan Wang | Moxin Li | Okan Koksal | Zi Wang | Grisha Spektor | David R. Carlson | Henri J. Lezec | Wenqi Zhu | Scott B. Papp | Amit Agrawal
Source: Light Science & Applications (article, 2024-01-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tantalum pentoxide: a new material platform for high-performance dielectric metasurface optics in the ultraviolet and visible region Dielectric metasurfaces, composed of planar arrays of subwavelength dielectric structures that collectively mimic the operation of conventional bulk optical elements, have revolutionized the field of optics by their potential in constructing high-efficiency and multi-functional optoelectronic systems on chip. The performance of a dielectric metasurface is largely determined by its constituent material, which is highly desired to have a high refractive index, low optical loss and wide bandgap, and at the same time, be fabrication friendly. Here, we present a new material platform based on tantalum pentoxide (Ta<sub>2</sub>O<sub>5</sub>) for implementing high-performance dielectric metasurface optics over the ultraviolet and visible spectral region. This wide-bandgap dielectric, exhibiting a high refractive index exceeding 2.1 and negligible extinction coefficient across a broad spectrum, can be easily deposited over large areas with good quality using straightforward physical vapor deposition, and patterned into high-aspect-ratio subwavelength nanostructures through commonly-available fluorine-gas-based reactive ion etching. We implement a series of high-efficiency ultraviolet and visible metasurfaces with representative light-field modulation functionalities including polarization-independent high-numerical-aperture lensing, spin-selective hologram projection, and vivid structural color generation, and the devices exhibit operational efficiencies up to 80%. Our work overcomes limitations faced by scalability of commonly-employed metasurface dielectrics and their operation into the visible and ultraviolet spectral range, and provides a novel route towards realization of high-performance, robust and foundry-manufacturable metasurface optics. Materials science ; Dielectric ; Optoelectronics ; Nanopillar ; Optics ; Ultraviolet ; Refractive index ; Tantalum pentoxide ; High-refractive-index polymer ; Nanotechnology ; Nanostructure ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Polydiacetylene-based materials and applications
Authors: Ming‐Chang Chiang | Yuping Yang | Christopher J.B. Nicol | Chieh-Ju Wang
Source: International Journal of Molecular Sciences (review, 2024-02-17)
Field: Materials Science — Biomaterials

Gold Nanoparticles in Neurological Diseases: A Review of Neuroprotection This review explores the diverse applications of gold nanoparticles (AuNPs) in neurological diseases, with a specific focus on Alzheimer's disease (AD), Parkinson's disease (PD), and stroke. The introduction highlights the pivotal role of neuroinflammation in these disorders and introduces the unique properties of AuNPs. The review's core examines the mechanisms by which AuNPs exert neuroprotection and anti-neuro-inflammatory effects, elucidating various pathways through which they manifest these properties. The potential therapeutic applications of AuNPs in AD are discussed, shedding light on promising avenues for therapy. This review also explores the prospects of utilizing AuNPs in PD interventions, presenting a hopeful outlook for future treatments. Additionally, the review delves into the potential of AuNPs in providing neuroprotection after strokes, emphasizing their significance in mitigating cerebrovascular accidents' aftermath. Experimental findings from cellular and animal models are consolidated to provide a comprehensive overview of AuNPs' effectiveness, offering insights into their impact at both the cellular and in vivo levels. This review enhances our understanding of AuNPs' applications in neurological diseases and lays the groundwork for innovative therapeutic strategies in neurology. Neuroprotection ; Colloidal gold ; Nanotechnology ; Medicine ; Neuroscience ; Nanoparticle ; Pharmacology ; Biology ; Materials science Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Yuri Alexeev | Maximilian Amsler | Marco Antonio Barroca | Sanzio Bassini | Torey Battelle | Daan Camps | David Casanova | Young Jay Choi | Frederic T. Chong | Charles Chung | C.F. Codella | Antonio Córcoles | James Cruise | Alberto Di Meglio | I. Ďuran | Thomas Eckl | Sophia E. Economou | Stephan Eidenbenz | Bruce G. Elmegreen | Clyde Fare | Ismael Faro | Cristina Sanz Fernández | Rodrigo Neumann Barros Ferreira | Keisuke Fuji | Bryce Fuller | Laura Gagliardi | Giulia Galli | Jennifer R. Glick | Isacco Gobbi | Pranav Gokhale | Salvador de la Puente Gonzalez | Johannes Greiner | William Gropp | Michele Grossi | Emanuel Gull | Burns Healy | Matthew R. Hermes | Benchen Huang | Travis S. Humble | Nobuyasu Ito | Artur F. Izmaylov | Ali Javadi-Abhari | Douglas M. Jennewein | Shantenu Jha | Liang Jiang | Barbara Jones | Wibe A. de Jong | Petar Jurcevic | William Kirby | Stefan Kister | Masahiro Kitagawa | Joel Klassen | Katherine Klymko | Kwangwon Koh | Masaaki Kondo | Dog̃a Murat Kürkçüog̃lu | Krzysztof Kurowski | Teodoro Laino | Ryan Landfield | Matt Leininger | Vicente Leyton‐Ortega | Ang Li | Meifeng Lin | Junyu Liu | Nicolás Lorente | André Luckow | Simon Martiel | Francisco Martín-Fernández | Margaret Martonosi | Claire Marvinney | Arcesio Castaneda Medina | Dirk Merten | Antonio Mezzacapo | Kristel Michielsen | Abhishek Mitra | Tushar Mittal | Kyungsun Moon | Joel E. Moore | Sarah Mostame | Mário Motta | Young-Hye Na | Yunseong Nam | Prineha Narang | Yu‐ya Ohnishi | Diego Ottaviani | Matthew Otten | Scott Pakin | V. R. Pascuzzi | Edwin Pednault | Tomasz Piontek | Jed W. Pitera | Patrick Rall | Gokul Subramanian Ravi | Niall F. Robertson | Matteo A. C. Rossi | Piotr Rydlichowski | Hoon Ryu | Ge. G. Samsonidze | Mitsuhisa Sato | Nishant Saurabh
Source: Future Generation Computer Systems (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Quantum-centric supercomputing for materials science: A perspective on challenges and future directions Supercomputer ; Computer science ; Quantum computer ; Perspective (graphical) ; Computational science ; Data science ; Identification (biology) ; Quantum ; Distributed computing ; Computer engineering ; Parallel computing ; Artificial intelligence Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Condensed Matter Physics
Authors: David B. Olawade | Abimbola O. Ige | Abimbola G. Olaremu | James Ijiwade | Adedapo O. Adeola
Source: Nano Trends (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

The synergy of artificial intelligence and nanotechnology towards advancing innovation and sustainability - A mini-review • AI enables optimization of nanomanufacturing processes, enhancing efficiency and precision in the production of nanoscale devices and materials. • AI-driven predictive modeling enables the simulation of complex nanoscale phenomena, aiding in the design and optimization of nanomaterials and devices. • AI plays a pivotal role in accelerating materials discovery, optimizing nanomaterial properties for various applications from electronics to healthcare. The convergence of Artificial Intelligence (AI) and nanotechnology is a transformative frontier, holding vast potential for scientific and technological advancements. This review explores the integration of AI and nanotechnology, aiming to uncover current trends, challenges, and future directions across various domains. A scientometric approach was employed, gathering and analyzing existing literature and research in the fields of AI and nanotechnology. Through a systematic examination of recent studies and developments, we identified key trends, challenges, and potential pathways for future research and applications. Findings from this review highlight the significant impact of AI-driven nanotechnology in materials discovery and design, healthcare, environmental monitoring, energy conversion, and quantum computing. These areas have witnessed remarkable advancements, such as the development of novel nanomaterials, precise drug delivery systems, advanced nanosensors, and efficient energy storage solutions. However, challenges persist, including insufficient interdisciplinary collaboration, data quality, ethical concerns, and regulatory frameworks. While challenges require careful consideration and mitigation, the potential for innovation and progress in diverse domains is undeniable. This review underscores the importance of responsible AI use and ongoing research to unlock the full potential of this convergence, ultimately shaping the future of science and technology. Sustainability ; Nanotechnology ; Business ; Engineering ; Engineering ethics ; Materials science ; Ecology ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Guangsheng Xu | Mingxi Jiang | Jinliang Li | Xiaoyang Xuan | Jiabao Li | Ting Lu | Likun Pan
Source: Energy storage materials (article, 2024-08-13)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries Materials science ; Lithium (medication) ; Electrolyte ; Electrode ; Ion ; Nanotechnology ; Engineering physics ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Xiaowei Zhang | Chong Wang | Xiaoyu Liu | Yueyao Fan | Ting Cao | Di Xiao
Source: Nature Communications (article, 2024-05-18)
Field: Materials Science — Materials Chemistry

Polarization-driven band topology evolution in twisted MoTe2 and WSe2 Motivated by recent experimental observations of opposite Chern numbers in R-type twisted MoTe<sub>2</sub> and WSe<sub>2</sub> homobilayers, we perform large-scale density-functional-theory calculations with machine learning force fields to investigate moiré band topology across a range of twist angles in both materials. We find that the Chern numbers of the moiré frontier bands change sign as a function of twist angle, and this change is driven by the competition between moiré ferroelectricity and piezoelectricity. Our large-scale calculations, enabled by machine learning methods, reveal crucial insights into interactions across different scales in twisted bilayer systems. The interplay between atomic-level relaxation effects and moiré-scale electrostatic potential variation opens new avenues for the design of intertwined topological and correlated states, including the possibility of mimicking higher Landau level physics in the absence of magnetic field. Topology (electrical circuits) ; Physics ; Polarization (electrochemistry) ; Ferroelectricity ; Condensed matter physics ; Length scale ; Quantum mechanics ; Mathematics 2D Materials and Applications ; Topological Materials and Phenomena ; Machine Learning in Materials Science
Authors: Anjali Leal | A Gutb er -Jena | Ayushi Bamola | Sadhna Mishra | Ishika Jain | Nandini Pathak | Nishita Sharma | Nitiksha Joshi | Renu Pandey | Shakshi Kaparwal | Vinay Kumar Yadav | Arun Kumar Gupta | Avinash Kumar Jha | Saurav Chandra Bhatt | Vijay Kumar | Bindu Naik | Sarvesh Rustagi | Manpreet Singh Preet | Saamir Akhtar
Source: Food Production Processing and Nutrition (article, 2024-02-29)
Field: Chemistry — Analytical Chemistry

State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions Abstract Recent advancements in signal processing and computational power have revolutionized computer vision applications in diverse industries such as agriculture, food processing, biomedical, and the military. These developments are propelling efforts to automate processes and enhance efficiency. Notably, computational techniques are replacing labor-intensive manual methods for assessing the maturity indices of fruits and vegetables during critical growth stages. This review paper focuses on recent advancements in computer vision techniques specifically applied to determine the maturity indices of fruits and vegetables within the food processing sector. It highlights successful applications of Nuclear Magnetic Resonance (NMR), Near-Infrared Spectroscopy (NIR), thermal imaging, and image scanning. By examining these techniques, their underlying principles, and practical feasibility, it offers valuable insights into their effectiveness and potential widespread adoption. Additionally, integrating biosensors and AI techniques further improves accuracy and efficiency in maturity index determination. In summary, this review underscores the significant role of computational techniques in advancing maturity index assessment and provides insights into their principles and effective utilization. Looking ahead, the future of computer vision techniques holds immense potential. Collaborative efforts among experts from various fields will be crucial to address challenges, ensure standardization, and safeguard data privacy. Embracing these advancements can lead to sustainable practices, optimized resource management, and progress across industries. Graphical Abstract Maturity (psychological) ; Index (typography) ; State (computer science) ; State of art ; Agricultural engineering ; Geography ; Computer science ; Engineering ; Data science ; Political science ; World Wide Web ; Law ; Algorithm Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Xiangjin Wu | Asir Intisar Khan | Hengyuan Lee | Chen-Feng Hsu | H. R. Zhang | Heshan Yu | N. Roy | Albert V. Davydov | Ichiro Takeuchi | Xinyu Bao | H.‐S. Philip Wong | Eric Pop
Source: Nature Communications (article, 2024-01-22)
Field: Materials Science — Materials Chemistry

Novel nanocomposite-superlattices for low energy and high stability nanoscale phase-change memory Data-centric applications are pushing the limits of energy-efficiency in today's computing systems, including those based on phase-change memory (PCM). This technology must achieve low-power and stable operation at nanoscale dimensions to succeed in high-density memory arrays. Here we use a novel combination of phase-change material superlattices and nanocomposites (based on Ge<sub>4</sub>Sb<sub>6</sub>Te<sub>7</sub>), to achieve record-low power density ≈ 5 MW/cm<sup>2</sup> and ≈ 0.7 V switching voltage (compatible with modern logic processors) in PCM devices with the smallest dimensions to date (≈ 40 nm) for a superlattice technology on a CMOS-compatible substrate. These devices also simultaneously exhibit low resistance drift with 8 resistance states, good endurance (≈ 2 × 10<sup>8</sup> cycles), and fast switching (≈ 40 ns). The efficient switching is enabled by strong heat confinement within the superlattice materials and the nanoscale device dimensions. The microstructural properties of the Ge<sub>4</sub>Sb<sub>6</sub>Te<sub>7</sub> nanocomposite and its high crystallization temperature ensure the fast-switching speed and stability in our superlattice PCM devices. These results re-establish PCM technology as one of the frontrunners for energy-efficient data storage and computing. Phase-change memory ; Superlattice ; Materials science ; Nanocomposite ; Nanoscopic scale ; Optoelectronics ; Nanotechnology ; Phase-change material ; CMOS ; Engineering physics ; Phase change ; Physics ; Layer (electronics) Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials
Authors: Hong Li | Qiuhui Li | Ying Li | Zongmeng Yang | Ruge Quhe | Xiaotian Sun | Yangyang Wang | Lin Xu | Lian‐Mao Peng | He Tian | Chenguang Qiu | Jing Lü
Source: Advanced Functional Materials (article, 2024-04-12)
Field: Materials Science — Materials Chemistry

Recent Experimental Breakthroughs on 2D Transistors: Approaching the Theoretical Limit Abstract Since Si‐based Moore's law is physically limited, 2D semiconductors are proposed as successors to continue shrinking the transistor size for more Moore electronics. However, limited by experimental technology bottlenecks, the theoretical predicted superiorities of the 2D transistors over the state‐of‐the‐art Si transistors have been lacking concrete evidence for a decade. In this review, recent exciting experimental breakthroughs for 2D transistors are presented, including gate length miniaturization to a sub‐1 nm limit, electrode contact optimization to the resistance quantum limit, high‐quality dielectric fabrication with an equivalent oxide thickness to sub‐0.5 nm, novel architecture form (2D fin field‐effect transistor), and back‐end‐of‐line integration of directly grown 2D materials on Si complementary metal‐oxide‐semiconductor circuits. Remarkably, an ultrashort channel, Ohmic contact, ballistic transport, and ultrathin dielectric layer are simultaneously satisfied in the 2D InSe transistor, and device performances approaching the theoretical limit are observed. The measured key figures of merit of the ideal 2D InSe transistor are comparable to or even surpass those of the Si transistors. Finally, the challenges and outlook on more Moore electronics based on 2D transistors are highlighted. Transistor ; Materials science ; Ohmic contact ; Optoelectronics ; Electronics ; Field-effect transistor ; Nanotechnology ; Miniaturization ; Figure of merit ; Contact resistance ; Semiconductor ; Electrical engineering ; Layer (electronics) ; Voltage 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Anshuman Dasgupta | Alexandros Marios Sofias | Fabian Kießling | Twan Lammers
Source: Nature Reviews Bioengineering (article, 2024-06-04)
Field: Materials Science — Biomaterials

Nanoparticle delivery to tumours: from EPR and ATR mechanisms to clinical impact Drug delivery ; Nanoparticle ; Nanotechnology ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Radiation Therapy and Dosimetry
Authors: Alireza Ghafarollahi | Markus J. Buehler
Source: Advanced Materials (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

SciAgents: Automating Scientific Discovery Through Bioinspired Multi‐Agent Intelligent Graph Reasoning A key challenge in artificial intelligence (AI) is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. In this work, SciAgents, an approach that leverages three core concepts is presented: (1) large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning capabilities. Applied to biologically inspired materials, SciAgents reveals hidden interdisciplinary relationships that were previously considered unrelated, achieving a scale, precision, and exploratory power that surpasses human research methods. The framework autonomously generates and refines research hypotheses, elucidating underlying mechanisms, design principles, and unexpected material properties. By integrating these capabilities in a modular fashion, the system yields material discoveries, critiques and improves existing hypotheses, retrieves up-to-date data about existing research, and highlights strengths and limitations. This is achieved by harnessing a "swarm of intelligence" similar to biological systems, providing new avenues for discovery. How this model accelerates the development of advanced materials by unlocking Nature's design principles, resulting in a new biocomposite with enhanced mechanical properties and improved sustainability through energy-efficient production is shown. Computer science ; Data science ; Modular design ; Suite ; Artificial intelligence Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Yixiang Ruan | Chenyin Lu | Ning Xu | Yuchen He | Yi-Xin Chen | Jian Zhang | Jun Xuan | Jian‐Zhang Pan | Qun Fang | Hanyu Gao | Xiaodong Shen | Ning Ye | Qiang Zhang | Yiming Mo
Source: Nature Communications (article, 2024-11-23)
Field: Materials Science — Materials Chemistry

An automatic end-to-end chemical synthesis development platform powered by large language models The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application with LLM-RDF as the backend was built to allow chemist users to interact with automated experimental platforms and analyze results via natural language, thus, eliminating the need for coding skills and ensuring accessibility for all chemists. We demonstrated the capabilities of LLM-RDF in guiding the end-to-end synthesis development process for the copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, including literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. Furthermore, LLM-RDF's broader applicability and versability was validated on various synthesis tasks of three distinct reactions (S<sub>N</sub>Ar reaction, photoredox C-C cross-coupling reaction, and heterogeneous photoelectrochemical reaction). Computer science ; RDF ; Linked data ; Semantic Web ; World Wide Web Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Electrocatalysts for Energy Conversion
Authors: Mutha Nandipati | Olukayode Fatoki | Salil Desai
Source: Materials (review, 2024-04-02)
Field: Materials Science — Materials Chemistry

Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution-Industry 4.0-as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed. Nanomanufacturing ; Artificial intelligence ; Transformative learning ; Computer science ; Machine learning ; Digital manufacturing ; Big data ; Deep learning ; Nanotechnology ; Manufacturing engineering ; Engineering ; Materials science ; Data mining Machine Learning in Materials Science ; Nanofabrication and Lithography Techniques ; Nanotechnology research and applications