jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengqi Li | Rui Li | Longjia Wu | Xiongfeng Lin | Xueqing Xia | Zitong Ao | Xiaojuan Sun | Xingtong Chen | Song Chen
Source: Nature Communications (article, 2024-06-17)
Field: Materials Science — Materials Chemistry

Ultrabright and stable top-emitting quantum-dot light-emitting diodes with negligible angular color shift Top emission can enhance luminance, color purity, and panel-manufacturing compatibility for emissive displays. Still, top-emitting quantum-dot light-emitting diodes (QLEDs) suffer from poor stability, low light outcoupling, and non-negligible viewing-angle dependence because, for QLEDs with non-red emission, the electrically optimum device structure is incompatible with single-mode optical microcavity. Here, we demonstrate that by improving the way of determining reflection penetration depths and creating refractive-index-lowering processes, the issues faced by green QLEDs can be overcome. This leads to advanced device performance, including a luminance exceeding 1.6 million nits, a current efficiency of 204.2 cd A<sup>-1</sup>, and a T<sub>95</sub> operational lifetime of 15,600 hours at 1000 nits. Meanwhile, our design does not compromise light outcoupling as it offers an external quantum efficiency of 29.2% without implementing light extraction methods. Lastly, an angular color shift of Δu'v' = 0.0052 from 0° to 60° is achieved by narrowing the emission linewidth of quantum dots. Quantum dot ; Light-emitting diode ; Optoelectronics ; Diode ; Quantum ; Physics ; Materials science ; Quantum mechanics Quantum Dots Synthesis And Properties ; Semiconductor Quantum Structures and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Ayman Maqsood | Chen Chen | T. Jesper Jacobsson
Source: Advanced Science (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

The Future of Material Scientists in an Age of Artificial Intelligence Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI-models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI-approaches. To structure the discussion, a conceptual framework of an AI-ladder is introduced. This AI-ladder ranges from basic data-fitting techniques to more advanced functionalities such as semi-autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping-stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence. Artificial intelligence ; Computer science ; Orchestration ; Variety (cybernetics) ; Big data ; Data science ; Perspective (graphical) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Jaewoong Choi | Byungju Lee
Source: Communications Materials (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Accelerating materials language processing with large language models Abstract Materials language processing (MLP) can facilitate materials science research by automating the extraction of structured data from research papers. Despite the existence of deep learning models for MLP tasks, there are ongoing practical issues associated with complex model architectures, extensive fine-tuning, and substantial human-labelled datasets. Here, we introduce the use of large language models, such as generative pretrained transformer (GPT), to replace the complex architectures of prior MLP models with strategic designs of prompt engineering. We find that in-context learning of GPT models with few or zero-shots can provide high performance text classification, named entity recognition and extractive question answering with limited datasets, demonstrated for various classes of materials. These generative models can also help identify incorrect annotated data. Our GPT-based approach can assist material scientists in solving knowledge-intensive MLP tasks, even if they lack relevant expertise, by offering MLP guidelines applicable to any materials science domain. In addition, the outcomes of GPT models are expected to reduce the workload of researchers, such as manual labelling, by producing an initial labelling set and verifying human-annotations. Computer science ; Generative grammar ; Transformer ; Artificial intelligence ; Language model ; Natural language processing ; Deep learning ; Context (archaeology) ; Set (abstract data type) ; Domain (mathematical analysis) ; Generative model ; Machine learning ; Programming language Machine Learning in Materials Science ; Software Engineering Research ; Topic Modeling
Authors: Shih‐Cheng Li | Haoyang Wu | Angiras Menon | Kevin Spiekermann | Yi‐Pei Li | William H. Green
Source: Journal of the American Chemical Society (article, 2024-08-06)
Field: Materials Science — Materials Chemistry

When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties? Deep graph neural networks are extensively utilized to predict chemical reactivity and molecular properties. However, because of the complexity of chemical space, such models often have difficulty extrapolating beyond the chemistry contained in the training set. Augmenting the model with quantum mechanical (QM) descriptors is anticipated to improve its generalizability. However, obtaining QM descriptors often requires CPU-intensive computational chemistry calculations. To identify when QM descriptors help graph neural networks predict chemical properties, we conduct a systematic investigation of the impact of atom, bond, and molecular QM descriptors on the performance of directed message passing neural networks (D-MPNNs) for predicting 16 molecular properties. The analysis surveys computational and experimental targets, as well as classification and regression tasks, and varied data set sizes from several hundred to hundreds of thousands of data points. Our results indicate that QM descriptors are mostly beneficial for D-MPNN performance on small data sets, provided that the descriptors correlate well with the targets and can be readily computed with high accuracy. Otherwise, using QM descriptors can add cost without benefit or even introduce unwanted noise that can degrade model performance. Strategic integration of QM descriptors with D-MPNN unlocks potential for physics-informed, data-efficient modeling with some interpretability that can streamline <i>de novo</i> drug and material designs. To facilitate the use of QM descriptors in machine learning workflows for chemistry, we provide a set of guidelines regarding when and how to best leverage QM descriptors, a high-throughput workflow to compute them, and an enhancement to Chemprop, a widely adopted open-source D-MPNN implementation for chemical property prediction. Interpretability ; Chemical space ; Artificial neural network ; Generalizability theory ; Molecular descriptor ; Artificial intelligence ; Quantum chemical ; Molecular graph ; Chemistry ; Graph ; Machine learning ; Computer science ; QM/MM ; Set (abstract data type) ; Quantitative structure–activity relationship ; Computational chemistry ; Theoretical computer science ; Molecular dynamics ; Molecule ; Mathematics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Panpan Zhou | Qianwen Zhou | Xuezhang Xiao | Xiulin Fan | Yongjin Zou | Lixian Sun | Jinghua Jiang | Dan Song | Lixin Chen
Source: Advanced Materials (review, 2024-12-20)
Field: Materials Science — Materials Chemistry

Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives Machine learning (ML) has emerged as a pioneering tool in advancing the research application of high-performance solid-state hydrogen storage materials (HSMs). This review summarizes the state-of-the-art research of ML in resolving crucial issues such as low hydrogen storage capacity and unfavorable de-/hydrogenation cycling conditions. First, the datasets, feature descriptors, and prevalent ML models tailored for HSMs are described. Specific examples include the successful application of ML in titanium-based, rare-earth-based, solid solution, magnesium-based, and complex HSMs, showcasing its role in exploiting composition-structure-property relationships and designing novel HSMs for specific applications. One of the representative ML works is the single-phase Ti-based HSM with superior cost-effective and comprehensive properties, tailored to fuel cell hydrogen feeding system at ambient temperature and pressure through high-throughput composition-performance scanning. More importantly, this review also identifies and critically analyzes the key challenges faced by ML in this domain, including poor data quality and availability, and the balance between model interpretability and accuracy, together with feasible countermeasures suggested to ameliorate these problems. In summary, this work outlines a roadmap for enhancing ML's utilization in solid-state hydrogen storage research, promoting more efficient and sustainable energy storage solutions. Hydrogen storage ; Interpretability ; Materials science ; Throughput ; Computer science ; Process engineering ; Systems engineering ; Nanotechnology ; Machine learning ; Engineering Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Dongxiao Chen | Lin Chen | Qiancheng Zhao | Zhengxin Yang | Cheng Shang | Zhi‐Pan Liu
Source: Nature Catalysis (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Square-pyramidal subsurface oxygen [Ag4OAg] drives selective ethene epoxidation on silver Square pyramidal molecular geometry ; Oxygen ; Square (algebra) ; Chemistry ; Organic chemistry ; Mathematics ; Metal Catalytic Processes in Materials Science ; Advanced Nanomaterials in Catalysis ; Electrocatalysts for Energy Conversion
Authors: Kamal Choudhary
Source: The Journal of Physical Chemistry Letters (article, 2024-06-27)
Field: Materials Science — Materials Chemistry

AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design Large language models (LLMs) such as generative pretrained transformers (GPTs) have shown potential for various commercial applications, but their applicability for materials design remains underexplored. In this Letter, AtomGPT is introduced as a model specifically developed for materials design based on transformer architectures, demonstrating capabilities for both atomistic property prediction and structure generation. This study shows that a combination of chemical and structural text descriptions can efficiently predict material properties with accuracy comparable to graph neural network models, including formation energies, electronic bandgaps from two different methods, and superconducting transition temperatures. Furthermore, AtomGPT can generate atomic structures for tasks such as designing new superconductors, with the predictions validated through density functional theory calculations. This work paves the way for leveraging LLMs in forward and inverse materials design, offering an efficient approach to the discovery and optimization of materials. Generative grammar ; Transformer ; Inverse ; Computer science ; Generative Design ; Materials science ; Artificial intelligence ; Engineering ; Mathematics ; Electrical engineering ; Geometry ; Compatibility (geochemistry) ; Composite material Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Shuaizhen Li | Tianwen Xie | Lin Ma | Bo Li | Daheng Liu | Nan Huang | Wei Liu | Bing Li | Zhigang Gai | Xin Jiang | Song Ma | Zhidong Zhang
Source: Composites Part B Engineering (article, 2024-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advanced bifunctional bionic neural network-like architecture constructed by multi-scale carbon nanotubes nanocomposites for enhanced microwave absorption Materials science ; Nanocomposite ; Carbon nanotube ; Bifunctional ; Microwave ; Composite material ; Absorption (acoustics) ; Nanotechnology ; Catalysis ; Computer science ; Organic chemistry Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Yifan Gao | Kongyu Ge | Zhen Zhang | Zhan Li | Shaowei Hu | Hongjun Ji | Mingyu Li | Huanhuan Feng
Source: Advanced Science (article, 2024-04-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fine Optimization of Colloidal Photonic Crystal Structural Color for Physically Unclonable Multiplex Encryption and Anti‐Counterfeiting Robust anti-counterfeiting techniques aim for easy identification while remaining difficult to forge, especially for high-value items such as currency and passports. However, many existing anti-counterfeiting techniques rely on deterministic processes, resulting in loopholes for duplication and counterfeiting. Therefore, achieving high-level encryption and easy authentication through conventional anti-counterfeiting techniques has remained a significant challenge. To address this, this work proposes a solution that combined fluorescence and structural colors, creating a physically unclonable multiplex encryption system (PUMES). In this study, the physicochemical properties of colloidal photonic inks are systematically adjusted to construct a comprehensive printing phase diagram, revealing the printable region. Furthermore, the brightness and color saturation of inkjet-printed colloidal photonic crystal structural colors are optimized by controlling the substrate's hydrophobicity, printed droplet volume, and the addition of noble metals. Finally, fluorescence is incorporated to build PUMES, including macroscopic fluorescence and structural color patterns, as well as microscopic physically unclonable fluorescence patterns. The PUMES with intrinsic randomness and high encoding capacity are authenticated by a deep learning algorithm, which proved to be reliable and efficient under various observation conditions. This approach can provide easy identification and formidable resistance against counterfeiting, making it highly promising for the next-generation anti-counterfeiting of currency and passports. Encryption ; Materials science ; Structural coloration ; Traceability ; Nanotechnology ; Multiplex ; Computer science ; Authentication (law) ; Photonic crystal ; Identification (biology) ; Optoelectronics ; Bioinformatics ; Computer security Gold and Silver Nanoparticles Synthesis and Applications ; Photochromic and Fluorescence Chemistry ; Nanomaterials and Printing Technologies
Authors: Hong Wang | Hengyu Cao | Liang Yang
Source: ACS Applied Nano Materials (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Multidomain Nanomaterial Design: From Bibliometric Analysis to Applications Machine learning (ML), as an advanced data analysis tool, simulates the learning process of the human brain, enabling the extraction of features, discovery of patterns, and making accurate predictions or decisions from complex data. In the field of nanomaterial design, the application of ML technology not only accelerates the discovery and performance optimization of nanomaterials but also promotes the innovation of materials science research methods. Bibliometrics, as a research method based on quantitative analysis, provides us with a macro perspective to observe and understand the application of ML technology in nanomaterial design by statistically analyzing various indicators in the scientific literature. This paper quantitatively analyzes the literature related to ML-driven nanomaterial design from seven dimensions, revealing the importance and necessity of ML technology in nanomaterial design. It also systematically analyzes the diversified applications of the combination of ML technology and nanomaterial technology with the design of suitable ML algorithms being key to enhancing the performance of nanomaterials. In addition, this paper discusses current challenges and future development directions, including data quality and data set construction, algorithm innovation and optimization, and the deepening of interdisciplinary cooperation. This review not only provides researchers with a macro perspective to observe the current state and development trends of the field but also provides ideas and suggestions for future research. This is of significant importance and value for promoting scientific progress in the field of nanomaterial design, fostering the in-depth development of interdisciplinary research, and accelerating the innovative application of material technologies. Computer science ; Nanotechnology ; Data science ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electronic and Structural Properties of Oxides
Authors: Lina M. Shaker | Ahmed A. Al‐Amiery | Waleed Khalid Al‐Azzawi
Source: Discover Nano (article, 2024-01-03)
Field: Materials Science — Materials Chemistry

Nanomaterials: paving the way for the hydrogen energy frontier This comprehensive review explores the transformative role of nanomaterials in advancing the frontier of hydrogen energy, specifically in the realms of storage, production, and transport. Focusing on key nanomaterials like metallic nanoparticles, metal-organic frameworks, carbon nanotubes, and graphene, the article delves into their unique properties. It scrutinizes the application of nanomaterials in hydrogen storage, elucidating both challenges and advantages. The review meticulously evaluates diverse strategies employed to overcome limitations in traditional storage methods and highlights recent breakthroughs in nanomaterial-centric hydrogen storage. Additionally, the article investigates the utilization of nanomaterials to enhance hydrogen production, emphasizing their role as efficient nanocatalysts in boosting hydrogen fuel cell efficiency. It provides a comprehensive overview of various nanocatalysts and their potential applications in fuel cells. The exploration extends to the realm of hydrogen transport and delivery, specifically in storage tanks and pipelines, offering insights into the nanomaterials investigated for this purpose and recent advancements in the field. In conclusion, the review underscores the immense potential of nanomaterials in propelling the hydrogen energy frontier. It emphasizes the imperative for continued research aimed at optimizing the properties and performance of existing nanomaterials while advocating for the development of novel nanomaterials with superior attributes for hydrogen storage, production, and transport. This article serves as a roadmap, shedding light on the pivotal role nanomaterials can play in advancing the development of clean and sustainable hydrogen energy technologies. Hydrogen storage ; Nanomaterials ; Nanotechnology ; Hydrogen production ; Nanomaterial-based catalyst ; Energy storage ; Materials science ; Biochemical engineering ; Hydrogen ; Engineering ; Nanoparticle ; Chemistry ; Physics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Xiaolong Gao | Huan Wei | Wenjie Ma | Wenjie Wu | Wenliang Ji | Junjie Mao | Ping Yu | Lanqun Mao
Source: Nature Communications (article, 2024-09-10)
Field: Materials Science — Materials Chemistry

Inflammation-free electrochemical in vivo sensing of dopamine with atomic-level engineered antioxidative single-atom catalyst Electrochemical methods with tissue-implantable microelectrodes provide an excellent platform for real-time monitoring the neurochemical dynamics in vivo due to their superior spatiotemporal resolution and high selectivity and sensitivity. Nevertheless, electrode implantation inevitably damages the brain tissue, upregulates reactive oxygen species level, and triggers neuroinflammatory response, resulting in unreliable quantification of neurochemical events. Herein, we report a multifunctional sensing platform for inflammation-free in vivo analysis with atomic-level engineered Fe single-atom catalyst that functions as both single-atom nanozyme with antioxidative activity and electrode material for dopamine oxidation. Through high-temperature pyrolysis and catalytic performance screening, we fabricate a series of Fe single-atom nanozymes with different coordination configurations and find that the Fe single-atom nanozyme with FeN<sub>4</sub> exhibits the highest activity toward mimicking catalase and superoxide dismutase as well as eliminating hydroxyl radical, while also featuring high electrode reactivity toward dopamine oxidation. These dual functions endow the single-atom nanozyme-based sensor with anti-inflammatory capabilities, enabling accurate dopamine sensing in living male rat brain. This study provides an avenue for designing inflammation-free electrochemical sensing platforms with atomic-precision engineered single-atom catalysts. In vivo ; Catalysis ; Electrochemistry ; Atom (system on chip) ; Dopamine ; Inflammation ; Chemistry ; Nanotechnology ; Materials science ; Biophysics ; Biochemistry ; Neuroscience ; Medicine ; Biology ; Computer science ; Electrode ; Physical chemistry ; Biotechnology ; Immunology Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Ting Xue | Jing Li | Lvcun Chen | Kanglu Li | Ying Hua | Yan Yang | Fan Dong
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Photocatalytic NO<sub><i>x</i></sub> removal and recovery: progress, challenges and future perspectives The excessive production of nitrogen oxides (NO <sub><i>x</i></sub> ) from energy production, agricultural activities, transportation, and other human activities remains a pressing issue in atmospheric environment management. NO <sub><i>x</i></sub> serves both as a significant pollutant and a potential feedstock for energy carriers. Photocatalytic technology for NO <sub><i>x</i></sub> removal and recovery has received widespread attention and has experienced rapid development in recent years owing to its environmental friendliness, mild reaction conditions, and high efficiency. This review systematically summarizes the recent advances in photocatalytic removal, encompassing NO <sub><i>x</i></sub> oxidation removal (including single and synergistic removal and NO<sub>3</sub> <sup>-</sup> decomposition), NO <sub><i>x</i></sub> reduction to N<sub>2</sub>, and the emergent NO <sub><i>x</i></sub> upcycling into green ammonia. Special focus is given to the molecular understanding of the interfacial nitrogen-associated reaction mechanisms and their regulation pathways. Finally, the status and the challenges of photocatalytic NO <sub><i>x</i></sub> removal and recovery are critically discussed and future outlooks are proposed for their potential practical application. Photocatalysis ; Agriculture ; Production (economics) ; Environmental science ; Nitrogen ; Nitrogen oxides ; Materials science ; Nanotechnology ; Environmental chemistry ; Business ; Waste management ; Chemistry ; Engineering ; Catalysis ; Geography ; Economics Catalytic Processes in Materials Science ; Atmospheric chemistry and aerosols ; Industrial Gas Emission Control