jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ahmed Elsayed Abouelez | Mohamed Nady Abdulaleem
Source: Optical and Quantum Electronics (article, 2023-09-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and analysis of quad-band polarization-insensitive infrared perfect metamaterial absorber with a wide-incident angle Abstract There is a pressing demand to design simple absorber structures that support optimal absorption in multi-band to meet different applications. In this work, a novel quad-band infrared perfect metamaterial absorber (QPMA) is proposed and numerically investigated. The structure of the QPMA is composed of coaxial multi-layer dielectric (SiO 2 ) disks of circular shape with different diameters where each disk is surrounded by a gold ring. All disks are placed on a SiO 2 dielectric spacer and an impermeable gold ground layer. Simulation results reveal that the proposed absorber has four absorption peaks around wavelengths of 1064 nm, 1550 nm, 2080 nm, and 3000 nm. Since these wavelengths correspond to commercially available laser sources, the structure under investigation can be used in different applications such as laser stealth, infrared photodetection, and medical applications. Additionally, the QPMA structure enjoys a good absorption performance for a wide range of incident angles for both TE and TM waves. It achieves absorption greater than 85% at incident angles up to 40° at 1064 nm, and absorption greater than 90% for the other bands at incident angles up to 50°. Moreover, due to the symmetrical shape of the structure, the absorption is insensitive to wave polarization changes. Materials science ; Optics ; Metamaterial absorber ; Metamaterial ; Optoelectronics ; Wavelength ; Polarization (electrochemistry) ; Absorption (acoustics) ; Infrared ; Absorption band ; Laser ; Dielectric ; Ray ; Brewster's angle ; Physics ; Tunable metamaterials Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Odysseas Tsilipakos | Thomas Koschny
Source: Physical review. B./Physical review. B (article, 2023-04-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiresonant metasurfaces for arbitrarily broad bandwidth pulse chirping and dispersion compensation We show that ultrathin metasurfaces with a specific multiresonant response can enable simultaneously arbitrarily strong and arbitrarily broadband dispersion compensation, pulse (de)chirping, and compression or broadening. This breakthrough overcomes the fundamental limitations of both conventional nonresonant approaches (bulky) and modern singly resonant metasurfaces (narrowband) for quadratic phase manipulations of electromagnetic signals. The required nonuniform trains of resonances in the electric and magnetic sheet conductivities that completely control phase delay, group delay, and chirp are rigorously derived and the limitations imposed by fundamental physical constraints are thoroughly discussed. Subsequently, a practical, truncated approximation by finite sequences of physically realizable linear resonances is constructed and the associated error is quantified. By appropriate spectral ordering of the resonances, operation can be achieved either in transmission or reflection mode, enabling full space coverage. The proposed concept is not limited to dispersion compensation, but introduces a generic and powerful ultrathin platform for the spatiotemporal control of broadband real-world signals with a myriad of applications in modern optics, microwave photonics, radar, and communication systems. Narrowband ; Chirp ; Broadband ; Physics ; Bandwidth (computing) ; Dispersion (optics) ; Group delay and phase delay ; Pulse compression ; Optics ; Metamaterial ; Photonics ; Microwave ; Computer science ; Radar ; Telecommunications ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Maximilian Wranik | Michal Kepa | Emma V. Beale | Daniel James | Quentin Bertrand | Tobias Weinert | Antonia Furrer | Hannah Glover | Dardan Gashi | Melissa Carrillo | Yasushi Kondo | Robin Stipp | Georgii Khusainov | Karol Nass | D. Ozerov | Claudio Cirelli | Philip J. M. Johnson | Florian Dworkowski | John H. Beale | Scott L. Stubbs | Thierry Zamofing | Marco Schneider | Kristina Krauskopf | Li Gao | Oliver Thorn‐Seshold | Christoph Bostedt | Camila Bacellar | Michel O. Steinmetz | Christopher J. Milne | Jörg Standfuss
Source: Nature Communications (article, 2023-12-02)
Field: Materials Science — Materials Chemistry

A multi-reservoir extruder for time-resolved serial protein crystallography and compound screening at X-ray free-electron lasers Serial crystallography at X-ray free-electron lasers (XFELs) permits the determination of radiation-damage free static as well as time-resolved protein structures at room temperature. Efficient sample delivery is a key factor for such experiments. Here, we describe a multi-reservoir, high viscosity extruder as a step towards automation of sample delivery at XFELs. Compared to a standard single extruder, sample exchange time was halved and the workload of users was greatly reduced. In-built temperature control of samples facilitated optimal extrusion and supported sample stability. After commissioning the device with lysozyme crystals, we collected time-resolved data using crystals of a membrane-bound, light-driven sodium pump. Static data were also collected from the soluble protein tubulin that was soaked with a series of small molecule drugs. Using these data, we identify low occupancy (as little as 30%) ligands using a minimal amount of data from a serial crystallography experiment, a result that could be exploited for structure-based drug design. Materials science ; Plastics extrusion ; Laser ; Computer science ; Analytical Chemistry (journal) ; Crystallography ; Chemistry ; Chromatography ; Optics Enzyme Structure and Function ; Biochemical and Molecular Research ; Advanced Electron Microscopy Techniques and Applications
Authors: Mehrad Ansari | Seyed Mohamad Moosavi
Source: arXiv (Cornell University) (preprint, 2023-12-18)
Field: Materials Science — Materials Chemistry

Agent-based Learning of Materials Datasets from Scientific Literature Advancements in machine learning and artificial intelligence are transforming materials discovery. Yet, the availability of structured experimental data remains a bottleneck. The vast corpus of scientific literature presents a valuable and rich resource of such data. However, manual dataset creation from these resources is challenging due to issues in maintaining quality and consistency, scalability limitations, and the risk of human error and bias. Therefore, in this work, we develop a chemist AI agent, powered by large language models (LLMs), to overcome these challenges by autonomously creating structured datasets from natural language text, ranging from sentences and paragraphs to extensive scientific research articles. Our chemist AI agent, Eunomia, can plan and execute actions by leveraging the existing knowledge from decades of scientific research articles, scientists, the Internet and other tools altogether. We benchmark the performance of our approach in three different information extraction tasks with various levels of complexity, including solid-state impurity doping, metal-organic framework (MOF) chemical formula, and property relations. Our results demonstrate that our zero-shot agent, with the appropriate tools, is capable of attaining performance that is either superior or comparable to the state-of-the-art fine-tuned materials information extraction methods. This approach simplifies compilation of machine learning-ready datasets for various materials discovery applications, and significantly ease the accessibility of advanced natural language processing tools for novice users in natural language. The methodology in this work is developed as an open-source software on https://github.com/AI4ChemS/Eunomia. Computer science ; Scalability ; Information extraction ; Data science ; Bottleneck ; Consistency (knowledge bases) ; Artificial intelligence ; Resource (disambiguation) ; Workflow ; Natural language ; The Internet ; Machine learning ; World Wide Web ; Database Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Steven B. Torrisi | Martin Z. Bazant | Alexander E. Cohen | Min Gee Cho | Jens S. Hummelshøj | Linda Hung | Gaurav A. Kamat | Arash Khajeh | Adeesh Kolluru | X. L. Lei | Handong Ling | Joseph H. Montoya | Tim Mueller | Aini Palizhati | Benjamin Paren | Brandon K. Phan | Jacob Pietryga | Elodie Sandraz | Daniel Schweigert | Yang Shao‐Horn | Amalie Trewartha | Ruijie Zhu | Debbie Zhuang | Shijing Sun
Source: APL Machine Learning (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Materials cartography: A forward-looking perspective on materials representation and devising better maps Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. The representation of input material features is critical to the accuracy, interpretability, and generalizability of data-driven models for scientific research. In this Perspective, we discuss a few central challenges faced by ML practitioners in developing meaningful representations, including handling the complexity of real-world industry-relevant materials, combining theory and experimental data sources, and describing scientific phenomena across timescales and length scales. We present several promising directions for future research: devising representations of varied experimental conditions and observations, the need to find ways to integrate machine learning into laboratory practices, and making multi-scale informatics toolkits to bridge the gaps between atoms, materials, and devices. Interpretability ; Data science ; Perspective (graphical) ; Representation (politics) ; Computer science ; Popularity ; Generalizability theory ; Materials informatics ; Bridge (graph theory) ; Management science ; Artificial intelligence ; Machine learning ; Engineering ; Health informatics ; Engineering informatics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yizhen Zheng | Huan Yee Koh | Jiaxin Ju | Thi Nguyen | Lauren T. May | Geoffrey I. Webb | Shirui Pan
Source: arXiv (Cornell University) (preprint, 2023-10-12)
Field: Materials Science — Materials Chemistry

Large Language Models for Scientific Synthesis, Inference and Explanation Large language models are a form of artificial intelligence systems whose primary knowledge consists of the statistical patterns, semantic relationships, and syntactical structures of language1. Despite their limited forms of "knowledge", these systems are adept at numerous complex tasks including creative writing, storytelling, translation, question-answering, summarization, and computer code generation. However, they have yet to demonstrate advanced applications in natural science. Here we show how large language models can perform scientific synthesis, inference, and explanation. We present a method for using general-purpose large language models to make inferences from scientific datasets of the form usually associated with special-purpose machine learning algorithms. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. When a conventional machine learning system is augmented with this synthesized and inferred knowledge it can outperform the current state of the art across a range of benchmark tasks for predicting molecular properties. This approach has the further advantage that the large language model can explain the machine learning system's predictions. We anticipate that our framework will open new avenues for AI to accelerate the pace of scientific discovery. Computer science ; Artificial intelligence ; Automatic summarization ; Machine translation ; Inference ; Benchmark (surveying) ; Language model ; Question answering ; Natural language processing ; Machine learning Machine Learning in Materials Science ; Topic Modeling ; Scientific Computing and Data Management
Authors: Seiji Takeda | Akihiro Kishimoto | Lisa Hamada | Daiju Nakano | John R. Smith
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Foundation Model for Material Science Foundation models (FMs) are achieving remarkable successes to realize complex downstream tasks in domains including natural language and visions. In this paper, we propose building an FM for material science, which is trained with massive data across a wide variety of material domains and data modalities. Nowadays machine learning models play key roles in material discovery, particularly for property prediction and structure generation. However, those models have been independently developed to address only specific tasks without sharing more global knowledge. Development of an FM for material science will enable overarching modeling across material domains and data modalities by sharing their feature representations. We discuss fundamental challenges and required technologies to build an FM from the aspects of data preparation, model development, and downstream tasks. Computer science ; Modalities ; Variety (cybernetics) ; Vision ; Foundation (evidence) ; Key (lock) ; Data science ; Feature (linguistics) ; Property (philosophy) ; Data sharing ; Downstream (manufacturing) ; Artificial intelligence ; Human–computer interaction ; Engineering Machine Learning in Materials Science
Authors: Wenjia Zhang | Xingyu Zi | Jinqiang Bi | Guohua Liu | H. W. Cheng | Kexin Bao | Qin Liu | Wei Wang
Source: Nanomaterials (review, 2023-07-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic Nanomaterials in Dark Field Sensing Systems Plasma nanoparticles offer promise in data storage, biosensing, optical imaging, photoelectric integration, etc. This review highlights the local surface plasmon resonance (LSPR) excitation mechanism of plasmonic nanoprobes and its critical significance in the control of dark-field sensing, as well as three main sensing strategies based on plasmonic nanomaterial dielectric environment modification, electromagnetic coupling, and charge transfer. This review then describes the component materials of plasmonic nanoprobes based on gold, silver, and other noble metals, as well as their applications. According to this summary, researchers raised the LSPR performance of composite plasmonic nanomaterials by combining noble metals with other metals or oxides and using them in process analysis and quantitative detection. Plasmon ; Nanomaterials ; Materials science ; Nanotechnology ; Surface plasmon resonance ; Biosensor ; Nanoparticle ; Dielectric ; Plasmonic nanoparticles ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Jing Zhu | Xuefeng Jiang
Source: Aggregate (article, 2023-11-09)
Field: Materials Science — Materials Chemistry

How does aggregation‐induced emission aggregate interdisciplinary research? Abstract It is a matter of debate whether the discipline independence in discipline formation narrows its interdisciplinarity. It is also less well understood how disruptive works emerge in investigative practice rather than a theory‐driven approach. Aggregation‐induced emission (AIE) is an atypical photophysical phenomenon, in which the whole (aggregate) is brighter than the sum of its parts (single molecule). Through measuring and computing the cognitive extent and evolution of research on AIE, including topics, epistemic‐social collaborative networks, interdisciplinarity, emergent concepts, core concept networks and knowledge flow, this study shows that a cross‐research scales concept and its practice can establish new bridges in the sciences and promote disruptive work. Focusing on mesoscale entities, scientists from many different branches of science are involved in theoretical research on mechanisms, as well as developing different AIE systems for applications. The data analysis in this study provides details showing how non‐reductionist concepts based on new scientific discoveries cross traditional disciplinary boundaries and aggregate interdisciplinary research. The emergence and evolution of the AIE field implies that scientists may be motivated to embrace nonreductionist ideas at different research scales, leading to a more permeable field boundary. Reductionism ; Discipline ; Epistemology ; Aggregate (composite) ; Data science ; Field (mathematics) ; Computer science ; Sociology ; Cross disciplinary ; Independence (probability theory) ; Cognitive science ; Management science ; Social science ; Psychology ; Nanotechnology ; Engineering ; Philosophy ; Mathematics Luminescence and Fluorescent Materials ; Biotin and Related Studies ; bioluminescence and chemiluminescence research
Authors: Kevin Spiekermann | Thijs Stuyver | Lagnajit Pattanaik | William H. Green
Source: Machine Learning Science and Technology (article, 2023-10-06)
Field: Materials Science — Materials Chemistry

Comment on ‘Physics-based representations for machine learning properties of chemical reactions’ Abstract In a recent article in this journal, van Gerwen et al (2022 Mach. Learn.: Sci. Technol. 3 045005) presented a kernel ridge regression model to predict reaction barrier heights. Here, we comment on the utility of that model and present references and results that contradict several statements made in that article. Our primary interest is to offer a broader perspective by presenting three aspects that are essential for researchers to consider when creating models for chemical kinetics: (1) are the model’s prediction targets and associated errors sufficient for practical applications? (2) Does the model prioritize user-friendly inputs so it is practical for others to integrate into prediction workflows? (3) Does the analysis report performance on both interpolative and more challenging extrapolative data splits so users have a realistic idea of the likely errors in the model’s predictions? Perspective (graphical) ; Computer science ; Machine learning ; Workflow ; Kernel (algebra) ; Artificial intelligence ; Data science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Binglei Zhang | Davide Grassano | Olivia Pulci | Yang Liu | Yi Luo | Adriano Mosca Conte | F. V. Kusmartsev | Anna Kusmartseva
Source: npj 2D Materials and Applications (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Covalent bonded bilayers from germanene and stanene with topological giant capacitance effects Abstract The discovery of twisted bilayer graphene with tunable superconductivity has diverted great focus at the world of twisted van der Waals heterostructures. Here we propose a paradigm for bilayer materials, where covalent bonding replaces the van der Waals interaction between the layers. On the example of germanene-stanene bilayer, we show that such systems demonstrate fascinating topological properties and manifest giant capacitance effects of the order of C = 10 2 μ F as well as dipole-like charge densities of q = 1 − 2 × 10 −4 μ C cm −2 , showing promise for 2D ferroelectricity. The observed unique behaviour is closely linked to transverse strain-induced buckling deformations at the bilayer/substrate interface. In alternative GeSn bilayer structures with low twist angles the strain distortions trigger rich topological defect physics. We propose that the GeSn bilayer topology may be switched locally by a substrate-strain-induced electric fields. We demonstrate an approach to fabricate covalent bilayer materials, holding vast possibilities to transform applications technologies across solar, energy and optoelectronic sectors. Germanene ; Bilayer ; van der Waals force ; Bilayer graphene ; Condensed matter physics ; Materials science ; Substrate (aquarium) ; Graphene ; Topology (electrical circuits) ; Capacitance ; Dipole ; Nanotechnology ; Silicene ; Physics ; Chemistry ; Molecule ; Quantum mechanics Graphene research and applications ; Topological Materials and Phenomena ; 2D Materials and Applications
Authors: А. Н. Коровин | Innokentiy S. Humonen | Artem I. Samtsevich | Roman A. Eremin | A.I. Vasilev | V.D. Lazarev | Semen Budennyy
Source: Materials Today Chemistry (article, 2023-04-21)
Field: Materials Science — Materials Chemistry

Boosting heterogeneous catalyst discovery by structurally constrained deep learning models The discovery of new catalysts is one of the significant topics of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources. Recently developed deep learning approaches such as graph neural networks open new opportunity to significantly extend scope for modeling novel high-performance catalysts. Nevertheless, the graph representation of a particular crystal structure is not a straightforward task due to the ambiguous connectivity schemes and numerous embeddings of nodes and edges. Here, we present embedding improvement for graph neural networks that has been modified by Voronoi tessellation and is able to predict the energy of catalytic systems within the Open Catalyst Project dataset. The enrichment of the graph was calculated via Voronoi tessellation, and the corresponding contact solid angles and types (direct/indirect) were considered as edges’ features, and Voronoi volumes were used as node characteristics. The auxiliary approach was enriching node representation by intrinsic atomic properties (electronegativity, period, and group position). The proposed modifications allowed us to improve the mean absolute error of the original model, and the final error equals to 651 meV on the Open Catalyst Project dataset and 6 meV/atom on the intermetallics dataset. Also, by the consideration of an additional dataset, we show that a sensible choice of data can decrease the error to values below a physically-based 20 meV/atom threshold. Voronoi diagram ; Embedding ; Graph ; Computer science ; Catalysis ; Artificial neural network ; Boosting (machine learning) ; Graph embedding ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Chemistry ; Geometry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts