jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ander García Díez | Mikel Rincón‐Iglesias | S. Lanceros‐Méndez | Javier Reguera | Erlantz Lizundia
Source: Materials Today Chemistry (article, 2022-10-22)
Field: Chemistry — Organic Chemistry

Multicomponent magnetic nanoparticle engineering: the role of structure-property relationship in advanced applications Combining magnetic nanomaterials with materials of other classes can produce multicomponent nanoparticles with an entire ensemble of new structures and unique, enhanced, synergetic, and/or complementary functionalities. Here we discuss the most recent developments in the synthesis of multicomponent magnetic nanoparticles, describe the resulting structures and their novel properties, and explore their application in a variety of fields, including multimodal imaging, nanomedicine, sensing, surface-enhanced Raman scattering, and heterogeneous catalysis. The current synthetic methods (usually bottom-up approaches) of multicomponent nanoparticles can produce a number of tailored morphologies ([email protected], yolk-shell, core-satellite, Janus, nanochains, anisotropic, etc.), making them invaluable for applications in biology, medicine, chemistry, physics, and engineering. But like any new technology, their synthesis methods need to be optimized to be simple, scalable, and as environmentally friendly as possible before they can be widely adopted. In particular, the use of life cycle assessment (LCA) to guide future works toward environmental sustainability is highlighted. Overall, this review not only presents a critical and timely summary of the state-of-the-art of this burgeoning field in both fundamental and applied nanotechnology, but also addresses the challenges associated with understanding the particular structure-property relationships of multicomponent magnetic nanoparticles. Nanotechnology ; Nanomedicine ; Nanoparticle ; Nanoengineering ; Materials science ; Nanomaterials ; Magnetic nanoparticles ; Computer science Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Nanocluster Synthesis and Applications
Authors: Andrew C. Jones | Jamie A. Leitch | Sarah E. Raby‐Buck | Duncan L. Browne
Source: Nature Synthesis (article, 2022-07-21)
Field: Chemistry — Physical and Theoretical Chemistry

Mechanochemical techniques for the activation and use of zero-valent metals in synthesis Catalysis ; Chemical reaction ; Ball mill ; Raw material ; Transition metal ; Nanotechnology ; Materials science ; Chemical process ; Chemistry ; Chemical engineering ; Metallurgy ; Organic chemistry ; Engineering Crystallography and molecular interactions ; Bone Tissue Engineering Materials ; Boron Compounds in Chemistry
Authors: Lenz Fiedler | Karan Shah | Michael Bußmann | Attila Cangi
Source: Physical Review Materials (article, 2022-04-05)
Field: Materials Science — Materials Chemistry

Deep dive into machine learning density functional theory for materials science and chemistry Electronic structure simulations enable the calculation of a wide variety of fundamental materials properties. However, they consume a significant portion of scientific HPC resources worldwide. Artificial intelligence and machine learning, which have emerged as a powerful tool for analyzing complex datasets, have the potential to accelerate electronic structure calculations such as density functional theory. The combination of these two fields enables highly efficient simulations at unprecedented scales. In this review, the authors present a comprehensive analysis of research articles in chemistry and materials science that employ machine-learning techniques and outline the current trends at the intersection of these fields. Density functional theory ; Artificial intelligence ; Machine learning ; Computer science ; Categorization ; Computational learning theory ; Field (mathematics) ; Data science ; Scope (computer science) ; Nanotechnology ; Materials science ; Active learning (machine learning) ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Sadman Sadeed Omee | Steph-Yves Louis | Nihang Fu | Lai Wei | Sourin Dey | Rongzhi Dong | Qinyang Li | Jianjun Hu
Source: Patterns (article, 2022-04-27)
Field: Materials Science — Materials Chemistry

Scalable deeper graph neural networks for high-performance materials property prediction Machine-learning-based materials property prediction models have emerged as a promising approach for new materials discovery, among which the graph neural networks (GNNs) have shown the best performance due to their capability to learn high-level features from crystal structures. However, existing GNN models suffer from their lack of scalability, high hyperparameter tuning complexity, and constrained performance due to over-smoothing. We propose a scalable global graph attention neural network model DeeperGATGNN with differentiable group normalization (DGN) and skip connections for high-performance materials property prediction. Our systematic benchmark studies show that our model achieves the state-of-the-art prediction results on five out of six datasets, outperforming five existing GNN models by up to 10%. Our model is also the most scalable one in terms of graph convolution layers, which allows us to train very deep networks (e.g., >30 layers) without significant performance degradation. Our implementation is available at https://github.com/usccolumbia/deeperGATGNN. Scalability ; Computer science ; Hyperparameter ; Graph ; Normalization (sociology) ; Artificial intelligence ; Smoothing ; Differentiable function ; Machine learning ; Artificial neural network ; Property (philosophy) ; Deep learning ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Mathias Schreiner | Arghya Bhowmik | Tejs Vegge | Jonas Busk | Ole Winther
Source: Scientific Data (article, 2022-12-24)
Field: Materials Science — Materials Chemistry

Transition1x - a dataset for building generalizable reactive machine learning potentials Machine Learning (ML) models have, in contrast to their usefulness in molecular dynamics studies, had limited success as surrogate potentials for reaction barrier search. This is primarily because available datasets for training ML models on small molecular systems almost exclusively contain configurations at or near equilibrium. In this work, we present the dataset Transition1x containing 9.6 million Density Functional Theory (DFT) calculations of forces and energies of molecular configurations on and around reaction pathways at the ωB97x/6-31 G(d) level of theory. The data was generated by running Nudged Elastic Band (NEB) with DFT on 10k organic reactions of various types while saving intermediate calculations. We train equivariant graph message-passing neural network models on Transition1x and cross-validate on the popular ANI1x and QM9 datasets. We show that ML models cannot learn features in transition state regions solely by training on hitherto popular benchmark datasets. Transition1x is a new challenging benchmark that will provide an important step towards developing next-generation ML force fields that also work far away from equilibrium configurations and reactive systems. Machine learning ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Reservoir Engineering and Simulation Methods
Authors: Chaitanya Gadre | Xingxu Yan | Qichen Song | Jie Li | Lei Gu | Huaixun Huyan | Toshihiro Aoki | Sheng‐Wei Lee | Gang Chen | Ruqian Wu | Xiaoqing Pan
Source: Nature (article, 2022-06-08)
Field: Materials Science — Materials Chemistry

Nanoscale imaging of phonon dynamics by electron microscopy Spatially resolved vibrational mapping of nanostructures is indispensable to the development and understanding of thermal nanodevices<sup>1</sup>, modulation of thermal transport<sup>2</sup> and novel nanostructured thermoelectric materials<sup>3-5</sup>. Through the engineering of complex structures, such as alloys, nanostructures and superlattice interfaces, one can significantly alter the propagation of phonons and suppress material thermal conductivity while maintaining electrical conductivity<sup>2</sup>. There have been no correlative experiments that spatially track the modulation of phonon properties in and around nanostructures due to spatial resolution limitations of conventional optical phonon detection techniques. Here we demonstrate two-dimensional spatial mapping of phonons in a single silicon-germanium (SiGe) quantum dot (QD) using monochromated electron energy loss spectroscopy in the transmission electron microscope. Tracking the variation of the Si optical mode in and around the QD, we observe the nanoscale modification of the composition-induced red shift. We observe non-equilibrium phonons that only exist near the interface and, furthermore, develop a novel technique to differentially map phonon momenta, providing direct evidence that the interplay between diffuse and specular reflection largely depends on the detailed atomistic structure: a major advancement in the field. Our work unveils the non-equilibrium phonon dynamics at nanoscale interfaces and can be used to study actual nanodevices and aid in the understanding of heat dissipation near nanoscale hotspots, which is crucial for future high-performance nanoelectronics. Phonon ; Materials science ; Nanoscopic scale ; Nanostructure ; Thermoelectric effect ; Condensed matter physics ; Nanoelectronics ; Thermoelectric materials ; Superlattice ; Thermal conductivity ; Ballistic conduction ; Nanotechnology ; Optoelectronics ; Electron ; Physics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Advanced Thermoelectric Materials and Devices
Authors: Olesja Starkova | Abedin I. Gagani | Christian W. Karl | I.B.C.M. Rocha | Juris Burlakovs | Andrey E. Krauklis
Source: Polymers (review, 2022-02-24)
Field: Materials Science — Polymers and Plastics

Modelling of Environmental Ageing of Polymers and Polymer Composites—Durability Prediction Methods Polymers and polymer composites are negatively impacted by environmental ageing, reducing their service lifetimes. The uncertainty of the material interaction with the environment compromises their superior strength and stiffness. Validation of new composite materials and structures often involves lengthy and expensive testing programs. Therefore, modelling is an affordable alternative that can partly replace extensive testing and thus reduce validation costs. Durability prediction models are often subject to conflicting requirements of versatility and minimum experimental efforts required for their validation. Based on physical observations of composite macroproperties, engineering and phenomenological models provide manageable representations of complex mechanistic models. This review offers a systematised overview of the state-of-the-art models and accelerated testing methodologies for predicting the long-term mechanical performance of polymers and polymer composites. Accelerated testing methods for predicting static, creep, and fatig ue lifetime of various polymers and polymer composites under environmental factors' single or coupled influence are overviewed. Service lifetimes are predicted by means of degradation rate models, superposition principles, and parametrisation techniques. This review is a continuation of the authors' work on modelling environmental ageing of polymer composites: the first part of the review covered multiscale and modular modelling methods of environmental degradation. The present work is focused on modelling engineering mechanical properties. Durability ; Composite material ; Materials science ; Polymer ; Ageing ; Polymer science Polymer crystallization and properties ; Asphalt Pavement Performance Evaluation ; Mechanical Behavior of Composites
Authors: M. R. M. Asyraf | Agusril Syamsir | N. M. Zahari | Mohd Supian Abu Bakar | Mohamad Ridzwan Ishak | S.M. Sapuan | Shubham Sharma | Ahmad Rashedi | Muhammad Rizal Razman | Sharifah Zarina Syed Zakaria | R.A. Ilyas | Mohamad Zakir Abd Rashid
Source: Polymers (review, 2022-02-25)
Field: Materials Science — Polymers and Plastics

Product Development of Natural Fibre-Composites for Various Applications: Design for Sustainability New product development review article aims to consolidate the principles and current literature on design for sustainability to seek the field's future direction. In this point of view, the design for sustainability methods can be established under the idea of sustainability in dimensions of ecology, economy and social pillars. Design for sustainability concept is implemented in concurrent engineering, including concept, embodiment and detail design processes. Integrating sustainability in engineering designs is crucial to producing greener products, system innovation, and services aligned with current market demand. Currently, many concurrent engineering studies related to natural fibre-reinforced polymer composites associated with sustainability enhance the application of design for sustainability techniques by professional designers. However, the current literature is scarce in bridging the design for sustainability concept with concurrent engineering during the design development stage, and these areas should be further developed. Several other future research directions, such as the need for aligning with principles and applications, along with exploring the relationships between the design for sustainability techniques and views of sustainability, are presented in this review paper. Sustainability ; Composite material ; Materials science ; Product design ; New product development ; Product (mathematics) ; Business ; Mathematics Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Sustainable Design and Development
Authors: Preeti Agarwal | Renu Soni | Pritam Kaur | Akanksha Madan | Reema Mishra | Jayati Pandey | Shreya Singh | Indrakant K. Singh
Source: Frontiers in Microbiology (review, 2022-07-13)
Field: Materials Science — Biomaterials

Cyanobacteria as a Promising Alternative for Sustainable Environment: Synthesis of Biofuel and Biodegradable Plastics With the aim to alleviate the increasing plastic burden and carbon footprint on Earth, the role of certain microbes that are capable of capturing and sequestering excess carbon dioxide (CO<sub>2</sub>) generated by various anthropogenic means was studied. Cyanobacteria, which are photosynthetic prokaryotes, are promising alternative for carbon sequestration as well as biofuel and bioplastic production because of their minimal growth requirements, higher efficiency of photosynthesis and growth rates, presence of considerable amounts of lipids in thylakoid membranes, and cosmopolitan nature. These microbes could prove beneficial to future generations in achieving sustainable environmental goals. Their role in the production of polyhydroxyalkanoates (PHAs) as a source of intracellular energy and carbon sink is being utilized for bioplastic production. PHAs have emerged as well-suited alternatives for conventional plastics and are a parallel competitor to petrochemical-based plastics. Although a lot of studies have been conducted where plants and crops are used as sources of energy and bioplastics, cyanobacteria have been reported to have a more efficient photosynthetic process strongly responsible for increased production with limited land input along with an acceptable cost. The biodiesel production from cyanobacteria is an unconventional choice for a sustainable future as it curtails toxic sulfur release and checks the addition of aromatic hydrocarbons having efficient oxygen content, with promising combustion potential, thus making them a better choice. Here, we aim at reporting the application of cyanobacteria for biofuel production and their competent biotechnological potential, along with achievements and constraints in its pathway toward commercial benefits. This review article also highlights the role of various cyanobacterial species that are a source of green and clean energy along with their high potential in the production of biodegradable plastics. Bioplastic ; Biochemical engineering ; Biofuel ; Cyanobacteria ; Photosynthesis ; Petrochemical ; Environmental science ; Polyhydroxyalkanoates ; Bioenergy ; Carbon sequestration ; Pulp and paper industry ; Biotechnology ; Waste management ; Carbon dioxide ; Ecology ; Environmental engineering ; Engineering ; Biology ; Botany biodegradable polymer synthesis and properties ; Enzyme Catalysis and Immobilization ; Algal biology and biofuel production
Authors: Mohsen Tamtaji | Hanyu Gao | Md Delowar Hossain | Patrick Ryan Galligan | Hoilun Wong | Zhenjing Liu | Hongwei Liu | Yuting Cai | William A. Goddard | Zhengtang Luo
Source: Journal of Materials Chemistry A (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for design principles for single atom catalysts towards electrochemical reactions Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom catalysts (SACs) through the establishment of deep structure–activity relationships. Density functional theory ; Catalysis ; Atom (system on chip) ; Electrochemistry ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Computational chemistry ; Physical chemistry ; Embedded system ; Organic chemistry ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Terkumbur E. Gber | Hitler Louis | Aniekan E. Owen | Benjamin E. Etinwa | Innocent Benjamin | Fredrick C. Asogwa | Muyiwa Michael Orosun | Ededet A. Eno
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Heteroatoms (Si, B, N, and P) doped 2D monolayer MoS<sub>2</sub>for NH<sub>3</sub>gas detection 2D transition metal dichalcogenide MoS<sub>2</sub> monolayer quantum dots (MoS<sub>2</sub>-QD) and their doped boron (B@MoS<sub>2</sub>-QD), nitrogen (N@MoS<sub>2</sub>-QD), phosphorus (P@MoS<sub>2</sub>-QD), and silicon (Si@MoS<sub>2</sub>-QD) surfaces have been theoretically investigated using density functional theory (DFT) computation to understand their mechanistic sensing ability, such as conductivity, selectivity, and sensitivity toward NH<sub>3</sub> gas. The results from electronic properties showed that P@MoS<sub>2</sub>-QD had the lowest energy gap, which indicated an increase in electrical conductivity and better adsorption behavior. By carrying out comparative adsorption studies using m062-X, ωB97XD, B3LYP, and PBE0 methods at the 6-311G++(d,p) level of theory, the most negative values were observed from ωB97XD for the P@MoS<sub>2</sub>-QD surface, signifying the preferred chemisorption surface for NH<sub>3</sub> detection. The mechanistic studies provided in this study also indicate that the P@MoS<sub>2</sub>-QD dopant is a promising sensing material for monitoring ammonia gas in the real world. We hope this research work will provide informative knowledge for experimental researchers to realize the potential of MoS<sub>2</sub> dopants, specifically the P@MoS<sub>2</sub>-QD surface, as a promising candidate for sensors to detect gas. Monolayer ; Heteroatom ; Silicon ; Boron ; Doping ; Materials science ; Transition metal ; Nitrogen ; Quantum dot ; Optoelectronics ; Nanotechnology ; Chemistry ; Inorganic chemistry ; Organic chemistry ; Catalysis 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Quantum Dots Synthesis And Properties
Authors: Zheng Wei | Qiu He | Yan Zhao
Source: Journal of Power Sources (article, 2022-09-29)
Field: Materials Science — Materials Chemistry

Machine learning for battery research Battery (electricity) ; Workflow ; Computer science ; Task (project management) ; Property (philosophy) ; Service (business) ; Reliability engineering ; Artificial intelligence ; Systems engineering ; Engineering ; Database Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fuel Cells and Related Materials
Authors: Addis Fuhr | Bobby G. Sumpter
Source: Frontiers in Materials (article, 2022-03-22)
Field: Materials Science — Materials Chemistry

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated new capabilities for high-throughput virtual screening, and global optimization approaches for inverse design of materials. Recently, a relatively new branch of AI/ML, deep generative models (GMs), provide additional promise as they encode material structure and/or properties into a latent space, and through exploration and manipulation of the latent space can generate new materials. These approaches learn representations of a material structure and its corresponding chemistry or physics to accelerate materials discovery, which differs from traditional AI/ML methods that use statistical and combinatorial screening of existing materials via distinct structure-property relationships. However, application of GMs to inorganic materials has been notably harder than organic molecules because inorganic structure is often more complex to encode. In this work we review recent innovations that have enabled GMs to accelerate inorganic materials discovery. We focus on different representations of material structure, their impact on inverse design strategies using variational autoencoders or generative adversarial networks, and highlight the potential of these approaches for discovering materials with targeted properties needed for technological innovation. Generative grammar ; Chemical space ; Deep learning ; Artificial intelligence ; Computer science ; ENCODE ; Generative model ; Machine learning ; Data science ; Nanotechnology ; Materials science ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography