jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Srinivasan V | Aravindhan D | Jeeva T | Sambasivam J | Keerthana Salas E
Source: Open MIND (article, 2022-02-19)
Field: Materials Science — Materials Chemistry

AI Based Water Treatment using Graphene Nano-Material Increasing population and industrial growth, combined with climate change, led to global water problems. The graphene-based membrane materials that are used for water purification using Artificial Intelligene(AI). Graphene-based membrane materials are believed to be the advanced materials for the water treatment process because of their atomic thickness and tunable functionalities. The effect of membrane fabrication techniques and operating conditions on the separation and membrane fouling mechanisms are discussed in depth. To extend the applications of graphene membranes, more attention should be paid to the water permeability and membrane selectivity in long-term operation at conditions similar to those in the field for performance analysis using AI. While literature reviews have mainly concerned the ability of graphene materials to adsorb water contaminants, there is very limited updated information by artificial intelligence, related to its sieving properties. Moreover, the literature lacks a comparative discussion of graphene-based membranes with other hybrid membrane materials. Thus, a profound understanding of graphene-based membranes is much needed to unlock the potential for graphene materials using Artificial Intelligence(AI). Graphene ; Membrane ; Water treatment ; Fouling ; Membrane technology ; Population ; Adsorption ; Synthetic membrane Graphene research and applications ; Membrane Separation Technologies ; Graphene and Nanomaterials Applications
Authors: Scott Broderick | Kaito Miyamoto | Krishna Rajan
Source: arXiv (Cornell University) (preprint, 2022-03-16)
Field: Materials Science — Materials Chemistry

High-Throughput Computation of Li-based Battery Material Databases: Chemistry-Processing-Property Relationships In this paper, we incorporate processing into the data driven property modeling of Li-based spinel battery materials. When considering spinel compounds of the form LiMe2O4 with Me as a metal or metals, there are 125,000 possible combinations assuming a maximum of three metallic elements. In this work, we focus on capacity for our predicted property, and increase the number of possible combinations to two million by incorporating processing into the modeling. Due to the non-linear relationship between processing and property, as well as ensuring proper sensitivity, a new approach which tracks the change with changing processing is introduced. This work provides an invaluable tool for guiding the next generation of battery experiments, providing a significant narrowing of the infinite design space. Property (philosophy) ; Spinel ; Battery (electricity) ; Computation ; Throughput ; Computer science ; Work (physics) ; Sensitivity (control systems) ; Focus (optics) ; Computational science ; Database ; Process engineering ; Materials science ; Algorithm ; Mechanical engineering ; Engineering ; Electronic engineering ; Physics ; Metallurgy ; Thermodynamics Machine Learning in Materials Science
Authors: Poonam Sundriyal
Source: ECS Meeting Abstracts (article, 2022-10-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

(Invited, Digital Presentation) 3D Printing of Flexible and Wearable Supercapacitors Flexible and wearable electronics have recently emerged as a potential solution for next-generation electronics for healthcare, sports, transport, military, soft robotics, artificial intelligence, the internet of things, and other applications. However, these devices are still at a fledgling stage due to the use of traditional manufacturing processes, lack of compatible power supply, poor commercialization capabilities, and integration problems. Here, we present 3D printing approaches for developing batteries and supercapacitors for flexible electronic applications. Such manufacturing techniques can revolutionize the rapidly growing field of flexible and wearable electronics due to their several attributes like; rapid production, simplicity, capability to produce size and shape versatile patterns, computer-aided design, user control, and environmental friendliness.(1-4) Different aspects of 3D printed supercapacitors (such as; device design, ink preparation of electrode/ electrolyte components, rheology control of inks, surface modification to get good print quality, electrochemical performance of printed devices, and flexibility analysis) will be discussed in detail with a focus on future requirements. References Sundriyal P, Bhattacharya S. Scalable micro-fabrication of flexible, solid-state, inexpensive, and high-performance planar micro-supercapacitors through inkjet printing. ACS Applied Energy Materials. 2019;2(3):1876-90. Sundriyal P, Bhattacharya S. Inkjet-printed electrodes on A4 paper substrates for low-cost, disposable, and flexible asymmetric supercapacitors. ACS Applied Materials &amp; Interfaces. 2017;9(44):38507-21. Sundriyal P, Bhattacharya S, editors. 3-D Printed Electrode Materials for Low-Cost, Flexible, and Stretchable Energy Storage Devices. ECS Meeting Abstracts; 2019: IOP Publishing. Sundriyal P, Bhattacharya S. Textile-based supercapacitors for flexible and wearable electronic applications. Scientific reports. 2020;10(1):1-15. Supercapacitor ; Electronics ; Wearable computer ; Printed electronics ; Wearable technology ; 3D printing ; Flexibility (engineering) ; Nanotechnology ; Flexible electronics ; Computer science ; Manufacturing engineering ; Electrical engineering ; Materials science ; Engineering ; Mechanical engineering ; Embedded system ; Capacitance ; Electrode Supercapacitor Materials and Fabrication ; Electrospun Nanofibers in Biomedical Applications
Authors: Laura Howes﻿
Source: C&EN Global Enterprise (article, 2022-04-25)
Field: Chemistry — Physical and Theoretical Chemistry

Artful chemistry Musical molecules A quick glance through past instances of this column will tell the reader that Newscripts can often take a musical turn . But at the University of Michigan, Timothy Cernak has added a new riff by incorporating effects such as pedals and synths to his lab kit. Instruments they certainly are, but chemical instrumentation? Maybe, if you want to use melody to design molecules, Cernak says. Cernak is an expert in designing drugs using artificial intelligence. That requires computational ways to describe compounds, such as string notations or matrices of molecular data. He says computers can read these descriptors, but they’re less immediately recognizable to human chemists. Wondering if these representations could be constraining how he and other chemists think about molecules, Cernak started thinking about the problem of how to encode all the structural information . If the medium chemists use to describe a molecule influences how Chemistry ; Polymer science Various Chemistry Research Topics
Authors: Ariana Remmel
Source: C&EN Global Enterprise (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

Chemically clumsy AI and a fantastical catalyst killer Artistic AI imagines atoms Several Newscriptsters have noticed our social media feeds filling up with whimsical pictures produced by Dall-E Mini , which was recently renamed Craiyon. This artificial intelligence generates images from language prompts such as “sharks painted by Van Gogh” or “Queen Elizabeth II shopping at Target.” To the delight of Twitter users around the world, many of the results are passable—or at least entertaining. So science Twitter did as science Twitter does and tested the AI’s scientific savvy. “I was curious to see if it had some kind of chemical understanding,” says computational scientist Victor Fung. He tried seeing if Craiyon could generate images of crystal structures and aromatic carbons. “I wasn’t expecting very much,” he tells Newscripts. Rightly so. The AI crunched numbers in a minute of silent contemplation, but the best it could do was produce networks of painted orbs that bear a passing resemblance Contemplation ; Queen (butterfly) ; Art ; Art history ; Visual arts ; Computer science ; Philosophy ; Ecology ; Epistemology ; Biology Machine Learning in Materials Science
Authors: Katsuaki Yanagiuchi
Source: Vacuum and Surface Science (article, 2022-03-09)
Field: Materials Science — Surfaces, Coatings and Films

Current Status and Future of Surface Analysis and Micro-beam Analysis in the Industrial Field In order to utilize data science such as artificial intelligence (AI) and materials informatics (MI) for measurement analysis data, it is necessary to use correct data. To do this, the sample must be handled correctly and cross-checked in a combined analysis. We will describe the combined measurement and analysis by the Cyber Physical System (CPS) for that purpose, and give an overview of the future of analysis technology aiming to create high-value-added data. Field (mathematics) ; Data science ; Sample (material) ; Computer science ; Data analysis ; Informatics ; Data mining ; Engineering ; Electrical engineering ; Mathematics ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques ; Advanced Surface Polishing Techniques
Authors: Takashi Miyake | Yosuke HARASHIMA | Taro Fukazawa | H. Akai
Source: Journal of the Japan Society of Powder and Powder Metallurgy (article, 2022-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Understanding and Optimization of Hard Magnetic Compounds from First Principles First-principles calculation based on density functional theory is a powerful tool for understanding and designing magnetic materials. It enables us to quantitatively describe magnetic properties and structural stability, although further methodological developments for the treatment of strongly-correlated 4f electrons and finite-temperature magnetism are needed. Here, we review recent developments of computational schemes for rare-earth magnet compounds, and summarize our theoretical studies on Nd2Fe14B and RFe12-type compounds. Effects of chemical substitution and interstitial dopants are clarified. We also discuss how data-driven approaches are used for studying multinary systems. Chemical composition can be optimized with fewer trials by the Bayesian optimization. We also present a data-assimilation method for predicting finite-temperature magnetization in wide composition space by integrating computational and experimental data. Magnetism ; Magnet ; Density functional theory ; Stability (learning theory) ; Experimental data ; Magnetization ; Computer science ; Materials science ; Statistical physics ; Physics ; Computational chemistry ; Condensed matter physics ; Chemistry ; Magnetic field ; Mathematics ; Quantum mechanics ; Machine learning Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Rare-earth and actinide compounds
Authors: Jack Brook | G. Payling Wright | Laura M. Bartolo | James A. Warren | Ralf B. Wehrspohn
Source: Materials Open Research (article, 2022-05-04)
Field: Materials Science — Materials Chemistry

Leading an open future for materials science research: Introducing Materials Open Research <ns4:p>Materials science is increasingly at the forefront of both academic and societal progress. It is clear that these advances will continue to be shaped by cutting-edge research from across this field. As a discipline, materials science has rapidly grown in profile as one of the most vibrant and pioneering areas of study, resulting in substantial growth in the numbers of published articles and associated research materials. In order to expedite these discoveries and maximize their impact, ensuring the availability of open and reproducible materials science research is vitally important, as well as embracing the opportunities provided by emerging cross-disciplinary fields such as artificial intelligence. The open sharing of materials research and data can encourage further exploration, improving collaboration between researchers and, ultimately, accelerating the discovery, design, and deployment of new materials as well as new business models. Materials Open Research, a new publication venture from publishers F1000 and Taylor &amp; Francis, has been conceived and launched with these principles in mind. All research shared on the platform will aim to provide impact, nurture innovation, and lead to a more sustainable future.</ns4:p> Open science ; Engineering ethics ; Open innovation ; Order (exchange) ; Software deployment ; Discipline ; Political science ; Engineering ; Nanotechnology ; Knowledge management ; Business ; Sociology ; Computer science ; Social science ; Materials science Machine Learning in Materials Science
Authors: Kevin Rossi | Daniel Georg Foerster | Claudio Zeni | Julien Lam
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Modeling and Characterization of the Nucleation and Growth of Carbon Nanostructures in Physical Synthesis Nucleation ; Characterization (materials science) ; Materials science ; Nanotechnology ; Nanostructure ; Carbon fibers ; Chemical engineering ; Chemical physics ; Chemistry ; Engineering ; Composite material ; Organic chemistry ; Composite number Carbon Nanotubes in Composites ; Advanced Physical and Chemical Molecular Interactions ; Graphene research and applications
Authors: María Luisa Vázquez de Ágredos-Pascual | R. Herráez‐Hernández | P. Campı́ns-Falcó | Lucía Rojo Iranzo | Ester Alba Pagán
Source: Ge-conservacion (article, 2022-06-16)
Field: Materials Science — Biomaterials

Color study of historic silks The chemical characterization of silk textiles of historic value may be necessary to achieve a better understanding of the production processes applied, to evaluate their preservation, to detect manipulations or forgeries, and to value the combination of tradition and innovation in contemporary manufacturing techniques. The main objective of this work was to study four historical silks from the Garín collection, all of them from the 20th century, although with 19th century designs. To this end, non-invasive spectroscopic and microscopy techniques were applied in order to obtain information on the dyes used during their production and their link with those used in the silk industry in previous centuries, and to ascertain their state of conservation. Moreover, the images in LM, SEM and CM are contributing to the development of a 3D virtual loom that will favor more accessible and inclusive museography in museums specialized in the silk sector. Both the study of dyes and silk fibers and their virtual reproduction through artificial intelligence programs applied to the study of cultural heritage, by specialists from the Robotics Institute of the Universitat de València, has been developed in the framework of the SILKNOW project (https://silknow.eu/). LOOM ; Cultural heritage ; SILK ; Artificial intelligence ; Value (mathematics) ; Computer science ; Engineering ; Visual arts ; Architectural engineering ; Art ; Archaeology ; History Silk-based biomaterials and applications ; melanin and skin pigmentation ; Microbial Metabolism and Applications
Authors: Hyoung Seop Kim | Pil-Ryoung Cha | Hyunjoo Choi
Source: MATERIALS TRANSACTIONS (article, 2022-09-24)
Field: Materials Science — Materials Chemistry

PREFACE From materials development to commercialization, the most time/cost-consuming step is the migration of a process from the lab-scale to the pilot plant or commercial scale.This special issue addresses recent advances in understanding, describing, and predicting material behavior during manufacturing processes for scaling the development of newly developed materials in lab to industrial levels.In general, multiple physical phenomena simultaneous take place during manufacturing, such as heat transfer, fluid flow, phase transformation, and elastic/plastic deformations.Hence, integration of material simulation models of multiple length scales is essential to capture the process-structures-properties relations of a material during manufacturing.To bring together detailed analysis, characterization as well as computational modeling of materials from atomistic to macroscopic length scales, the International Symposium on Innovation in Materials Processing (ISIMP 2021) has been held on 2629, October 2021 in Jeju Island in South Korea.Articles in this special issue were selected from presentations in this conference, which cover process modelling and optimization techniques such as numerical studies, discrete element method, phase-field modelling, artificial intelligence (AI), and design of experiments (DOE). Materials science ; Engineering ethics ; Engineering Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Microstructure and Mechanical Properties of Steels
Authors: Mojtaba Ghodsi | Morteza Mohammadzaheri | Payam Soltani | Hamidreza Ziaifar
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A New Active Anti-Vibration System Using a Magnetostrictive Bimetal Actuator Bimetal ; Magnetostriction ; Actuator ; Vibration ; Structural engineering ; Active vibration control ; Materials science ; Mechanical engineering ; Engineering ; Acoustics ; Vibration control ; Electrical engineering ; Composite material ; Physics Magnetic Properties and Applications ; Aeroelasticity and Vibration Control ; Shape Memory Alloy Transformations
Authors: Yiming Chen | Xiaobing Hu | Jiajun Zhao | Zhijun Wang | Junjie Li | Jincheng Wang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Symbolic Regression and Two-Point Statistics Assisted Structure-Property Linkage Based on Irregular-Representative Volume Element Linkage (software) ; Property (philosophy) ; Statistics ; Volume (thermodynamics) ; Regression ; Mathematics ; Regression analysis ; Econometrics ; Physics ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Shinji Kohara
Source: Vacuum and Surface Science (article, 2022-10-09)
Field: Materials Science — Ceramics and Composites

Extracting Order within Disorder in Glassy Materials The structural study of glassy materials remains still a challenging scientific topic owing to the lack of distinct translational periodicity. However, a combination of the advent of quantum (synchrotron X-ray/neutron) beam instrumentation and data-driven structural modelling assisted by advanced computational/mathematical techniques enables us to probe the order within disorder in glassy materials. In particular, it is possible to reveal the structure beyond the nearest-neighbour distance by structural modelling. In this article, we focus on structural studies of oxide glasses and liquids by quantum beam diffraction and data-driven structural modelling. Neutron diffraction ; Synchrotron ; Materials science ; Quantum ; Diffraction ; Structural material ; Nanotechnology ; Order (exchange) ; Statistical physics ; Physics ; Optics ; Quantum mechanics ; Composite material Glass properties and applications ; Material Dynamics and Properties ; Mineralogy and Gemology Studies
Authors: Aya M. A. ElEbshihy | Roshdy A. Abdelrassoul
Source: 2022 International Telecommunications Conference (ITC-Egypt) (article, 2022-07-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Retracted: Modeling of Supercapacitors Utilizing Deoxyribonucleic Acid Neural Network Lately, the interest in more storage capacity has been creating a result of their use in various applications that use supercapacitors. The fundamental objective of this assessment is to investigate and depict the components of electrochemical execution of graphene-based supercapacitors. An illustration of a supercapacitor was organized using a terminal of graphene layer covered with a main polymer layer (polyaniline). In this paper, a proposed approach for predicting the charge move resistance of supercapacitor subject to DNA (Deoxyribonucleic acid) figuring Brain Organization (DNANN) is introduced. Animated by the new achievement of using Artificial Intelligence(AI) computations in work assessment, and concentrate critical level features from data, we use this proposed brain association to the issue of recognizing the repeat - impedance association and to choose a similar course of action resistance of the supercapacitor so that to evaluate its power capacity. The alleged knee repeats of the supercapacitor which is the essential repeat where all surface locale is submerged is in like manner expected. Supercapacitor ; Computer science ; Artificial neural network ; Graphene ; Layer (electronics) ; Polyaniline ; Materials science ; Nanotechnology ; Artificial intelligence ; Capacitance ; Polymer ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Tsuyoshi Sekitani
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Polymers and Plastics

(Invited, Digital Presentation) Ultra-Thin Organic Integrated Circuits Enabling Bio-Signal Monitoring Digital technology has permeated our society, and a wide variety of electronic devices are now in use. In particular, the development of electronic devices for biometric measurements, such as wearable electronics, has been remarkable, and coupled with research and development of high-speed communication and artificial intelligence (AI), many social implementations are being presented. Our group has been conducting research and development on flexible and stretchable electronic systems, which are flexible, soft like rubber, and lightweight, by integrating functional organic nano-materials. In this research activity, our flexible and stretchable electronics have obtained certification for medical devices and are promoting the development of new electronics for use in medical institutions. In this presentation, I would like to introduce our recent activities on the flexible and stretchable electronics utilizing the nanoscience and technology, and developed low-noise and ultra-flexible systems for measuring biological action potentials (electroencephalogram; EEG and electrocardiogram ; ECG). Electronics ; Stretchable electronics ; Computer science ; Wearable technology ; Implementation ; Wearable computer ; Digital electronics ; Organic electronics ; Presentation (obstetrics) ; Electronic circuit ; Electrical engineering ; Systems engineering ; Engineering ; Embedded system ; Transistor ; Voltage Conducting polymers and applications