jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rui Fang | Dagou A. Zeze | Mehdi Keshavarz Hedayati
Source:  (article, 2023-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning assisted inverse design on mechanically tunable lateral hybrid metasurface The proliferation of artificial intelligence (AI) has resulted in a surge of metasurface design for various purposes. Our latest work [1] has introduced the lateral hybrid system for the first time, where resonators composed of two different materials are placed horizontally next to each other to form a lattice. Such design demonstrates a sensitivity that is five times greater than conventional tunable metasurfaces. With this work, we have successfully implemented an AI-assisted design process to develop an optimized lateral hybrid metasurface for structural colour applications. Our results have demonstrated that the lateral hybrid concept can enhance the performance and optical tuning of various semiconductor and high dielectric metasurfaces, such as TiO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and Si<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf>. This approach has the potential to unlock new opportunities for using metamaterials in high precision sensors with increased sensitivity, and represents a major step forward for the field. Inverse ; Metamaterial ; Resonator ; Dielectric ; Sensitivity (control systems) ; Computer science ; Lattice (music) ; Materials science ; Optoelectronics ; Physics ; Electronic engineering ; Engineering ; Mathematics ; Acoustics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Animal Vocal Communication and Behavior
Authors: Mohsen Sotoudeh | Axel Groß
Source: ECS Meeting Abstracts (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Enhancing Ion Mobility in Solids with Physics-Informed and AI-Driven Descriptors The field of sustainable energy storage and conversion has witnessed increasing interest in ionic conductors due to their potential applications in electrochemical devices such as electrodes and solid electrolytes. In this study, we employed a combination of density functional theory investigations and artificial intelligence techniques to develop a robust descriptor for ion mobility [1,2]. Our descriptor incorporates factors like structural distortions, electronegativity, ionic radii, and oxidation states. By investigating various charge carrier chemistries and the anion and cation chemistries of the host lattice, we discovered that migration barriers are interconnected through linear scaling relations. Building upon this descriptor, we conducted a comprehensive study that employed both theoretical and experimental approaches to identify cathode materials with enhanced mobility for Mg-ions. Our focus was on oxide spinels, and we employed periodic density functional theory calculations to analyze the migration of Mg 2+ ions and evaluate the electronic, geometrical, and stability properties of different transition metals within the spinel structures. Our findings reveal that electronegative transition metals, such as Rh, facilitate high mobility of Mg-ions and promote favorable cathode functionality within oxide spinel frameworks [3]. Based on these insights, we identified MgRh 2 O 4 as a promising candidate and proceeded to prepare and characterize it structurally. This study opens up new possibilities for utilizing functional cathode materials with improved transport properties in battery-related applications. [1] M. Sotoudeh and A. Groß JACS Au 2022, 2, 463–471. [2] M. Sotoudeh, M. Dillenz, A. Groß, Adv. Energy Sustainability Res. 2021, 2, 2100113. [3] M. Sotoudeh, M. Dillenz, J. Döhn, J. Hansen, S. Dsoke, A. Groß, Chem. Mater. 2023, 35, 4786–4797. Ion ; Physics ; Nanotechnology ; Materials science ; Quantum mechanics Machine Learning in Materials Science
Authors: Siwei Tang | Haonan Dong | Zhe Huang | Baishan Chen | Haiguo Tang
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic Properties of B20 Crystal Structured Dual-Phase High Entropy Helimagnet Dual (grammatical number) ; Entropy (arrow of time) ; Phase (matter) ; Materials science ; Crystallography ; Physics ; Chemistry ; Thermodynamics Heusler alloys: electronic and magnetic properties ; Magnetic and transport properties of perovskites and related materials ; Rare-earth and actinide compounds
Authors: Simon‐Johannes Burgdorf | Thomas Roddelkopf | A.I. Cooper | Kerstin Thurow
Source:  (article, 2023-08-21)
Field: Materials Science — Materials Chemistry

Computer vision based crystallization monitoring in automated laboratories Research into new functional materials has been ongoing on for many years. The successes are based on a classic trialand-error method. In the years that followed, various methods such as computer-aided calculations and high-throughput screening were added. Since the beginning of the 21st century, immense progress has been made in the field of artificial intelligence, which has since found its way into a wide variety of specialist disciplines and everyday life. With the advent of artificial intelligence in the research of new materials, there is hope for new results and savings in time and money. The approach presented here serves to monitor crystallization processes. Crystallization processes are used to evaporate new compositions of substances dissolved in a solvent. Evaporation produces crystals, which are then used for further investigations into the material properties. However, the crystallization process is very time-consuming and highly dependent on the solution and the environmental parameters. As a result, the timing of the process is difficult to predict and very lengthy. Therefore, this paper presents a method combines two areas, computer vision and artificial intelligence, and thus offers the possibility to monitor a crystallization process. The significant points, the start and end point, are detected, and the course of the crystallization process over time is also recorded. For this purpose, a pre-trained ResNet34 network is used, which has been trained on the characteristics of crystals through transfer learning, and a visual analyzer unit for in-situ sample acquisition. With this precise measurement setup, crystallization processes can be monitored and subsequently automated. This can save time and money and accelerate research into new materials. Crystallization ; Computer science ; Process (computing) ; Throughput ; Evaporation ; Process engineering ; Artificial intelligence ; Machine learning ; Engineering ; Chemical engineering ; Telecommunications Crystallization and Solubility Studies ; Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science
Authors: Takumi KUMAGAI | Yuya Nakajima | Junji Seino
Source: Journal of Computer Chemistry Japan (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Automatic Molecular Identification System Based on Spectral Information and Quantum Chemical Calculation Recent practical application of automated experiments using robotics, high-throughput experiments, and artificial intelligence technology has been progressing rapidly. In automated experiments, molecular identification is an important process for obtaining structural information on synthesized compounds and understanding their reactivity and chemical properties. In this study, we developed a system for automated molecular identification. The system uses spectral information and quantum chemical calculations, which provide no fluctuating data and have a potential to explore a wide range of chemical space. Numerical validation results suggested that the system is capable of efficient and accurate automated molecular identification in organic compounds with low molecular weight. Identification (biology) ; Computer science ; Artificial intelligence ; Process (computing) ; Quantum chemical ; Biological system ; Chemistry ; Molecule ; Organic chemistry Various Chemistry Research Topics ; Innovative Microfluidic and Catalytic Techniques Innovation ; Machine Learning in Materials Science
Authors: Yoshihiro Kangawa | Akira Kusaba
Source: Vacuum and Surface Science (article, 2023-04-09)
Field: Materials Science — Materials Chemistry

Progress in Surface Science in III-Nitride MOCVD Data-driven materials development using first-principles calculations (materials informatics, MI) is widely known. Process informatics (PI), which explores crystal growth processes, is expected to be pioneered as the next research target in materials development. However, simulation techniques for the chemical vapor deposition (CVD) process, which is the core of PI, have not been developed. This paper describes recent progress in simulation technology for CVD processes, with a particular focus on surface modeling. Chemical vapor deposition ; Informatics ; Process (computing) ; Computer science ; Process development ; Nanotechnology ; Materials science ; Engineering ; Process engineering ; Electrical engineering ; Programming language Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Manufacturing Process and Optimization
Authors: Guillaume Roland | A. Portavoce | M. Bertoglio | Marion Descoins | Jacopo Remondina | F. Lorut | Magali Putero
Source: Materialia (article, 2023-09-02)
Field: Materials Science — Materials Chemistry

Quantification of fcc-Ge2Sb2Te5 stoichiometry variations Nucleation ; Chalcogenide ; Materials science ; Amorphous solid ; Crystallization ; Stoichiometry ; Transmission electron microscopy ; Atom probe ; Microstructure ; Phase (matter) ; Crystallography ; Analytical Chemistry (journal) ; Chemical engineering ; Nanotechnology ; Metallurgy ; Thermodynamics ; Chemistry ; Physical chemistry Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Crystal Structures and Properties
Authors: Tae Hyun Kim | Myoungsub Kim | Seungwon Park | Seung‐min Chung | Hyungjun Kim
Source: ECS Meeting Abstracts (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

A Study on Film Properties Control of Atomic Layer Deposition Amorphous Carbon for PRAM Electrode Application As the amount of data usage increases with the development of technologies such as artificial intelligence, Internet of Things (IoT), and big data, the need for development of high-performance memory devices and improvement of density is increasing. Phase change memory is a next-generation memory device that stores and erases information by the resistance difference generated by reversibly and rapidly changing the crystal structure of the memory cell. Recently, research on the material engineering aspect to increase thermal efficiency by using various electrode materials and the structural aspect to increase the degree of integration by making a 3D structure is attracting attention. As an electrode material to increase the thermal efficiency of PRAM devices, carbon, which has various resistances (conductor (graphite, sp2 bond) – insulator (diamond, sp3 bond)) depending on the state of atomic bonding is an attractive material. Therefore, research using carbon as an electrode by using PVD and CVD processes has been conducted, but there is a problem that it is difficult to use it for devices with a complex 3D structure with the existing process. Therefore, in this study, an a-C ALD process that can be uniformly deposited in a 3D structure was developed using the CBr4 precursor. And by analyzing the bonding state and physical properties of the deposited thin film, the mechanism of how the a-C thin film grows according to the thin film deposition conditions is studied. Atomic layer deposition ; Electrode ; Layer (electronics) ; Materials science ; Deposition (geology) ; Amorphous solid ; Chemical engineering ; Carbon fibers ; Amorphous carbon ; Nanotechnology ; Optoelectronics ; Chemistry ; Composite material ; Crystallography ; Composite number ; Engineering Diamond and Carbon-based Materials Research ; Analytical Chemistry and Sensors ; Semiconductor materials and devices
Authors: Arnab Maity | Yael Hershkovitz‐Pollak | Ritu Gupta | Weiwei Wu | Hossam Haick
Source: Advanced Materials (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Spin‐Controlled Helical Quantum Sieve Chiral Spectrometer (Adv. Mater. 24/2023) Identification of Chiral Enantiomers In article number 2209125, Hossam Haick and co-workers report a molecular-spin-sensitive-antenna (MSSA) made up of organically functionalized graphene layers on a fibrous helical cellulose network. The MSSA structure combines relativistic spin–orbit interaction coupled with organic spin-coherence and offers spatiotemporal chiral molecule separation, recognition, and selective identification of enantiomers. Coupled with neuromorphic artificial intelligence, it enables rapid classification of chiral molecules, resulting in portable spin-controlled spectrometry. Materials science ; Enantiomer ; Spin (aerodynamics) ; Molecule ; Spectrometer ; Coherence (philosophical gambling strategy) ; Chemical physics ; Nanotechnology ; Organic chemistry ; Optics ; Physics ; Quantum mechanics ; Chemistry Covalent Organic Framework Applications ; Mesoporous Materials and Catalysis
Authors: Jianwei Wang | Yanling Zhang | Taiang Liu | Lu Tian | Baojian Wu | Weidong Duan | Hanping Zhou | Guangshan Wang | Xinzhong Wang
Source:  (article, 2023-12-08)
Field: Chemistry — Analytical Chemistry

Prediction of Flue-Cured Tobacco Leaves Aroma Quality and Volume Based on genetic algorithm and support vector machine To reveal the relationship between the aroma quality of roasted tobacco and other quality indicators, the conventional chemical composition and physical characteristics of tobacco samples (central leaves) from 22 tobacco producing provinces (cities and autonomous regions) in China were used to integrate the data. The genetic algorithm and support vector machine regression in artificial intelligence algorithms were used for screening variables and establishing prediction models, and the model was evaluated by using the modeling, leave-one-out and prediction. The results showed that the chemical composition indexes that have a greater influence on tobacco aroma quality are nicotine, total nitrogen and reducing sugar, and the physical properties are thickness and equilibrium water content. The chemical composition indexes that have a greater influence on tobacco aroma volume are nicotine and total nitrogen, and the physical property indexes are tensile strength, stem rate and equilibrium water content. The prediction results of the support vector machine regression model for tobacco flavor are less than 0.5% of the measured values. The proportion of samples with absolute errors within 0.5 ranged from 89.32% to 98.24%, and the absolute errors ranged from 0.17 to 0.26; the proportion of samples with absolute errors within 0.5 ranged from 84.94<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> to 97.61%, and the absolute errors ranged from 0.16 to 0.27. In summary, the fusion data of standard chemical composition and physical characteristic indexes indicate that a tobacco flavor quality forecasting model with high accuracy can be established through the joint application of artificial intelligence algorithms. Aroma ; Mathematics ; Support vector machine ; Algorithm ; Volume (thermodynamics) ; Flavor ; Linear regression ; Nicotine ; Regression analysis ; Statistics ; Food science ; Machine learning ; Chemistry ; Computer science ; Biology ; Thermodynamics ; Physics Spectroscopy and Chemometric Analyses
Authors: Matt Blois
Source: C&EN Global Enterprise (article, 2023-02-13)
Field: Materials Science — Materials Chemistry

Mattiq raises funds to discover new materials Inspired by high-throughput techniques that led to breakthroughs in DNA sequencing and drug discovery, the Chicago start-up Mattiq has raised $15 million in seed funding to speed the discovery of materials needed to wean the world off fossil fuels. Mattiq, previously known as Stoicheia , prints millions of tiny particles onto chips and tests some to determine if they have the properties the company is seeking. Characterizing all the materials would be slow and costly, so an artificial intelligence tool uses data from those initial experiments to suggest which candidates should be tested next. Because the recommended materials are already on the chip, the experiment can proceed immediately. “This gives us the ability to go through the materials space ... much faster,” says Andrey Ivankin, Mattiq’s chief technology officer. The firm, which is based on technology developed by Northwestern University chemist Chad Mirkin, searches for materials used in electrochemical reactions, Officer ; Electronic materials ; Nanotechnology ; Engineering ; Data science ; Computer science ; History ; Materials science ; Archaeology Machine Learning in Materials Science
Authors: Meishuai Zou | Zichen Bai | Xiaodong Li | Tianhao Wu | Hao Jiang | Xudong Zhang | Lichen Zhang | Yi Yang | Shuang Liu | Lisha Lei | Ningning Song | Zhengdi Wang | Xing Su
Source: Research Square (preprint, 2023-07-31)
Field: Materials Science — Polymers and Plastics

A facile and on-demand optimizing strategy for polyurethane elastomers via programmable hydrogen bonding Abstract Polyurethane elastomers have advantages in terms of elasticity, toughness and oil-resistance because of its unique hydrogen bonding structure. It has been widely used in industry. However, the poor designability of hydrogen bonding structure in existing polyurethane elastomers severely restricts the on-demand regulation of their properties. Moreover, like most polymer materials, polyurethane elastomers are susceptible to heat or radiation induced aging which significantly limit their practical effectiveness. Herein, a facile, universal, efficient and programmable strategy based on physical modification of polyphenol aggregates was proposed, which could systematically improve the overall performance of engineering polyurethane elastomers. Through precisely manipulated heat-induced phase transition and/or photo-induced interfacial hydrogen bonding upgrading due to polyphenol aggregates, programmable strengthening and toughening effect could be achieved with high precision. Typically, the tensile strength and toughness of our proposed polyurethane elastomers could be enhanced by 3.23 and 2.22 times comparing with neat polyurethane samples, respectively. The relevant results were supported by various characterization and mathematical modeling. In addition, the polyurethane elastomers exhibited unique selective biocompatibility, rapid self-healing capability under mild conditions and nice recyclability, which could fulfill a variety of functions. Our proposed modifying strategy by using polyphenol aggregates can not only programmably optimize the comprehensive properties of polyurethane elastomers, but also inspire programmable regulation of polyurethane performance through programmable design of its certain microstructure in the future. Our proposed polyurethane materials hold big promise in the application of structure materials, programmable devices, artificial intelligence, biomedicine, environmental protection and etc. Polyurethane ; Elastomer ; Materials science ; Hydrogen bond ; On demand ; Polymer science ; Nanotechnology ; Polymer chemistry ; Composite material ; Business ; Chemistry ; Organic chemistry ; Molecule ; Commerce Polymer composites and self-healing ; Polymer Nanocomposites and Properties ; Advanced Polymer Synthesis and Characterization
Authors: Laura Howes﻿
Source: C&EN Global Enterprise (article, 2023-09-18)
Field: Chemistry — Physical and Theoretical Chemistry

Who will win the #ChemNobel? Nobel season approaches, and that means so too does C&EN’s annual Nobel Prize prediction webinar, which will be held Sept. 28, 2–3 p.m. (EDT). Coproduced with ACS Webinars, the annual event is free to attend and always provokes a lively discussion . This year, regular host Laura Howes will be joined by Robert Root-Bernstein of Michigan State University, Joaquín Barroso-Flores of the National Autonomous University of Mexico, Michael Booth of University College London, and Michelle Muzzio, deputy editor of the journal iScience . Root-Bernstein will present his work on the importance of creativity to science and why prize winners are often polymaths. The panelists will also debate whether hot topics in chemistry, like artificial intelligence, will impact this year’s decision-making process and which fields of science should be considered for future awards. This edition of the annual webinar will reveal the scientists that the panelists believe will win this year, Creativity ; Library science ; Event (particle physics) ; State (computer science) ; Art history ; History ; Management ; Sociology ; Operations research ; Political science ; Engineering ; Law ; Computer science ; Economics ; Physics History and advancements in chemistry ; Biotechnology and Related Fields
Authors: Hyoung Seop Kim | Pil-Ryoung Cha | Hyunjoo Choi
Source: MATERIALS TRANSACTIONS (article, 2023-08-24)
Field: Materials Science — Materials Chemistry

PREFACE The most time and cost-consuming stage from materials development to commercialization involves transitioning a process from the lab-scale to the pilot plant or commercial scale.This special issue focuses on the latest advancements in understanding, describing, and predicting material behavior during manufacturing processes to facilitate the seamless scaling of newly developed materials from laboratory research to industrial-level production.Generally, during manufacturing, multiple physical phenomena occur simultaneously, including heat transfer, fluid flow, phase transformation, and elastic/plastic deformations.Therefore, it is crucial to integrate material simulation models spanning multiple length scales to effectively capture the intricate process-structure-properties relationships of a material during its manufacturing.To bring together detailed analysis, characterization as well as computational modeling of materials from atomistic to macroscopic length scales, the 17th International Symposium on Novel and Nano Materials (ISNNM 2022) has been held on 14-18, November 2022 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 Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Microstructure and mechanical properties