jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Malthe Kjær Bisbo | Bjørk Hammer
Source: Physical review. B./Physical review. B (article, 2022-06-09)
Field: Materials Science — Materials Chemistry

Global optimization of atomic structure enhanced by machine learning Global optimization with first-principles energy expressions (GOFEE) is an efficient method for identifying low-energy structures in computationally expensive energy landscapes such as the ones described by density functional theory (DFT), van der Waals enabled DFT, or even methods beyond DFT. GOFEE is an evolutionary algorithm, that in order to explore configuration space creates several candidates in parallel. These are treated approximately using a machine learned surrogate model of energies and forces, trained on the fly, eliminating the need for expensive relaxations using first-principles methods. Eventually, using Bayesian statistics, GOFEE chooses one candidate and treats that at the full first-principles level. In this paper we elaborate on the importance of the use of a Gaussian kernel with two length scales in the Gaussian process regression surrogate model. We further explore the role of the use in GOFEE of the lower confidence bound for relaxation and selection of candidate structures. In addition, we present details of a sampling scheme for obtaining parent structures in the evolution. Using machine learning clustering of the entire pool of low-energy structures ever calculated, and choosing the most stable member from each cluster, the scheme ensures a highly diverse sample of structures that plays the role of a population. The versatility of the GOFEE method is demonstrated by applying it to identify the low-energy structures of gas-phase fullerene-type 24-atom carbon clusters and of dome-shaped 18-atom carbon clusters supported on Ir(111). Computer science ; Kernel (algebra) ; Cluster analysis ; Atom (system on chip) ; Kriging ; Gaussian process ; Population ; van der Waals force ; Cluster (spacecraft) ; Artificial intelligence ; Density functional theory ; Gaussian ; Algorithm ; Statistical physics ; Machine learning ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Camilo Augusto Fernandes Salvador | Eloá Lopes Maia | Fernando Henrique da Costa | Julián Escobar | J.P. Oliveira
Source: Scientific Data (article, 2022-04-26)
Field: Materials Science — Materials Chemistry

A compilation of experimental data on the mechanical properties and microstructural features of Ti-alloys The present work depicts a compilation of mechanical properties of 282 distinct multicomponent Ti-based alloys and their respective microstructural features. The dataset includes the chemical composition (in at.%), phase constituents, Young modulus, hardness, yield strength, ultimate strength, and elongation. Each entry is associated with a high-quality experimental work containing a complete description of the processing route and testing setup. Furthermore, we incorporated flags to the dataset indicating (a) the use of high-resolution techniques for microstructural analysis and (b) the observation of non-linear elastic responses during mechanical testing. Oxygen content and average grain size are presented whenever available. The selected features can help material scientists to adjust the data to their needs concerning materials selection and discovery. Most alloys in the dataset were produced via an ingot metallurgy route, followed by solubilization and water quench (≈58%), which is considered a standard condition for β-Ti alloys. The database is hosted and maintained up to date in an open platform. For completeness, a few graphical representations of the dataset are included. Ingot ; Materials science ; Elongation ; Microstructure ; Metallurgy ; Ultimate tensile strength Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties ; Advanced materials and composites
Authors: Dong Wang | Anette Brocks Hagen | P.U. Fathi | Meichao Lin | Roy Johnsen | Xu Lu
Source: Materials Science and Engineering A (article, 2022-11-02)
Field: Materials Science — Metals and Alloys

Investigation of hydrogen embrittlement behavior in X65 pipeline steel under different hydrogen charging conditions The hydrogen effect on a X65 carbon steel was investigated by tensile tests under both ex-situ and in-situ hydrogen charging conditions. The fractured samples were characterized and compared using a combination of scanning electron microscopy, electron backscattering diffraction, and energy-dispersive spectroscopy. The work highlights that the in-situ hydrogen charging is a necessity for investigation of hydrogen detrimental effects on the studied material, where a pronounced reduction in fracture elongation, the evolution of secondary cracks on gauge surface, and the corresponding brittle fractography were thoroughly characterized after in-situ testing. The reason resides in the rapid hydrogen outgassing effect, which was proved by Fick's law-based diffusion models. Then the interrupted tensile tests were performed to track the crack initiation and propagation behavior. The results show that the majority of cracks initiated at the interfaces of MnS and Al2O3 inclusions or between inclusions and matrix, which attributes to the elevated stress concentration around the inclusions. Moreover, the cracks were found to propagate along the {110} slip planes. Materials science ; Hydrogen embrittlement ; Hydrogen ; Ultimate tensile strength ; Fractography ; Scanning electron microscope ; Brittleness ; Tensile testing ; Composite material ; Fracture mechanics ; Charpy impact test ; Metallurgy ; Corrosion ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Material Properties and Failure Mechanisms
Authors: Bratati Das | Seiko Jo | Jianlu Zheng | Jiali Chen | Kaori Sugihara
Source: Nanoscale (review, 2022-01-01)
Field: Chemistry — Organic Chemistry

Recent progress in polydiacetylene mechanochromism Polydiacetylenes (PDAs) are a family of mechanochromic polymers that change color from blue to red and emit fluorescence when exposed to external stimuli, making them extremely popular materials in biosensing. Although several informative reviews on PDA biosensing have been reported in the last few years, their mechanochromism, where external forces induce the color transition, has not been reviewed for a long time. This mini review summarizes recent progress in PDA mechanochromism, with a special focus on the quantitative and nanoscopic data that have emerged in recent years. Polydiacetylenes ; Nanotechnology ; Nanoscopic scale ; Biosensor ; Materials science ; Fluorescence ; Polymer ; Optics ; Physics ; Composite material Polydiacetylene-based materials and applications ; Ocular Surface and Contact Lens ; Antimicrobial Peptides and Activities
Authors: Kai Bian | Guanghui Zhang | Jie Zhu | Xiang Wang | Mingrui Wang | Feijian Lou | Yi Liu | Chunshan Song | Xinwen Guo
Source: ACS Catalysis (article, 2022-05-18)
Field: Materials Science — Materials Chemistry

Promoting Propane Dehydrogenation with CO<sub>2</sub> over the PtFe Bimetallic Catalyst by Eliminating the Non-selective Fe(0) Phase Fine tuning the structure of bimetallic nanoparticles is critical toward understanding structure–activity relationships and further improving the catalytic performance in propane dehydrogenation (PDH). Excessive Fe species in the PtFe bimetallic catalysts promote carbon deposition leading to low propylene selectivity, and it remains challenging to synthesize well-defined PtFe catalysts while selectively eliminating the excessive Fe. Herein, we show that the formation of coke can be significantly inhibited by introducing CO2 into the PDH over PtFe catalysts, where CO2 effectively eliminates the active Fe(0) coking sites without changing the catalytic surface structure of the PtFe alloy. With a CO2/C3H8 feeding ratio of 0.20, the Pt1Fe7/S-1 catalyst shows the highest propylene production rate and decreased amount of coke from 18.8 to 1.0 wt % compared with dehydrogenation without CO2. X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and 57Fe Mössbauer results indicate that it is the oxidation of excessive unalloyed Fe species during the CO2-PDH reaction, instead of the reverse Boudouard reaction (CO2 + C = 2CO), that significantly inhibits the carbon deposition. This work provides a promising strategy for tuning the structure of PtFe bimetallic catalysts under reaction conditions and improving the performance of the PDH reaction. Bimetallic strip ; Dehydrogenation ; Catalysis ; Materials science ; Propane ; Coke ; Selectivity ; X-ray photoelectron spectroscopy ; Chemical engineering ; Carbon fibers ; Inorganic chemistry ; Chemistry ; Organic chemistry ; Metallurgy ; Composite material Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Zeolite Catalysis and Synthesis
Authors: Yang Cao | Zhiyuan Xiong | Fang Xia | George V. Franks | Lianhai Zu | Xiao Wang | Yvonne Hora | Stephen Mudie | Zijun He | Longbing Qu | Yanlu Xing | Dan Li
Source: Advanced Functional Materials (article, 2022-04-08)
Field: Materials Science — Materials Chemistry

New Structural Insights into Densely Assembled Reduced Graphene Oxide Membranes Abstract Densely assembled graphene‐based membranes have attracted substantial interest for their widespread applications, such as compact capacitive energy storage, ion/molecular separation, gas barrier films, and flexible electronics. However, the multiscale structure of densely packed graphene membranes remains ambiguously understood. This article combines X‐ray and light scattering techniques as well as dynamic electrosorption analysis to uncover the stacking structure of the densely stacked reduced graphene oxide (rGO) membranes. The membranes are produced by reducing graphene oxide (GO) membranes with hydrazine, during which the colloidal interactions between GO sheets are modulated by the electrolyte solution. In contrast to the common notion that direct reduction of densely assembled GO sheets in parallel tends to result in significant “graphitization”, this article unexpectedly discovers that the resultant densely packed rGO membrane can still retain the interconnected network nanochannels and show good capacitive performances. This inspires the development of a hierarchical structural model to describe the densely packed rGO membranes. This article further shows that the nanochannel network can be fine‐tuned at the sub‐nanometer level by tailoring the salt concentration and the reduction temperature to render exceptional volumetric capacitance and good rate performance for rGO membranes even with increased packing density. Graphene ; Membrane ; Materials science ; Oxide ; Nanotechnology ; Supercapacitor ; Electrolyte ; Stacking ; Chemical engineering ; Capacitance ; Electrode ; Chemistry Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials ; Nanopore and Nanochannel Transport Studies
Authors: Yuqin Feng | Mei-Ling Lv | Ming Yang | Wenxia Ma | Gang Zhang | Yunzi Yu | Yaqi Wu | Haibo Li | Dezheng Liu | Yongsheng Yang
Source: Molecules (review, 2022-03-01)
Field: Materials Science — Polymers and Plastics

Application of New Energy Thermochromic Composite Thermosensitive Materials of Smart Windows in Recent Years Thermochromic smart windows technology can intelligently regulate indoor solar radiation by changing indoor light transmittance in response to thermal stimulation, thus reducing energy consumption of the building. In recent years, with the development of new energy-saving materials and the combination with practical technology, energy-saving smart windows technology has received more and more attention from scientific research. Based on the summary of thermochromic smart windows by Yi Long research groups, this review described the applications of thermal responsive organic materials in smart windows, including poly(N-isopropylacrylamide) (PNIPAm) hydrogels, hydroxypropyl cellulose (HPC) hydrogels, ionic liquids and liquid crystals. Besides, the mechanism of various organic materials and the properties of functional materials were also introduced. Finally, opportunities and challenges relating to thermochromic smart windows and prospects for future development are discussed. Thermochromism ; Materials science ; Ionic liquid ; Smart material ; Self-healing hydrogels ; Hydroxypropyl cellulose ; Transmittance ; Nanotechnology ; Computer science ; Composite material ; Chemistry ; Optoelectronics ; Polymer ; Polymer chemistry ; Organic chemistry Transition Metal Oxide Nanomaterials ; Urban Heat Island Mitigation ; Building Energy and Comfort Optimization
Authors: Han Young Jung | Jeong Seok Lee | Hyun Taek Han | Jaehan Jung | KwangSup Eom | Jung Tae Lee
Source: Polymers (review, 2022-02-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Lignin-Based Materials for Sustainable Rechargeable Batteries This review discusses important scientific progress, problems, and prospects of lignin-based materials in the field of rechargeable batteries. Lignin, a component of the secondary cell wall, is considered a promising source of biomass. Compared to cellulose, which is the most extensively studied biomass material, lignin has a competitive price and a variety of functional groups leading to broad utilization such as adhesive, emulsifier, pesticides, polymer composite, carbon precursor, etc. The lignin-based materials can also be applied to various components in rechargeable batteries such as the binder, separator, electrolyte, anode, and cathode. This review describes how lignin-based materials are adopted in these five components with specific examples and explains why lignin is attractive in each case. The electrochemical behaviors including charge-discharge profiles, cyclability, and rate performance are discussed between lignin-based materials and materials without lignin. Finally, current limitations and future prospects are categorized to provide design guidelines for advanced lignin-based materials. Lignin ; Materials science ; Nanotechnology ; Chemistry ; Organic chemistry Supercapacitor Materials and Fabrication ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Huadong Fu | Hong‐Tao Zhang | Changsheng Wang | Wei Yong | Jianxin Xie
Source: International Journal of Minerals Metallurgy and Materials (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Recent progress in the machine learning-assisted rational design of alloys Computer science ; Process (computing) ; Artificial intelligence ; Machine learning ; Iterative and incremental development ; Manufacturing engineering ; Industrial engineering ; Engineering ; Software engineering Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Victor Fung | Panchapakesan Ganesh | Bobby G. Sumpter
Source: Chemistry of Materials (article, 2022-05-24)
Field: Materials Science — Materials Chemistry

Physically Informed Machine Learning Prediction of Electronic Density of States The electronic structure of a material, such as its density of states (DOS), provides key insights into its physical and functional properties and serves as a valuable source of high-quality features for many materials screening and discovery workflows. However, the computational cost of calculating the DOS, most commonly with density functional theory (DFT), becomes prohibitive for meeting high-fidelity or high-throughput requirements, necessitating a cheaper but sufficiently accurate surrogate. To fulfill this demand, we develop a general machine learning method based on graph neural networks for predicting the DOS purely from atomic positions, six orders of magnitude faster than DFT. This approach can effectively use large materials databases and be applied generally across the entire periodic table to materials classes of arbitrary compositional and structural diversity. We furthermore devise a highly adaptable scheme for physically informed learning which encourages the DOS prediction to favor physically reasonable solutions defined by any set of desired constraints. This functionality provides a means for ensuring that the predicted DOS is reliable enough to be used as an input to downstream materials screening workflows to predict more complex functional properties, which rely on accurate physical features. Workflow ; Computer science ; Fidelity ; Key (lock) ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: An‐Qi Chen | Hai‐Long Wu | Tong Wang | Xiaozhi Wang | Hai-Bo Sun | Ru-Qin Yu
Source: Talanta (article, 2022-07-09)
Field: Chemistry — Analytical Chemistry

Intelligent analysis of excitation-emission matrix fluorescence fingerprint to identify and quantify adulteration in camellia oil based on machine learning Chemistry ; Fingerprint (computing) ; Matrix (chemical analysis) ; Artificial intelligence ; Fluorescence ; Vegetable oil ; Biological system ; Edible oil ; Pattern recognition (psychology) ; Complex matrix ; Analytical Chemistry (journal) ; Machine learning ; Chromatography ; Food science ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Joshua D. Elliott | Athanasios A. Papaderakis | Robert A. W. Dryfe | Paola Carbone
Source: Journal of Materials Chemistry C (article, 2022-01-01)
Field: Chemistry — Electrochemistry

The electrochemical double layer at the graphene/aqueous electrolyte interface: what we can learn from simulations, experiments, and theory The structure of the electrical-double layer of the graphene/aqueous–electrolyte interface is challenging to model and characterize experimentally. Here we review the published data and discuss their interpretation in the context of the EDL theory. Graphene ; Electrolyte ; Materials science ; Context (archaeology) ; Electrochemistry ; Double layer (biology) ; Interface (matter) ; Layer (electronics) ; Interpretation (philosophy) ; Aqueous solution ; Nanotechnology ; Chemical physics ; Electrode ; Computer science ; Physical chemistry ; Composite material ; Physics ; Chemistry Electrochemical Analysis and Applications ; Electrostatics and Colloid Interactions ; Membrane-based Ion Separation Techniques
Authors: Weiqi Wang | Yuting Yu | Yilan Jin | Xiao Liu | Min Shang | Xiaohua Zheng | Tingting Liu | Zhigang Xie
Source: Journal of Nanobiotechnology (review, 2022-05-02)
Field: Chemistry — Inorganic Chemistry

Two-dimensional metal-organic frameworks: from synthesis to bioapplications As a typical class of crystalline porous materials, metal-organic framework possesses unique features including versatile functionality, structural and compositional tunability. After being reduced to two-dimension, ultrathin metal-organic framework layers possess more external excellent properties favoring various technological applications. In this review article, the unique structural properties of the ultrathin metal-organic framework nanosheets benefiting from the planar topography were highlighted, involving light transmittance, and electrical conductivity. Moreover, the design strategy and versatile fabrication methodology were summarized covering discussions on their applicability and accessibility, especially for porphyritic metal-organic framework nanosheet. The current achievements in the bioapplications of two-dimensional metal-organic frameworks were presented comprising biocatalysis, biosensor, and theranostic, with an emphasis on reactive oxygen species-based nanomedicine for oncology treatment. Furthermore, current challenges confronting the utilization of two-dimensional metal-organic frameworks and future opportunities in emerging research frontiers were presented. Nanotechnology ; Metal-organic framework ; Materials science ; Nanomedicine ; Nanosheet ; Nanoparticle ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Edward J. Beard | Jacqueline M. Cole
Source: Scientific Data (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

Perovskite- and Dye-Sensitized Solar-Cell Device Databases Auto-generated Using ChemDataExtractor The number of scientific publications reporting cutting-edge third-generation photovoltaic devices is increasing rapidly, owing to the pressing need to develop renewable-energy technologies that address the climate-change crisis. Consequently, the field could benefit from a central repository where photovoltaic-performance metrics, such as the power-conversion efficiency (η) are recorded. We present two automatically generated databases that contain photovoltaic properties and device material data for dye-sensitized solar cells (DSCs) and perovskite solar cells (PSCs), totalling 660,881 data entries representing 57,678 photovoltaic devices. The databases were generated by applying the text-mining toolkit ChemDataExtractor on a corpus of 25,720 articles. A multi-faceted evaluation, incorporating manual and automatic methods, was applied to ensure that the data contained therein were of the highest quality, with precision metrics ranging from 73.1% to 95.8%. The DSC database contains 475,045 entries representing 41,680 devices, and the PSC database contains 185,836 entries representing 15,818 devices. The databases are available in MongoDB and JSON formats, which can be queried in Python, R, Java and MATLAB for data-driven photovoltaic materials discovery. Photovoltaic system ; Database ; Computer science ; Python (programming language) ; Data conversion ; Java ; JSON ; Data mining ; Operating system ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Chalcogenide Semiconductor Thin Films
Authors: Glen M. Hocky | Andrew Dickson White
Source: Digital Discovery (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Natural language processing models that automate programming will transform chemistry research and teaching Natural language processing models have emerged that can generate useable software and automate a number of programming tasks with high fidelity. These tools have yet to have an impact on the chemistry community. Yet, our initial testing demonstrates that this form of artificial intelligence is poised to transform chemistry and chemical engineering research. Here, we review developments that brought us to this point, examine applications in chemistry, and give our perspective on how this may fundamentally alter research and teaching. USable ; Computer science ; Perspective (graphical) ; Natural language ; Software engineering ; Programming language ; Artificial intelligence ; Multimedia Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research