jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Samrat Ghosh | Akinobu Nakada | Maximilian A. Springer | Takahiro Kawaguchi | Katsuaki Suzuki | Hironori Kaji | Igor A. Baburin | Agnieszka Kuc | Thomas Heine | Hajime Suzuki | Ryu Abe | Shu Seki
Source: Journal of the American Chemical Society (article, 2020-04-30)
Field: Materials Science — Materials Chemistry

Identification of Prime Factors to Maximize the Photocatalytic Hydrogen Evolution of Covalent Organic Frameworks Visible-light-driven hydrogen (H<sub>2</sub>) production from water is a promising strategy to convert and store solar energy as chemical energy. Covalent organic frameworks (COFs) are front runners among different classes of organic photocatalysts. The photocatalytic activity of COFs depends on numerous factors such as the electronic band gap, crystallinity, surface area, exciton migration, stability of transient species, charge separation and transport, etc. However, it is challenging to fine tune all of these factors simultaneously to enhance the photocatalytic activity. Hence, in this report, an effort has been made to understand the interplay of these factors and identify the key factors for efficient photocatalytic H<sub>2</sub> production through a structure-property-activity relationship. Careful molecular engineering allowed us to optimize all of the above plausible factors impacting the overall catalytic activities of a series of isoreticular COFs. The present study determines three prime factors: light absorption, charge carrier generation, and its transport, which influence the photocatalytic H<sub>2</sub> production of COFs to a much greater extent than the other factors. Chemistry ; Covalent bond ; Identification (biology) ; Photocatalysis ; Prime (order theory) ; Hydrogen bond ; Metal-organic framework ; Nanotechnology ; Photochemistry ; Organic chemistry ; Molecule ; Catalysis ; Adsorption Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Vladislav Gladkikh | Dong Yeon Kim | Amir Hajibabaei | Atanu Jana | Chang Woo Myung | Kwang S. Kim
Source: The Journal of Physical Chemistry C (article, 2020-04-02)
Field: Materials Science — Materials Chemistry

Machine Learning for Predicting the Band Gaps of ABX<sub>3</sub> Perovskites from Elemental Properties The band gap is an important parameter that determines light-harvesting capability of perovskite materials. It governs the performance of various optoelectronic devices such as solar cells, light-emitting diodes, and photodetectors. For perovskites of a formula ABX3 having a non-zero band gap, we study nonlinear mappings between the band gap and properties of constituent elements (e.g., electronegativities, electron affinities, etc) using alternating conditional expectations (ACE)—a machine learning technique suitable for small data sets. We also compare ACE with other machine learning methods: decision trees, kernel ridge regression, extremely randomized trees, AdaBoost, and gradient boosting. The best performance is achieved by kernel ridge regression and extremely randomized trees. However, ACE has an advantage that it presents its results in a graphic form, helping in interpretation. The models are trained with the data obtained from density functional theory calculations. Different statistical approaches for feature selection are applied and compared: Pearson correlation, Spearman’s rank correlation, maximal information coefficient, distance correlation, and ACE. A classification task of separating metallic perovskites from nonmetallic ones is solved using support-vector machines with the radial basis function kernel. Artificial intelligence ; Machine learning ; Support vector machine ; Electronegativity ; Band gap ; Pearson product-moment correlation coefficient ; Computer science ; Materials science ; Decision tree ; Mathematics ; Physics ; Optoelectronics ; Statistics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Conducting polymers and applications
Authors: Issam Barra | Stephan M. Haefele | Ruben Sakrabani | Fassil Kebede
Source: TrAC Trends in Analytical Chemistry (article, 2020-12-29)
Field: Chemistry — Analytical Chemistry

Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review Chemometrics ; Hyperspectral imaging ; Context (archaeology) ; Computer science ; Preprocessor ; Biochemical engineering ; Machine learning ; Remote sensing ; Spectroscopy ; Environmental science ; Process engineering ; Artificial intelligence ; Engineering ; Geology ; Physics Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Mineral Processing and Grinding
Authors: Anton Kokalj | Matic Lozinšek | Barbara Kapun | Peyman Taheri | Shova Neupane | Patricia Losada‐Pérez | Chenyang Xie | Stojan Stavber | Daniel Crespo | Frank Uwe Renner | J.M.C. Mol | Ingrid Milošev
Source: Corrosion Science (article, 2020-07-22)
Field: Materials Science — Materials Chemistry

Simplistic correlations between molecular electronic properties and inhibition efficiencies: Do they really exist? The often used simplistic correlations between molecular electronic parameters and experimentally determined corrosion inhibition efficiencies are critically evaluated for a set of 24 heterocyclic organic compounds, tested as corrosion inhibitors for copper in 3 wt.% NaCl aqueous solution. Twelve different molecular electronic descriptors—such as ionization potential, electron affinity, HOMO–LUMO gap, dipole moment—are tested and it is shown that none of them displays any noticeable correlation with the inhibition efficiency. Our results, therefore, cast serious doubt on reported correlations between such parameters and inhibition efficiency, obtained for only a few inhibitors, which are abundant in the literature. We also discuss some pros and cons of inhibition efficiency as a metric for evaluating the performance of corrosion inhibitors, and introduce a new metric termed inhibition power that uses the universal logarithmic scale and dimensionless decibel (dB) units. Corrosion ; Dimensionless quantity ; Dipole ; Metric (unit) ; Ionization energy ; HOMO/LUMO ; Aqueous solution ; Electron affinity (data page) ; Ionization ; Quantitative structure–activity relationship ; Materials science ; Molecular descriptor ; Chemistry ; Computational chemistry ; Chemical physics ; Molecule ; Physics ; Stereochemistry ; Thermodynamics ; Physical chemistry ; Metallurgy ; Organic chemistry ; Ion Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Crystallography and molecular interactions
Authors: Paul Hodgkinson
Source: Progress in Nuclear Magnetic Resonance Spectroscopy (review, 2020-03-18)
Field: Chemistry — Spectroscopy

NMR crystallography of molecular organics Developments of NMR methodology to characterise the structures of molecular organic structures are reviewed, concentrating on the previous decade of research in which density functional theory-based calculations of NMR parameters in periodic solids have become widespread. With a focus on demonstrating the new structural insights provided, it is shown how "NMR crystallography" has been used in a spectrum of applications from resolving ambiguities in diffraction-derived structures (such as hydrogen atom positioning) to deriving complete structures in the absence of diffraction data. As well as comprehensively reviewing applications, the different aspects of the experimental and computational techniques used in NMR crystallography are surveyed. NMR crystallography is seen to be a rapidly maturing subject area that is increasingly appreciated by the wider crystallographic community. Chemistry ; Crystallography ; Materials science Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; Crystallography and molecular interactions
Authors: Ganesh Sivaraman | Anand Narayanan Krishnamoorthy | Matthias Baur | Christian Holm | Marius Stan | Gábor Csányi | Chris Benmore | Álvaro Vázquez-Mayagoitia
Source: npj Computational Materials (article, 2020-07-23)
Field: Materials Science — Materials Chemistry

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide Abstract We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO 2 ) dataset generated from a “melt-quench” ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO 2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies. Molecular dynamics ; Ab initio ; Amorphous solid ; Gaussian ; Ab initio quantum chemistry methods ; Bayesian optimization ; Atom (system on chip) ; Diffusion Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Chemical Physics Studies
Authors: Jun Hyuk Kim | Jun Kyu Kim | Jiapeng Liu | Antonino Curcio | Ji‐Soo Jang | Il‐Doo Kim | Francesco Ciucci | WooChul Jung
Source: ACS Nano (article, 2020-12-28)
Field: Materials Science — Materials Chemistry

Nanoparticle Ex-solution for Supported Catalysts: Materials Design, Mechanism and Future Perspectives Supported metal catalysts represent one of the major milestones in heterogeneous catalysis. Such catalytic systems are feasible for use in a broad range of applications, including renewable energy devices, sensors, automotive emission control systems, and chemical reformers. The lifetimes of these catalytic platforms depend strongly on the stability of the supported nanoparticles. With this regard, nanoparticles synthesized <i>via</i> ex-solution process emphasize exceptional robustness as they are socketed in the host oxide. Ex-solution refers to a phenomenon which yields selective growth of fine and uniformly distributed metal nanocatalysts on oxide supports upon partial reduction. This type of advanced structural engineering is a game-changer in the field of heterogeneous catalysis with numerous studies showing the benefits of ex-solution process. In this review, we highlight the latest research efforts regarding the origin of the ex-solution phenomenon and the mechanism underpinning particle formation. We also propose research directions to expand the utility and functionality of the current ex-solution techniques. Nanomaterial-based catalyst ; Catalysis ; Nanotechnology ; Nanoparticle ; Oxide ; Robustness (evolution) ; Materials science ; Underpinning ; Chemistry ; Engineering Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Andreas Mautner
Source: Polymer International (review, 2020-02-14)
Field: Materials Science — Biomaterials

Nanocellulose water treatment membranes and filters: a review Abstract This review covers the use of nanocelluloses in water treatment applications with particular focus on membranes and filters made either entirely from (nano)cellulose or in composite approaches. Nanocelluloses are among the emerging materials of this century, having found an abundance of potential applications in the fields of composites, medicine, functional additives or water treatment. Water treatment applications in particular have received significant academic and commercial attention, with a large variety of approaches developed in order to address arguably one of the largest problems that humanity is confronted with in the 21st century: clean water. In this regard, treatment of both potable water and wastewater is of high importance. The reason for the viability of nanocelluloses as base material relies upon their high specific surface area and abundance of OH groups that already exhibit certain attraction toward pollutants carrying ionic structures or dyes and also can be easily modified to significantly increase the affinity of nanocelluloses toward these pollutants. Nanocelluloses in their various forms (cellulose nanocrystals, cellulose nanofibrils, bacterial cellulose) have been applied in water treatment, with membranes and filters (size exclusion, e.g. for nanoparticle filtration, or affinity membranes) as well as adsorbents (e.g. heavy metal ions, dyes, nitrates) being the most studied. © 2020 The Author. Polymer International published by John Wiley &amp; Sons Ltd on behalf of Society of Chemical Industry. Nanocellulose ; Cellulose ; Water treatment ; Membrane ; Nanocomposite ; Filtration (mathematics) ; Sewage treatment ; Regenerated cellulose ; Materials science ; Bacterial cellulose ; Chemistry ; Polymer science ; Chemical engineering ; Nanotechnology ; Environmental engineering ; Environmental science ; Organic chemistry ; Mathematics ; Engineering Advanced Cellulose Research Studies ; Electrohydrodynamics and Fluid Dynamics ; Electrospun Nanofibers in Biomedical Applications
Authors: Zan Gao | Jiadeng Zhu | Siavash Rajabpour | Kaushik Joshi | Małgorzata Kowalik | Brendan P. Croom | Yosyp Schwab | Liwen Zhang | Clifton H. Bumgardner | Kenneth R. Brown | Diana Elizabeth Burden | James Klett | Adri C. T. van Duin | Leonid V. Zhigilei | Xiaodong Li
Source: Science Advances (article, 2020-04-24)
Field: Materials Science — Materials Chemistry

Graphene reinforced carbon fibers The superlative strength-to-weight ratio of carbon fibers (CFs) can substantially reduce vehicle weight and improve energy efficiency. However, most CFs are derived from costly polyacrylonitrile (PAN), which limits their widespread adoption in the automotive industry. Extensive efforts to produce CFs from low cost, alternative precursor materials have failed to yield a commercially viable product. Here, we revisit PAN to study its conversion chemistry and microstructure evolution, which might provide clues for the design of low-cost CFs. We demonstrate that a small amount of graphene can minimize porosity/defects and reinforce PAN-based CFs. Our experimental results show that 0.075 weight % graphene-reinforced PAN/graphene composite CFs exhibits 225% increase in strength and 184% enhancement in Young's modulus compared to PAN CFs. Atomistic ReaxFF and large-scale molecular dynamics simulations jointly elucidate the ability of graphene to modify the microstructure by promoting favorable edge chemistry and polymer chain alignment. Graphene ; Polyacrylonitrile ; Materials science ; Microstructure ; Modulus ; ReaxFF ; Composite material ; Porosity ; Composite number ; Carbon fibers ; Nanotechnology ; Polymer ; Molecular dynamics ; Chemistry Graphene research and applications ; Fiber-reinforced polymer composites ; Supercapacitor Materials and Fabrication
Authors: Jean‐Marc Joubert | Valérie Paul-Boncour | Fermín Cuevas | Junxian Zhang | M. Latroche
Source: Journal of Alloys and Compounds (article, 2020-12-18)
Field: Materials Science — Materials Chemistry

LaNi5 related AB5 compounds: Structure, properties and applications Intermetallic ; Sorption ; Stoichiometry ; Materials science ; Lanthanide ; Transition metal ; Metal ; Crystal structure ; Thermochemistry ; Thermodynamics ; Chemical engineering ; Chemistry ; Inorganic chemistry ; Physical chemistry ; Metallurgy ; Crystallography ; Organic chemistry ; Catalysis ; Ion Hydrogen Storage and Materials ; Rare-earth and actinide compounds ; Superconductivity in MgB2 and Alloys
Authors: Jiali Li | Kaizhuo Lim | Haitao Yang | Zekun Ren | S. Raghavan | Po‐Yen Chen | Tonio Buonassisi | Xiaonan Wang
Source: Matter (article, 2020-08-01)
Field: Materials Science — Materials Chemistry

AI Applications through the Whole Life Cycle of Material Discovery Computer science ; Business process discovery ; Process (computing) ; Property (philosophy) ; Artificial intelligence ; Scientific discovery ; Knowledge extraction ; Perspective (graphical) ; Data science ; Work in process ; Engineering ; Cognitive science ; Programming language Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Catalysis and Oxidation Reactions
Authors: Wei Zhang | E. Ma
Source: Materials Today (article, 2020-08-13)
Field: Materials Science — Materials Chemistry

Unveiling the structural origin to control resistance drift in phase-change memory materials The global demand for data storage and processing is increasing exponentially. To deal with this challenge, massive efforts have been devoted to the development of advanced memory and computing technologies. Chalcogenide phase-change materials (PCMs) are currently at the forefront of this endeavor. In this Review, we focus on the mechanisms of the spontaneous structural relaxation – aging – of amorphous PCMs, which causes the well-known resistance drift issue that significantly reduces the device accuracy needed for phase-change memory and computing applications. We review the recent breakthroughs in uncovering the structural origin, achieved through state-of-the-art experiments and ab initio atomistic simulations. Emphasis will be placed on the evolving atomic-level details during the relaxation of the complex amorphous structure. We also highlight emerging strategies to control aging, inspired by the in-depth structural understanding, from both materials science and device engineering standpoints, that offer effective solutions to reduce the resistance drift. In addition, we discuss an important new paradigm – machine learning – and the potential power it brings in interrogating amorphous PCMs as well as other disordered alloy systems. Finally, we present an outlook to comment on future research opportunities in amorphous PCMs, as well as on their reduced aging tendency in other advanced applications such as non-volatile photonics. Chalcogenide ; Amorphous solid ; Nanotechnology ; Materials science ; Phase-change memory ; Relaxation (psychology) ; Computer science ; Phase change ; Engineering physics ; Phase (matter) ; Engineering ; Optoelectronics ; Physics ; Chemistry ; Psychology Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials
Authors: Carl Herriott | Ashley D. Spear
Source: Computational Materials Science (article, 2020-02-29)
Field: Materials Science — Materials Chemistry

Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods Convolutional neural network ; Microstructure ; Computer science ; Artificial intelligence ; Ridge ; Property (philosophy) ; Orientation (vector space) ; Feature (linguistics) ; Artificial neural network ; Regression ; Work (physics) ; Elastic net regularization ; Materials science ; Pattern recognition (psychology) ; Machine learning ; Mechanical engineering ; Geometry ; Geology ; Mathematics ; Composite material ; Feature selection Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Industrial Vision Systems and Defect Detection
Authors: Javier Troyano | Ceren Çamur | Luis Garzón‐Tovar | Arnau Carné‐Sánchez | Inhar Imaz | Daniel Maspoch
Source: Accounts of Chemical Research (article, 2020-06-04)
Field: Chemistry — Inorganic Chemistry

Spray-Drying Synthesis of MOFs, COFs, and Related Composites ConspectusMetal-organic frameworks (MOFs) and covalent organic frameworks (COFs) are among the most attractive porous materials today. They exhibit outstanding porosity for countless applications such as gas storage, CO<sub>2</sub> capture, gas separation, sensing, drug delivery, and catalysis. Moreover, researchers have recently begun to combine MOFs or COFs with other functional materials to obtain composites that boast the respective strengths, and mitigate the respective weaknesses, of each component, enabling enhanced performance in many of the aforementioned applications. Accordingly, development of methods for fabrication of MOFs, COFs, and related composites is important for facilitating adoption of these materials in industry. One promising synthetic technique is <i>spray-drying</i>, which is already well-integrated in manufacturing processes for diverse sectors. It enables rapid, continuous and scalable production of dry microspherical powders in a single step, leading to lower fabrication costs and shorter production times compared to traditional methods.In this Account, we outline our ongoing work on spray-drying synthesis of crystalline porous MOFs, COFs, and related composites. Versatile and tunable, spray-drying can be adapted to perform reactions involving coordination and covalent chemistry for the synthesis of micrometer spherical beads/superstructures of MOFs and COFs. Likewise, MOF- and COF-based composites can be synthesized using similar conditions as those for pure MOFs or COFs, through the simple introduction of additional functional materials into the feed precursor solution or colloid. Interestingly, spray-drying can also be done in water, thus providing the basis for its use as a scalable green method for industrial fabrication of these materials. To date, spray-drying has already been scaled up for pilot production (kilogram scale) of MOFs. Composite material ; Spray drying ; Materials science ; Chemical engineering ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Thien Q. Nguyen | Verena Sesin | Anna Kisiała | R. J. Neil Emery
Source: Environmental Toxicology and Chemistry (review, 2020-10-19)
Field: Materials Science — Materials Chemistry

Phytohormonal Roles in Plant Responses to Heavy Metal Stress: Implications for Using Macrophytes in Phytoremediation of Aquatic Ecosystems Heavy metals can represent a threat to the health of aquatic ecosystems. Unlike organic chemicals, heavy metals cannot be eliminated by natural processes such as their degradation into less toxic compounds, and this creates unique challenges for their remediation from soil, water, and air. Phytoremediation, defined as the use of plants for the removal of environmental contaminants, has many benefits compared to other pollution-reducing methods. Phytoremediation is simple, efficient, cost-effective, and environmentally friendly because it can be carried out at the polluted site, which simplifies logistics and minimizes exposure to humans and wildlife. Macrophytes represent a unique tool to remediate diverse environmental media because they can accumulate heavy metals from contaminated sediment via roots, from water via submerged leaves, and from air via emergent shoots. In this review, a synopsis is presented about how plants, especially macrophytes, respond to heavy metal stress; and we propose potential roles that phytohormones can play in the alleviation of metal toxicity in the aquatic environment. We focus on the uptake, translocation, and accumulation mechanisms of heavy metals in organs of macrophytes and give examples of how phytohormones interact with plant defense systems under heavy metal exposure. We advocate for a more in-depth understanding of these processes to inform more effective metal remediation techniques from metal-polluted water bodies. Environ Toxicol Chem 2021;40:7-22. © 2020 SETAC. Phytoremediation ; Macrophyte ; Environmental remediation ; Environmental science ; Environmental chemistry ; Aquatic ecosystem ; Aquatic plant ; Pollution ; Heavy metals ; Contamination ; Ecology ; Chemistry ; Biology Nanoparticles: synthesis and applications ; Plant Stress Responses and Tolerance ; Heavy metals in environment