jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jinhu Zhang | Xuexiong Li | Dongsheng Xu | Rui Yang
Source: Progress in Natural Science Materials International (article, 2019-06-01)
Field: Materials Science — Materials Chemistry

Recent progress in the simulation of microstructure evolution in titanium alloys Titanium alloys (Ti-alloys) have complicated multiscale hierarchical structures that enabled a variety of excellent mechanical properties, such as strength, ductility, fatigue and creep resistance etc. But how are the microstructures formed under different conditions, and what are the inter-dependencies between the properties and various microstructures are not easy to reveal solely by experiments. In the last two decades, multiscale modeling and simulation have been carried out, and promoted our understanding of the microstructure formation and their deformation mechanisms. In the present paper, recent progresses are reviewed and future trends analyzed, on the simulation of microstructure evolution in Ti-alloys and how these are combined with experimental characterization and verification, to reveal the controlling factor and hidden mechanism under various deformation/transformation conditions; and to promote the optimization of thermo-mechanical processing. It is recognized that, future computations should pay more attention to the bottleneck problems in the design and application of Ti-alloys and play a more important role in identifying specific controlling factors and revealing the relevant working mechanisms in more complicated process, such as thermo-mechanical processing, powder metallurgy or additive manufacturing. In addition, artificial intelligence (AI) may assist the screening of new materials. These efforts may accelerate the development and application of new alloys, and at lower cost, especially for Ti-alloys in aero/space engine satisfying multiple performance requirements such as strength, toughness, creep resistance, fatigue life and other properties. Materials science ; Microstructure ; Ductility (Earth science) ; Creep ; Deformation (meteorology) ; Toughness ; Titanium alloy ; Deformation mechanism ; Mechanical engineering ; Metallurgy ; Composite material ; Alloy ; Engineering Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Lingyang Liu | Lijun Su | Yulan Lü | Qingnuan Zhang | Li Zhang | Shulai Lei | Siqi Shi | Mikhael D. Levi | Xingbin Yan
Source: Advanced Functional Materials (article, 2019-01-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Origin of Electrochemical Actuation of MnO<sub>2</sub>/Ni Bilayer Film Derived by Redox Pseudocapacitive Process Abstract Pseudocapacitance‐induced electrochemical actuators (EC‐actuators) have attracted great attention in robots and artificial intelligence technologies. Despite major efforts to design such EC‐actuators, a molecular‐level understanding of the deformation mechanism is still lacking. Here, a reversible deformation of a freestanding MnO 2 /Ni bilayer film is demonstrated and in situ electrochemical atomic force microscopy, in situ Raman spectroscopy, and density functional theory simulation are used to study the origin of the deformation. The results show that the electrochemical actuation of the MnO 2 /Ni film is highly related with the redox pseudocapacitive behavior of MnO 2 layer. Valence state variation of Mn element, shortening and lengthening of MnO bond, and insertion and extraction of Na + ions, which all result from the redox pseudocapacitance of MnO 2 during charging and discharging, eventually lead to the reversible contraction and expansion of MnO 2 morphology. Such action counters with the nonactive Ni layer, finally inducing the reversible deformation of the MnO 2 /Ni bilayer film. It is believed that the study can provide useful guidance to design better EC‐actuators in the future. Pseudocapacitance ; Materials science ; Bilayer ; Redox ; Electrochemistry ; Raman spectroscopy ; Electrochemical energy storage ; Nanotechnology ; Valence (chemistry) ; Actuator ; Chemical engineering ; Supercapacitor ; Membrane ; Metallurgy ; Electrode ; Physical chemistry Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics
Authors: Tanay Kundu | Bhuvan B. Shah | Linius Bolinois | Dan Zhao
Source: Chemistry of Materials (article, 2019-03-04)
Field: Chemistry — Inorganic Chemistry

Functionalization-Induced Breathing Control in Metal–Organic Frameworks for Methane Storage with High Deliverable Capacity Metal–organic frameworks (MOFs) that can undergo structural flexibility upon gas sorption cycle bear enormous potential particularly as adsorbents for methane storage. A molecular level control over such flexibility can be advantageous for the development of smart adsorbents with increased deliverable capacity. Herein, we report the flexible and stable MIL-53(Al) type MOFs, whose breathing behavior can be controlled by systematic installation of hydrogen bonding sites into the frameworks. Such a control has been fine-tuned to induce high deliverable capacity in MOFs by changing gas pressure, temperature, and density. The establishment of a structure–property relationship for methane storage and successful pelletization of MOFs pave the way for MOF-based onboard natural gas storage. Deliverable ; Metal-organic framework ; Natural gas storage ; Methane ; Flexibility (engineering) ; Materials science ; Nanotechnology ; Natural gas ; Hydrogen storage ; Adsorption ; Environmental science ; Waste management ; Chemistry ; Organic chemistry ; Engineering ; Composite material ; Systems engineering Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Carbon dioxide utilization in catalysis
Authors: Shoushun Chen | Soumya Mukherjee | Bryan E. G. Lucier | Ying Guo | Y. T. Angel Wong | Victor V. Terskikh | Michael J. Zaworotko | Yining Huang
Source: Journal of the American Chemical Society (article, 2019-08-20)
Field: Chemistry — Inorganic Chemistry

Cleaving Carboxyls: Understanding Thermally Triggered Hierarchical Pores in the Metal–Organic Framework MIL-121 Carboxylic acid linker ligands are known to form strong metal-carboxylate bonds to afford many different variations of permanently microporous metal-organic frameworks (MOFs). A controlled approach to decarboxylation of the ligands in carboxylate-based MOFs could result in structural modifications, offering scope to improve existing properties or to unlock entirely new properties. In this work, we demonstrate that the microporous MOF MIL-121 is transformed to a hierarchically porous MOF via thermally triggered decarboxylation of its linker. Decarboxylation and the introduction of hierarchical porosity increases the surface area of this material from 13 to 908 m<sup>2</sup>/g and enhances gas adsorption uptake for industrially relevant gases (i.e., CO<sub>2</sub>, C<sub>2</sub>H<sub>2</sub>, C<sub>2</sub>H<sub>4</sub>, and CH<sub>4</sub>). For example, CO<sub>2</sub> uptake in hierarchically porous MIL-121 is improved 8.5 times over MIL-121, reaching 215.7 cm<sup>3</sup>/g at 195 K and 1 bar; CH<sub>4</sub> uptake is 132.3 cm<sup>3</sup>/g at 298 K and 80 bar in hierarchically porous MIL-121 versus zero in unmodified MIL-121. The approach taken was validated using a related aluminum-based MOF, ISOMIL-53. However, many specifics of the decarboxylation procedure in MOFs have yet to be unraveled and demand prompt examination. Decarboxylation, the formation of heterogeneous hierarchical pores, gas uptakes, and host-guest interactions are comprehensively investigated using variable-temperature multinuclear solid-state NMR spectroscopy, X-ray diffraction, electron microscopy, and gas adsorption; we propose a mechanism for how decarboxylation proceeds and which local structural features are involved. Understanding the complex relationship among the molecular-level MOF structure, thermal stability, and the decarboxylation process is essential to fine-tune MOF porosity, thus offering a systematic approach to the design of hierarchically porous, custom-built MOFs suited for targeted applications. Decarboxylation ; Chemistry ; Microporous material ; Metal-organic framework ; Carboxylate ; Porosity ; Adsorption ; Mesoporous material ; Chemical engineering ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; X-ray Diffraction in Crystallography
Authors: Stefan Zechel | Martin D. Hager | Tobias Priemel | Matthew J. Harrington
Source: Biomimetics (review, 2019-02-27)
Field: Materials Science — Polymers and Plastics

Healing through Histidine: Bioinspired Pathways to Self-Healing Polymers via Imidazole–Metal Coordination Biology offers a valuable inspiration toward the development of self-healing engineering composites and polymers. In particular, chemical level design principles extracted from proteinaceous biopolymers, especially the mussel byssus, provide inspiration for design of autonomous and intrinsic healing in synthetic polymers. The mussel byssus is an acellular tissue comprised of extremely tough protein-based fibers, produced by mussels to secure attachment on rocky surfaces. Threads exhibit self-healing response following an apparent plastic yield event, recovering initial material properties in a time-dependent fashion. Recent biochemical analysis of the structure-function relationships defining this response reveal a key role of sacrificial cross-links based on metal coordination bonds between Zn<sup>2+</sup> ions and histidine amino acid residues. Inspired by this example, many research groups have developed self-healing polymeric materials based on histidine (imidazole)-metal chemistry. In this review, we provide a detailed overview of the current understanding of the self-healing mechanism in byssal threads, and an overview of the current state of the art in histidine- and imidazole-based synthetic polymers. Byssus ; Histidine ; Imidazole ; Polymer ; Materials science ; Self-healing ; Biomimetic materials ; Nanotechnology ; Synthetic polymer ; Mussel ; Chemistry ; Organic chemistry ; Composite material ; Ecology ; Biology Polymer composites and self-healing ; Polymer Surface Interaction Studies ; Electrospun Nanofibers in Biomedical Applications
Authors: Maria Lykaki | Sofia Stefa | Sónia A. C. Carabineiro | Pavlos K. Pandis | Vassilis N. Stathopoulos | Michalis Konsolakis
Source: Catalysts (article, 2019-04-19)
Field: Materials Science — Materials Chemistry

Facet-Dependent Reactivity of Fe2O3/CeO2 Nanocomposites: Effect of Ceria Morphology on CO Oxidation Ceria has been widely studied either as catalyst itself or support of various active phases in many catalytic reactions, due to its unique redox and surface properties in conjunction to its lower cost, compared to noble metal-based catalytic systems. The rational design of catalytic materials, through appropriate tailoring of the particles’ shape and size, in order to acquire highly efficient nanocatalysts, is of major significance. Iron is considered to be one of the cheapest transition metals while its interaction with ceria support and their shape-dependent catalytic activity has not been fully investigated. In this work, we report on ceria nanostructures morphological effects (cubes, polyhedra, rods) on the textural, structural, surface, redox properties and, consequently, on the CO oxidation performance of the iron-ceria mixed oxides (Fe2O3/CeO2). A full characterization study involving N2 adsorption at –196 °C, X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), temperature programmed reduction (TPR), and X-ray photoelectron spectroscopy (XPS) was performed. The results clearly revealed the key role of support morphology on the physicochemical properties and the catalytic behavior of the iron-ceria binary system, with the rod-shaped sample exhibiting the highest catalytic performance, both in terms of conversion and specific activity, due to its improved reducibility and oxygen mobility, along with its abundance in Fe2+ species. Nanomaterial-based catalyst ; Catalysis ; X-ray photoelectron spectroscopy ; Materials science ; Chemical engineering ; Noble metal ; Redox ; Scanning electron microscope ; Transition metal ; Catalytic oxidation ; Nanoparticle ; Nanotechnology ; Metal ; Chemistry ; Metallurgy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Mitsutaro Umehara | Helge S. Stein | Dan Guevarra | Paul F. Newhouse | David A. Boyd | John M. Gregoire
Source: npj Computational Materials (article, 2019-03-08)
Field: Materials Science — Materials Chemistry

Analyzing machine learning models to accelerate generation of fundamental materials insights Abstract Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data. Computer science ; Artificial intelligence ; Key (lock) ; Metric (unit) ; Convolutional neural network ; Machine learning ; Identification (biology) ; Artificial neural network ; Interpretation (philosophy) ; Chemical space ; Space (punctuation) ; SIGNAL (programming language) ; Data science ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Michael Chatzidakis | Gianluigi A. Botton
Source: Scientific Reports (article, 2019-02-14)
Field: Chemistry — Electrochemistry

Towards calibration-invariant spectroscopy using deep learning The interaction between matter and electromagnetic radiation provides a rich understanding of what the matter is composed of and how it can be quantified using spectrometers. In many cases, however, the calibration of the spectrometer changes as a function of time (such as in electron spectrometers), or the absolute calibration may be different between different instruments. Calibration differences cause difficulties in comparing the absolute position of measured emission or absorption peaks between different instruments and even different measurements taken at different times on the same instrument. Present methods of avoiding this issue involve manual feature extraction of the original signal or qualitative analysis. Here we propose automated feature extraction using deep convolutional neural networks to determine the class of compound given only the shape of the spectrum. We classify three unique electronic environments of manganese (being relevant to many battery materials applications) in electron energy loss spectroscopy using 2001 spectra we collected in addition to testing on spectra from different instruments. We test a variety of commonly used neural network architectures found in the literature and propose a new fully convolutional architecture with improved translation-invariance which is immune to calibration differences. Spectrometer ; Computer science ; Calibration ; Convolutional neural network ; Artificial intelligence ; Spectroscopy ; Invariant (physics) ; Pattern recognition (psychology) ; Physics ; Optics ; Mathematics ; Statistics Electrochemical Analysis and Applications ; Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Han Liu | Tony Zhang | N. M. Anoop Krishnan | Morten M. Smedskjær | Joseph V. Ryan | Stéṕhane Gin | Mathieu Bauchy
Source: npj Materials Degradation (article, 2019-08-29)
Field: Materials Science — Materials Chemistry

Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning Abstract Machine learning (ML) regression methods are promising tools to develop models predicting the properties of materials by learning from existing databases. However, although ML models are usually good at interpolating data, they often do not offer reliable extrapolations and can violate the laws of physics. Here, to address the limitations of traditional ML, we introduce a “topology-informed ML” paradigm—wherein some features of the network topology (rather than traditional descriptors) are used as fingerprint for ML models—and apply this method to predict the forward (stage I) dissolution rate of a series of silicate glasses. We demonstrate that relying on a topological description of the atomic network (i) increases the accuracy of the predictions, (ii) enhances the simplicity and interpretability of the predictive models, (iii) reduces the need for large training sets, and (iv) improves the ability of the models to extrapolate predictions far from their training sets. As such, topology-informed ML can overcome the limitations facing traditional ML (e.g., accuracy vs. simplicity tradeoff) and offers a promising route to predict the properties of materials in a robust fashion. Interpretability ; Computer science ; Simplicity ; Topology (electrical circuits) ; Silicate glass ; Machine learning ; Artificial intelligence ; Network topology ; Data mining ; Algorithm ; Mathematics ; Materials science ; Physics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Topological and Geometric Data Analysis
Authors: Neil W. J. Scott | Mark J. Ford | Christoph Schotes | Rachel R. Parker | Adrian C. Whitwood | Ian J. S. Fairlamb
Source: Chemical Science (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

The ubiquitous cross-coupling catalyst system ‘Pd(OAc)<sub>2</sub>’/2PPh<sub>3</sub> forms a unique dinuclear Pd<sup>I</sup> complex: an important entry point into catalytically competent cyclic Pd<sub>3</sub> clusters Pd<sub>3</sub>-type clusters generated from Pd(OAc)<sub>2</sub>/<italic>n</italic>PPh<sub>3</sub>, formed <italic>via</italic> a dinuclear Pd(<sc>i</sc>) species, exhibit high activity in Suzuki–Miyaura cross-coupling. Catalysis ; Palladium ; Coupling (piping) ; Chemistry ; Crystallography ; Physics ; Stereochemistry ; Materials science ; Organic chemistry Catalytic Cross-Coupling Reactions ; Nanocluster Synthesis and Applications ; Organometallic Complex Synthesis and Catalysis
Authors: Patrick Weber | Cédric Pissis | Rafael Navaza | Ariel Méchaly | Frederick Saul | Pedro M. Alzari | Ahmed Haouz
Source: Molecules (review, 2019-12-05)
Field: Materials Science — Materials Chemistry

High-Throughput Crystallization Pipeline at the Crystallography Core Facility of the Institut Pasteur The availability of whole-genome sequence data, made possible by significant advances in DNA sequencing technology, led to the emergence of structural genomics projects in the late 1990s. These projects not only significantly increased the number of 3D structures deposited in the Protein Data Bank in the last two decades, but also influenced present crystallographic strategies by introducing automation and high-throughput approaches in the structure-determination pipeline. Today, dedicated crystallization facilities, many of which are open to the general user community, routinely set up and track thousands of crystallization screening trials per day. Here, we review the current methods for high-throughput crystallization and procedures to obtain crystals suitable for X-ray diffraction studies, and we describe the crystallization pipeline implemented in the medium-scale crystallography platform at the Institut Pasteur (Paris) as an example. Pipeline (software) ; Crystallization ; Throughput ; Structural genomics ; Automation ; Computer science ; Protein crystallization ; Crystallography ; Computational biology ; Engineering ; Chemistry ; Biology ; Protein structure ; Mechanical engineering ; Chemical engineering ; Operating system Enzyme Structure and Function ; Protein Structure and Dynamics ; Crystallization and Solubility Studies
Authors: Li-Fang Zhu | Yufei Zheng | Jiannan Fan | Yuanfa Yao | Zeeshan Ahmad | Ming‐Wei Chang
Source: European Journal of Pharmaceutical Sciences (article, 2019-07-11)
Field: Materials Science — Biomaterials

A novel core-shell nanofiber drug delivery system intended for the synergistic treatment of melanoma Nanofiber ; Drug delivery ; Drug ; Shell (structure) ; Core (optical fiber) ; Delivery system ; Chemistry ; Nanotechnology ; Pharmacology ; Medicine ; Materials science ; Composite material Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; RNA Interference and Gene Delivery
Authors: Chunmei Zhang | Tianwei He | Sri Kasi Matta | Ting Liao | Liangzhi Kou | Zhongfang Chen | Aijun Du
Source: The Journal of Physical Chemistry Letters (article, 2019-05-03)
Field: Materials Science — Materials Chemistry

Predicting Novel 2D MB<sub>2</sub> (M = Ti, Hf, V, Nb, Ta) Monolayers with Ultrafast Dirac Transport Channel and Electron-Orbital Controlled Negative Poisson’s Ratio Three-dimensional diborides MB<sub>2</sub>, featured in stacking the M layer above the middle of the honeycomb boron layer, have been extensively studied. However, little information on the two-dimensional counterparts of MB<sub>2</sub> is available. Here, by means of evolutionary algorithm and first-principles calculations, we extensively studied the monolayer MB<sub>2</sub> crystal with M elements ranging from group IIA to IVA covering 34 candidates. Our computations screened out eight stable monolayers MB<sub>2</sub> (M = Be, Mg, Fe, Ti, Hf, V, Nb, Ta), and they exhibit Dirac-like band structures. Dramatically, among them, groups IVB-VB transition-metal diboride MB<sub>2</sub> (M = Ti, Hf, V, Nb, Ta) are predicted to be a new class of auxetic materials. They harbor in-plane negative Poisson's ratio (NPR) arising mainly from the orbital hybridization between M d and Boron p orbitals, which is distinct from previously reported auxetic materials. The unusual NPR and the Dirac transport channel of these materials are applicable to nanoelectronics and nanomechanics. Monolayer ; Poisson's ratio ; Orbital hybridisation ; Materials science ; Dirac (video compression format) ; Auxetics ; Crystallography ; Atomic orbital ; Condensed matter physics ; Electron ; Nanotechnology ; Chemistry ; Physics ; Composite material ; Poisson distribution MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications