jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jing Lyu | Xinyue Wen | Uttam Kumar | Yi You | Vicki Chen | Rakesh Joshi
Source: RSC Advances (review, 2018-01-01)
Field: Materials Science — Materials Chemistry

Separation and purification using GO and r-GO membranes Many materials with varied characteristics have been used for water purification and separation applications. Recently discovered graphene oxide (GO), a two-dimensional derivative of graphene has been considered as a promising membrane material for water purification due to its excellent hydrophilicity, high water permeability, and excellent ionic/molecular separation properties. This review is focussed on the possible versatile applicability of GO membranes. It is also known that selective reduction of GO results in membranes with a pore size of ∼0.35 nm, ideally suited for desalination applications. This article presents the applicability of graphene-based membranes for multiple separation applications. This is indeed the first review article outlining a comparison of GO and r-GO membranes and discussing the suitability for applications based on the porosity of the membranes. Membrane ; Graphene ; Materials science ; Desalination ; Oxide ; Water desalination ; Chemical engineering ; Portable water purification ; Nanotechnology ; Porosity ; Chemistry ; Engineering ; Organic chemistry ; Composite material Graphene research and applications ; Membrane Separation Technologies ; Graphene and Nanomaterials Applications
Authors: Yuping He | Ekin D. Cubuk | Mark D. Allendorf | Evan J. Reed
Source: The Journal of Physical Chemistry Letters (article, 2018-07-27)
Field: Materials Science — Materials Chemistry

Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations Emerging applications of metal-organic frameworks (MOFs) in electronic devices will benefit from the design and synthesis of intrinsically, highly electronically conductive MOFs. However, very few are known to exist. It is a challenging task to search for electronically conductive MOFs within the tens of thousands of reported MOF structures. Using a new strategy (i.e., transfer learning) of combining machine learning techniques, statistical multivoting, and ab initio calculations, we screened 2932 MOFs and identified 6 MOF crystal structures that are metallic at the level of semilocal DFT band theory: Mn<sub>2</sub>[Re<sub>6</sub>X<sub>8</sub>(CN)<sub>6</sub>]<sub>4</sub> (X = S, Se,Te), Mn[Re<sub>3</sub>Te<sub>4</sub>(CN)<sub>3</sub>], Hg[SCN]<sub>4</sub>Co[NCS]<sub>4</sub>, and CdC<sub>4</sub>. Five of these structures have been synthesized and reported in the literature, but their electrical characterization has not been reported. Our work demonstrates the potential power of machine learning in materials science to aid in down-selecting from large numbers of potential candidates and provides the information and guidance to accelerate the discovery of novel advanced materials. Ab initio ; Metal ; Materials science ; Computational chemistry ; Ab initio quantum chemistry methods ; Chemical physics ; Chemistry ; Metallurgy ; Organic chemistry ; Molecule Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Jack D. Evans | Bikash Garai | Helge Reinsch | Weijin Li | Stefano Dissegna | Volodymyr Bon | Irena Senkovska | Roland A. Fischer | Stefan Kaskel | Christoph Janiak | Norbert Stock | Dirk Volkmer
Source: Coordination Chemistry Reviews (article, 2018-11-15)
Field: Chemistry — Inorganic Chemistry

Metal–organic frameworks in Germany: From synthesis to function Chemistry ; Metal-organic framework ; Nanotechnology ; Organic synthesis ; Porosity ; Characterization (materials science) ; Catalysis ; Throughput ; Function (biology) ; Biochemical engineering ; Organic chemistry ; Adsorption ; Telecommunications ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Zeolite Catalysis and Synthesis
Authors: Dillon T. Gentekos | Junteng Jia | Erika S. Tirado | Katherine P. Barteau | Detlef‐M. Smilgies | Robert A. DiStasio | Brett P. Fors
Source: Journal of the American Chemical Society (article, 2018-03-09)
Field: Materials Science — Materials Chemistry

Exploiting Molecular Weight Distribution Shape to Tune Domain Spacing in Block Copolymer Thin Films We report a method for tuning the domain spacing ( D<sub>sp</sub>) of self-assembled block copolymer thin films of poly(styrene- block-methyl methacrylate) (PS- b-PMMA) over a large range of lamellar periods. By modifying the molecular weight distribution (MWD) shape (including both the breadth and skew) of the PS block via temporal control of polymer chain initiation in anionic polymerization, we observe increases of up to 41% in D<sub>sp</sub> for polymers with the same overall molecular weight ( M<sub>n</sub> ≈ 125 kg mol<sup>-1</sup>) without significantly changing the overall morphology or chemical composition of the final material. In conjunction with our experimental efforts, we have utilized concepts from population statistics and least-squares analysis to develop a model for predicting D<sub>sp</sub> based on the first three moments of the MWDs. This statistical model reproduces experimental D<sub>sp</sub> values with high fidelity (with mean absolute errors of 1.2 nm or 1.8%) and provides novel physical insight into the individual and collective roles played by the MWD moments in determining this property of interest. This work demonstrates that both MWD breadth and skew have a profound influence over D<sub>sp</sub>, thereby providing an experimental and conceptual platform for exploiting MWD shape as a simple and modular handle for fine-tuning D<sub>sp</sub> in block copolymer thin films. Copolymer ; Polymer ; Chemistry ; Thin film ; Block (permutation group theory) ; Polymer chemistry ; Population ; Lamellar structure ; Materials science ; Nanotechnology ; Crystallography ; Organic chemistry ; Mathematics ; Geometry Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Zheng Chen | Kiran Mathew | Chi Chen | Yiming Chen | Hanmei Tang | Alan Dozier | J. J. Kas | Fernando D. Vila | J. J. Rehr | Louis F. J. Piper | Kristin A. Persson | Shyue Ping Ong
Source: npj Computational Materials (article, 2018-03-14)
Field: Materials Science — Materials Chemistry

Automated generation and ensemble-learned matching of X-ray absorption spectra Abstract X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra. We will demonstrate that the ELSIE algorithm, which combines 33 weak “learners” comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt , an open source machine-learning library for materials science. X-ray absorption spectroscopy ; XANES ; Spectral line ; Absorption spectroscopy ; Computer science ; Materials science ; Physics ; Optics Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; X-ray Diffraction in Crystallography
Authors: Randy Jalem | Kenta Kanamori | Ichiro Takeuchi | Masanobu Nakayama | Hisatsugu Yamasaki | Toshiya Saito
Source: Scientific Reports (article, 2018-04-11)
Field: Materials Science — Materials Chemistry

Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application Safe and robust batteries are urgently requested today for power sources of electric vehicles. Thus, a growing interest has been noted for fabricating those with solid electrolytes. Materials search by density functional theory (DFT) methods offers great promise for finding new solid electrolytes but the evaluation is known to be computationally expensive, particularly on ion migration property. In this work, we proposed a Bayesian-optimization-driven DFT-based approach to efficiently screen for compounds with low ion migration energies ([Formula: see text]. We demonstrated this on 318 tavorite-type Li- and Na-containing compounds. We found that the scheme only requires ~30% of the total DFT-[Formula: see text] evaluations on the average to recover the optimal compound ~90% of the time. Its recovery performance for desired compounds in the tavorite search space is ~2× more than random search (i.e., for [Formula: see text] < 0.3 eV). Our approach offers a promising way for addressing computational bottlenecks in large-scale material screening for fast ionic conductors. Battery (electricity) ; Computer science ; Electrical conductor ; Bayesian probability ; Ion ; Bayesian optimization ; Electrical engineering ; Artificial intelligence ; Physics ; Engineering ; Power (physics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Sijing He | Bing Yin | Hongyun Niu | Yaqi Cai
Source: Applied Catalysis B: Environmental (article, 2018-08-04)
Field: Materials Science — Materials Chemistry

Targeted synthesis of visible-light-driven covalent organic framework photocatalyst via molecular design and precise construction Photocatalysis ; Covalent bond ; Imine ; Monomer ; Materials science ; Visible spectrum ; Nanotechnology ; Photoelectric effect ; Degradation (telecommunications) ; Aldehyde ; Photochemistry ; Semiconductor ; Chemical engineering ; Chemistry ; Catalysis ; Polymer ; Optoelectronics ; Organic chemistry ; Computer science ; Composite material Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Norma Mallegni | Thanh Vu Phuong | Maria‐Beatrice Coltelli | Patrizia Cinelli | Andrea Lazzeri
Source: Materials (article, 2018-01-17)
Field: Materials Science — Biomaterials

Poly(lactic acid) (PLA) Based Tear Resistant and Biodegradable Flexible Films by Blown Film Extrusion Poly(lactic acid) (PLA) was melt mixed in a laboratory extruder with poly(butylene adipate-<i>co</i>-terephthalate) (PBAT) and poly(butylene succinate) (PBS) in the presence of polypropylene glycol di glycidyl ether (EJ400) that acted as both plasticizer and compatibilizer. The process was then scaled up in a semi-industrial extruder preparing pellets having different content of a nucleating agent (LAK). All of the formulations could be processed by blowing extrusion and the obtained films showed mechanical properties dependent on the LAK content. In particular the tearing strength showed a maximum like trend in the investigated composition range. The films prepared with both kinds of blends showed a tensile strength in the range 12-24 MPa, an elongation at break in the range 150-260% and a significant crystallinity. Extrusion ; Lactic acid ; Materials science ; Composite material ; Bacteria biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Carbon dioxide utilization in catalysis
Authors: Mikhail Y. Shalaginov | Sawyer D. Campbell | Sensong An | Yifei Zhang | Carlos Rı́os | Eric B. Whiting | Yuhao Wu | Lei Kang | Bowen Zheng | Clayton Fowler | Hualiang Zhang | Douglas H. Werner | Juejun Hu | Tian Gu
Source: Nanophotonics (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design for quality: reconfigurable flat optics based on active metasurfaces Abstract Optical metasurfaces, planar subwavelength nanoantenna arrays with the singular ability to sculpt wavefront in almost arbitrary manners, are poised to become a powerful tool enabling compact and high‐performance optics with novel functionalities. A particularly intriguing research direction within this field is active metasurfaces, whose optical response can be dynamically tuned postfabrication, thus allowing a plurality of applications unattainable with traditional bulk optics. Designing reconfigurable optics based on active metasurfaces is, however, presented with a unique challenge, since the optical quality of the devices must be optimized at multiple optical states. In this article, we provide a critical review on the active meta‐optics design principles and algorithms that are applied across structural hierarchies ranging from single meta‐atoms to full meta‐optical devices. The discussed approaches are illustrated by specific examples of reconfigurable metasurfaces based on optical phase‐change materials. Wavefront ; Metamaterial ; Planar ; Optics ; Active optics ; Computer science ; Adaptive optics ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Αλέξανδρος Σολωμού | Guang Zhao | Shahin Boluki | Jobin K. Joy | Xiaoning Qian | İbrahim Karaman | Raymundo Arróyave | Dimitris C. Lagoudas
Source: Materials & Design (article, 2018-10-10)
Field: Materials Science — Materials Chemistry

Multi-objective Bayesian materials discovery: Application on the discovery of precipitation strengthened NiTi shape memory alloys through micromechanical modeling In this study, a framework for the multi-objective materials discovery based on Bayesian approaches is developed and demonstrated on the efficient discovery of precipitation-strengthened NiTi shape memory alloys with up to three desired properties. The framework is used to minimize the required computational experiments for the discovery of the targeted materials, performed by a thermodynamically-consistent micromechanical model that predicts the materials response based on its composition and microstructure. The developed scheme features a Bayesian optimal experimental design process that operates in a closed loop. During each iteration of the process, a Gaussian process regression model is constructed based on the available computational data and used to emulate the response of the material in the unexplored regions of the materials design space. The sequential exploration of the materials design space is carried out by using an optimal experiment selection policy based on the expected hyper-volume improvement acquisition function that accounts for the uncertainty on the predictions of the regression model. The results indicate the considerable efficiency of the proposed framework, in discovering the targeted materials, compared with the exhaustive model-driven exploration of the materials design space. Gaussian process ; Bayesian optimization ; Materials science ; Process (computing) ; Bayesian probability ; Bayesian inference ; Computer science ; Nickel titanium ; Shape-memory alloy ; Machine learning ; Artificial intelligence ; Gaussian Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Grey System Theory Applications
Authors: Sai Puneet Desai | Jingyun Ye | Jian Zheng | Magali Ferrandon | Thomas E. Webber | Ana E. Platero‐Prats | Jiaxin Duan | Paula García-Holley | Donald M. Camaioni | Karena W. Chapman | Massimiliano Delferro | Omar K. Farha | John L. Fulton | Laura Gagliardi | Johannes A. Lercher | R. Lee Penn | Andreas Stein | Connie C. Lu
Source: Journal of the American Chemical Society (article, 2018-10-23)
Field: Chemistry — Inorganic Chemistry

Well-Defined Rhodium–Gallium Catalytic Sites in a Metal–Organic Framework: Promoter-Controlled Selectivity in Alkyne Semihydrogenation to <i>E</i>-Alkenes Promoters are ubiquitous in industrial heterogeneous catalysts. The wider roles of promoters in accelerating catalysis and/or controlling selectivity are, however, not well understood. A model system has been developed where a heterobimetallic active site comprising an active metal (Rh) and a promoter ion (Ga) is preassembled and delivered onto a metal-organic framework (MOF) support, NU-1000. The Rh-Ga sites in NU-1000 selectively catalyze the hydrogenation of acyclic alkynes to E-alkenes. The overall stereoselectivity is complementary to the well-known Lindlar's catalyst, which generates Z-alkenes. The role of the Ga in promoting this unusual selectivity is evidenced by the lack of semihydrogenation selectivity when Ga is absent and only Rh is present in the active site. Chemistry ; Rhodium ; Alkyne ; Selectivity ; Catalysis ; Gallium ; Metal ; Combinatorial chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Catalytic Processes in Materials Science
Authors: Aayush Gupta | Manish Mittal | Mahesh Kumar Singh | Steven L. Suib | O.P. Pandey
Source: Scientific Reports (article, 2018-09-05)
Field: Materials Science — Materials Chemistry

Low temperature synthesis of NbC/C nano-composites as visible light photoactive catalyst A facile carbothermal route was adopted to obtain niobium carbide nanoparticles (NPs) embedded in carbon network from Nb<sub>2</sub>O<sub>5</sub> to study photocatalytic behavior. Optimization of synthesis parameters to obtain single phase NbC NPs has been successfully done. The phase identification, morphology and nature of carbon were determined with the help of X-ray diffraction, transmission electron microscopy (TEM) and Raman spectroscopy. X-ray photoelectron spectroscopy (XPS) suggested the presence of multiple oxidation states of Nb associated to NbC and NbC<sub>x</sub>O<sub>y</sub> centers on the surface of NPs. Due to the presence of NbC<sub>x</sub>O<sub>y</sub> on the surface of NPs, absorption under visible region of EM spectrum has been observed by UV-visible spectroscopy. Different organic dyes (RhB, MB and MO) were used to study the effect of holding time on the photocatalytic performance of as-synthesized samples. RhB dye was found to be the most sensitive organic molecule among all the considered dyes and degraded 78% in 120 min. X-ray photoelectron spectroscopy ; Photocatalysis ; Materials science ; Raman spectroscopy ; Transmission electron microscopy ; Visible spectrum ; Nanoparticle ; Absorption spectroscopy ; Chemical engineering ; Spectroscopy ; Catalysis ; Scanning electron microscope ; Carbon fibers ; Carbide ; Photochemistry ; Nanotechnology ; Composite material ; Chemistry ; Composite number ; Organic chemistry ; Optoelectronics ; Optics Diamond and Carbon-based Materials Research ; Graphene research and applications ; Catalytic Processes in Materials Science
Authors: Dominic J. Wales | Qun Cao | Katharina Kastner | Erno Karjalainen | Graham N. Newton | Víctor Sans
Source: Advanced Materials (article, 2018-04-30)
Field: Materials Science — Materials Chemistry

3D‐Printable Photochromic Molecular Materials for Reversible Information Storage The formulation of advanced molecular materials with bespoke polymeric ionic-liquid matrices that stabilize and solubilize hybrid organic-inorganic polyoxometalates and allow their processing by additive manufacturing, is effectively demonstrated. The unique photo and redox properties of nanostructured polyoxometalates are translated across the scales (from molecular design to functional materials) to yield macroscopic functional devices with reversible photochromism. These properties open a range of potential applications including reversible information storage based on controlled topological and temporal reduction/oxidation of pre-formed printed devices. This approach pushes the boundaries of 3D printing to the molecular limits, allowing the freedom of design enabled by 3D printing to be coupled with the molecular tuneability of polymerizable ionic liquids and the photoactivity and orbital engineering possible with hybrid polyoxometalates. Photochromism ; Materials science ; Bespoke ; Ionic liquid ; Nanotechnology ; Ionic bonding ; 3D printing ; Molecular engineering ; Catalysis ; Organic chemistry ; Ion ; Chemistry ; Composite material Polyoxometalates: Synthesis and Applications ; Supramolecular Self-Assembly in Materials ; Advanced Photocatalysis Techniques
Authors: Haichen Li | Christopher R. Collins | Matteus Tanha | Geoffrey J. Gordon | David Yaron
Source: Journal of Chemical Theory and Computation (article, 2018-10-15)
Field: Materials Science — Materials Chemistry

A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by implementing self-consistent-charge Density-Functional-Tight-Binding (DFTB) theory as a layer for use in deep learning models. The DFTB layer takes, as input, Hamiltonian matrix elements generated from earlier layers and produces, as output, electronic properties from self-consistent field solutions of the corresponding DFTB Hamiltonian. Backpropagation enables efficient training of the model to target electronic properties. Two types of input to the DFTB layer are explored, splines and feed-forward neural networks. Because overfitting can cause models trained on smaller molecules to perform poorly on larger molecules, regularizations are applied that penalize nonmonotonic behavior and deviation of the Hamiltonian matrix elements from those of the published DFTB model used to initialize the model. The approach is evaluated on 15 700 hydrocarbons by comparing the root-mean-square error in energy and dipole moment, on test molecules with eight heavy atoms, to the error from the initial DFTB model. When trained on molecules with up to seven heavy atoms, the spline model reduces the test error in energy by 60% and in dipole moments by 42%. The neural network model performs somewhat better, with error reductions of 67% and 59%, respectively. Training on molecules with up to four heavy atoms reduces performance, with both the spline and neural net models reducing the test error in energy by about 53% and in dipole by about 25%. Overfitting ; Hamiltonian (control theory) ; Dipole ; Tight binding ; Artificial neural network ; Backpropagation ; Computer science ; Quantum ; Mean squared error ; Statistical physics ; Test data ; Moment (physics) ; Hamiltonian matrix ; Biological system ; Algorithm ; Electronic structure ; Artificial intelligence ; Physics ; Quantum mechanics ; Mathematics ; Mathematical optimization ; Eigenvalues and eigenvectors Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials