jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suttipong Wannapaiboon | Andreas Schneemann | Inke Hante | Min Tu | Konstantin Epp | A. Lisa Semrau | Christian Sternemann | Michael Paulus | Samuel J. Baxter | Gregor Kieslich | Roland A. Fischer
Source: Nature Communications (article, 2019-01-21)
Field: Chemistry — Inorganic Chemistry

Control of structural flexibility of layered-pillared metal-organic frameworks anchored at surfaces Flexible metal-organic frameworks (MOFs) are structurally flexible, porous, crystalline solids that show a structural transition in response to a stimulus. If MOF-based solid-state and microelectronic devices are to be capable of leveraging such structural flexibility, then the integration of MOF thin films into a device configuration is crucial. Here we report the targeted and precise anchoring of Cu-based alkylether-functionalised layered-pillared MOF crystallites onto substrates via stepwise liquid-phase epitaxy. The structural transformation during methanol sorption is monitored by in-situ grazing incidence X-ray diffraction. Interestingly, spatially-controlled anchoring of the flexible MOFs on the surface induces a distinct structural responsiveness which is different from the bulk powder and can be systematically controlled by varying the crystallite characteristics, for instance dimensions and orientation. This fundamental understanding of thin-film flexibility is of paramount importance for the rational design of MOF-based devices utilising the structural flexibility in specific applications such as selective sensors. Materials science ; Crystallite ; Nanotechnology ; Flexibility (engineering) ; Microelectronics ; Rational design ; Metal-organic framework ; Porosity ; Fabrication ; Thin film ; Sorption ; Chemistry ; Composite material ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Koki Muraoka | Yuki Sada | Daiki Miyazaki | Watcharop Chaikittisilp | Tatsuya Okubo
Source: Nature Communications (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials Correlating synthesis conditions and their consequences is a significant challenge, particularly for materials formed as metastable phases via kinetically controlled pathways, such as zeolites, owing to a lack of descriptors that effectively illustrate the synthesis protocols and their corresponding results. This study analyzes the synthetic records of zeolites compiled from the literature using machine learning techniques to rationalize physicochemical, structural, and heuristic insights to their chemistry. The synthesis descriptors extracted from the machine learning models are used to identify structure descriptors with the appropriate importance. A similarity network of crystal structures based on the structure descriptors shows the formation of communities populated by synthetically similar materials, including those outside the dataset. Crossover experiments based on previously overlooked structural similarities reveal the synthesis similarity of zeolites, confirming the synthesis-structure relationship. This approach is applicable to any system to rationalize empirical knowledge, populate synthesis records, and discover novel materials. Computer science ; Similarity (geometry) ; Heuristic ; Artificial intelligence ; Metastability ; Data mining ; Machine learning ; Chemistry Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Surface Chemistry and Catalysis
Authors: Colin A. Grambow | Yi‐Pei Li | William H. Green
Source: The Journal of Physical Chemistry A (article, 2019-06-17)
Field: Materials Science — Materials Chemistry

Accurate Thermochemistry with Small Data Sets: A Bond Additivity Correction and Transfer Learning Approach Machine learning provides promising new methods for accurate yet rapid prediction of molecular properties, including thermochemistry, which is an integral component of many computer simulations, particularly automated reaction mechanism generation. Often, very large data sets with tens of thousands of molecules are required for training the models, but most data sets of experimental or high-accuracy quantum mechanical quality are much smaller. To overcome these limitations, we calculate new high-level data sets and derive bond additivity corrections to significantly improve enthalpies of formation. We adopt a transfer learning technique to train neural network models that achieve good performance even with a relatively small set of high-accuracy data. The training data for the entropy model are carefully selected so that important conformational effects are captured. The resulting models are generally applicable thermochemistry predictors for organic compounds with oxygen and nitrogen heteroatoms that approach experimental and coupled cluster accuracy while only requiring molecular graph inputs. Due to their versatility and the ease of adding new training data, they are poised to replace conventional estimation methods for thermochemical parameters in reaction mechanism generation. Since high-accuracy data are often sparse, similar transfer learning approaches are expected to be useful for estimating many other molecular properties. Thermochemistry ; Computer science ; Additive function ; Machine learning ; Artificial intelligence ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Rodrigo Garcı́a-Muelas | Núria López
Source: Nature Communications (article, 2019-10-15)
Field: Materials Science — Materials Chemistry

Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals The rational design of heterogeneous catalysts relies on the efficient survey of mechanisms by density functional theory (DFT). However, massive reaction networks cannot be sampled effectively as they grow exponentially with the size of reactants. Here we present a statistical principal component analysis and regression applied to the DFT thermochemical data of 71 C[Formula: see text]-C[Formula: see text] species on 12 close-packed metal surfaces. Adsorption is controlled by covalent ([Formula: see text]-band center) and ionic terms (reduction potential), modulated by conjugation and conformational contributions. All formation energies can be reproduced from only three key intermediates (predictors) calculated with DFT. The results agree with accurate experimental measurements having error bars comparable to those of DFT. The procedure can be extended to single-atom and near-surface alloys reducing the number of explicit DFT calculation needed by a factor of 20, thus paving the way for a rapid and accurate survey of whole reaction networks on multimetallic surfaces. Thermochemistry ; Transition metal ; Transition (genetics) ; Chemical physics ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Statistical physics ; Physical chemistry ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Neural Networks and Applications
Authors: Diego F. Gauto | Leandro F. Estrozi | Charles D. Schwieters | Grégory Effantin | Pavel Macek | Rémy Sounier | Astrid C. Sivertsen | Elena Yu. Schmidt | Rime Kerfah | Guillaume Mas | Jacques‐Philippe Colletier | Peter Güntert | Adrien Favier | Guy Schoehn | Paul Schanda | Jérôme Boisbouvier
Source: Nature Communications (article, 2019-06-19)
Field: Materials Science — Materials Chemistry

Integrated NMR and cryo-EM atomic-resolution structure determination of a half-megadalton enzyme complex Atomic-resolution structure determination is crucial for understanding protein function. Cryo-EM and NMR spectroscopy both provide structural information, but currently cryo-EM does not routinely give access to atomic-level structural data, and, generally, NMR structure determination is restricted to small (<30 kDa) proteins. We introduce an integrated structure determination approach that simultaneously uses NMR and EM data to overcome the limits of each of these methods. The approach enables structure determination of the 468 kDa large dodecameric aminopeptidase TET2 to a precision and accuracy below 1 Å by combining secondary-structure information obtained from near-complete magic-angle-spinning NMR assignments of the 39 kDa-large subunits, distance restraints from backbone amides and ILV methyl groups, and a 4.1 Å resolution EM map. The resulting structure exceeds current standards of NMR and EM structure determination in terms of molecular weight and precision. Importantly, the approach is successful even in cases where only medium-resolution cryo-EM data are available. Resolution (logic) ; Nuclear magnetic resonance spectroscopy ; Chemistry ; Structural biology ; Protein structure ; Crystallography ; Computer science ; Stereochemistry ; Biochemistry Enzyme Structure and Function ; Peptidase Inhibition and Analysis ; Glycosylation and Glycoproteins Research
Authors: Michele Kotiuga | Zhen Zhang | Jiarui Li | Fanny Rodolakis | Hua Zhou | Ronny Sutarto | Feizhou He | Qi Wang | Yifei Sun | Ying Wang | Neda Alsadat Aghamiri | Steven Bennett Hancock | Leonid P. Rokhinson | D. P. Landau | Yohannes Abate | J. W. Freeland | Riccardo Comin | Shriram Ramanathan | Karin M. Rabe
Source: Proceedings of the National Academy of Sciences (article, 2019-10-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Carrier localization in perovskite nickelates from oxygen vacancies Point defects, such as oxygen vacancies, control the physical properties of complex oxides, relevant in active areas of research from superconductivity to resistive memory to catalysis. In most oxide semiconductors, electrons that are associated with oxygen vacancies occupy the conduction band, leading to an increase in the electrical conductivity. Here we demonstrate, in contrast, that in the correlated-electron perovskite rare-earth nickelates, <i>R</i>NiO<sub>3</sub> (<i>R</i> is a rare-earth element such as Sm or Nd), electrons associated with oxygen vacancies strongly localize, leading to a dramatic decrease in the electrical conductivity by several orders of magnitude. This unusual behavior is found to stem from the combination of crystal field splitting and filling-controlled Mott-Hubbard electron-electron correlations in the Ni 3<i>d</i> orbitals. Furthermore, we show the distribution of oxygen vacancies in NdNiO<sub>3</sub> can be controlled via an electric field, leading to analog resistance switching behavior. This study demonstrates the potential of nickelates as testbeds to better understand emergent physics in oxide heterostructures as well as candidate systems in the emerging fields of artificial intelligence. Perovskite (structure) ; Oxygen ; Materials science ; Condensed matter physics ; Business ; Physics ; Chemistry ; Crystallography ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells
Authors: Proserpio, D.M.
Source: Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (article, 2019-09-03)
Field: Materials Science — Materials Chemistry

Predicting superhard materials via a machine learning informed evolutionary structure search Abstract The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications. Herein, good agreement was found between experimental Vickers hardnesses, H v , of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from: (i) first principles via AFLOW-AEL (AFLOW Automatic Elastic Library), and (ii) a machine learning (ML) model trained on materials within the AFLOW repository. Because $$H_{\mathrm{v}}^{{\mathrm{ML}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>v</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ML</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> values can be quickly estimated, they can be used in conjunction with an evolutionary search to predict stable, superhard materials. This methodology is implemented in the X tal O pt evolutionary algorithm. Each crystal is minimized to the nearest local minimum, and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter. Both the energy/enthalpy and $$H_{{\mathrm{v,Teter}}}^{{\mathrm{ML}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>v,Teter</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ML</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> are employed to determine a structure’s fitness. This implementation is applied towards the carbon system, and 43 new superhard phases are found. A topological analysis reveals that phases estimated to be slightly harder than diamond contain a substantial fraction of diamond and/or lonsdaleite. Algorithm ; Machine learning ; Artificial intelligence ; Materials science ; Computer science ; Thermodynamics ; Physics Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics ; Advanced materials and composites
Authors: Harikrishna Sahu | Feng Yang | Xiaobo Ye | Jing Ma | Wei‐Hai Fang | Haibo Ma
Source: Journal of Materials Chemistry A (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Designing promising molecules for organic solar cells <i>via</i> machine learning assisted virtual screening Rational design of new OPV molecules <italic>via</italic> virtual screening of candidate materials using high-performing machine learning models. Virtual screening ; Organic molecules ; Computer science ; Machine learning ; Artificial intelligence ; Molecule ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Valerio Apicella | Carmine Stefano Clemente | Daniele Davino | Damiano Leone | C. Visone
Source: Actuators (article, 2019-05-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Review of Modeling and Control of Magnetostrictive Actuators Magnetostrictive actuators play an important role in the perception of usefulness of smart materials and devices. Their applications are potentially wider than that of piezoelectric actuators because of the higher energy density and intrinsic robustness. However, the non-negligible hysteresis and complexity of their characteristics make the design and control quite difficult and has limited their diffusion in industrial applications. Nevertheless, the scientific literature presents a wide offer of results in design and geometries, modeling and control that may be exploited for applications. This paper gives a reasoned review of the main results achieved in the literature about design, modeling and control of magnetostrictive actuators exploiting the direct effects of magnetostriction (Joule and Wiedemann). Some perspectives and challenges about magnetostrictive actuators development are also gathered. Magnetostriction ; Actuator ; Robustness (evolution) ; Computer science ; Control engineering ; Engineering ; Magnetic field ; Physics ; Artificial intelligence Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Shape Memory Alloy Transformations
Authors: Hanwen Wang | Mao-Lin Chen | Mengjian Zhu | Yaning Wang | Baojuan Dong | Xingdan Sun | Xiaorong Zhang | Shimin Cao | Xiaoxi Li | Jianqi Huang | Lei Zhang | Weilai Liu | Dongming Sun | Yu Ye | Kepeng Song | Jianjian Wang | Yu Han | Teng Yang | Huaihong Guo | Chengbing Qin | Liantuan Xiao | Jing Zhang | Jianhao Chen | Zheng Han | Zhidong Zhang
Source: Nature Communications (article, 2019-05-24)
Field: Materials Science — Materials Chemistry

Gate tunable giant anisotropic resistance in ultra-thin GaTe Anisotropy in crystals arises from different lattice periodicity along different crystallographic directions, and is usually more pronounced in two dimensional (2D) materials. Indeed, in the emerging 2D materials, electrical anisotropy has been one of the recent research focuses. However, key understandings of the in-plane anisotropic resistance in low-symmetry 2D materials, as well as demonstrations of model devices taking advantage of it, have proven difficult. Here, we show that, in few-layered semiconducting GaTe, electrical conductivity anisotropy between x and y directions of the 2D crystal can be gate tuned from several fold to over 10<sup>3</sup>. This effect is further demonstrated to yield an anisotropic non-volatile memory behavior in ultra-thin GaTe, when equipped with an architecture of van der Waals floating gate. Our findings of gate-tunable giant anisotropic resistance effect pave the way for potential applications in nanoelectronics such as multifunctional directional memories in the 2D limit. Anisotropy ; Nanoelectronics ; van der Waals force ; Lattice (music) ; Electrical resistivity and conductivity ; Yield (engineering) ; Crystal (programming language) 2D Materials and Applications ; Topological Materials and Phenomena ; Organic and Molecular Conductors Research
Authors: Hideyo Tsurusawa | Mathieu Leocmach | John Russo | Hajime Tanaka
Source: Science Advances (article, 2019-05-03)
Field: Materials Science — Materials Chemistry

Direct link between mechanical stability in gels and percolation of isostatic particles Colloidal gels have unique mechanical and transport properties that stem from their bicontinuous nature, in which a colloidal network is intertwined with a viscous solvent, and have found numerous applications in foods, cosmetics, and construction materials and for medical applications, such as cartilage replacements. So far, our understanding of the process of colloidal gelation is limited to long-time dynamical effects, where gelation is viewed as a phase separation process interrupted by the glass transition. However, this purely out-of-equilibrium thermodynamic picture does not address the emergence of mechanical stability. With confocal microscopy experiments, we reveal that mechanical metastability is reached only after isotropic percolation of locally isostatic environments, establishing a direct link between the load-bearing ability of gels and the isostaticity condition. Our work suggests an operative description of gels based on mechanical equilibrium and isostaticity, providing the physical basis for the stability and rheology of these materials. Rheology ; Percolation (cognitive psychology) ; Materials science ; Colloid ; Metastability ; Isotropy ; Percolation theory ; Phase (matter) ; Work (physics) ; Chemical physics ; Nanotechnology ; Composite material ; Chemical engineering ; Chemistry ; Thermodynamics ; Physics Material Dynamics and Properties ; Proteins in Food Systems ; Polysaccharides Composition and Applications
Authors: Nils J. Flodén | Aaron Trowbridge | Darren Willcox | Scarlett M. Walton | Yongjoon Kim | Matthew J. Gaunt
Source: Journal of the American Chemical Society (article, 2019-05-09)
Field: Chemistry — Organic Chemistry

Streamlined Synthesis of C(sp<sup>3</sup>)-Rich <i>N</i>-Heterospirocycles Enabled by Visible-Light-Mediated Photocatalysis We report a general visible-light-mediated strategy that enables the construction of complex C(sp<sup>3</sup>)-rich N-heterospirocycles from feedstock aliphatic ketones and aldehydes with a broad selection of alkene-containing secondary amines. Key to the success of this approach was the utilization of a highly reducing Ir-photocatalyst and orchestration of the intrinsic reactivities of 1,4-cyclohexadiene and Hantzsch ester. This methodology provides streamlined access to complex C(sp<sup>3</sup>)-rich N-heterospirocycles displaying structural and functional features relevant to fragment-based lead identification programs. Chemistry ; Photocatalysis ; Visible spectrum ; Orchestration ; Alkene ; Combinatorial chemistry ; Catalysis ; Stereochemistry ; Photochemistry ; Organic chemistry ; Optoelectronics Radical Photochemical Reactions ; Oxidative Organic Chemistry Reactions ; Sulfur-Based Synthesis Techniques
Authors: Zesheng Zhang | Yang Hong | Bo Hou | Zhongtao Zhang | Mehrdad Negahban | Jingchao Zhang
Source: Carbon (article, 2019-03-21)
Field: Materials Science — Materials Chemistry

Accelerated discoveries of mechanical properties of graphene using machine learning and high-throughput computation Graphene ; Artificial intelligence ; Stochastic gradient descent ; Machine learning ; Computation ; Artificial neural network ; Computer science ; Support vector machine ; Materials science ; Work (physics) ; Fracture (geology) ; Decision tree ; Algorithm ; Mechanical engineering ; Nanotechnology ; Composite material ; Engineering Graphene research and applications ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Jagannathan Madhavan | Jayaraman Theerthagiri | Dhandapani Balaji | S. Sunitha | Myong Yong Choi | Muthupandian Ashokkumar
Source: Molecules (review, 2019-09-13)
Field: Materials Science — Materials Chemistry

Hybrid Advanced Oxidation Processes Involving Ultrasound: An Overview <b>:</b> Sonochemical oxidation of organic pollutants in an aqueous environment is considered to be a green process. This mode of degradation of organic pollutants in an aqueous environment is considered to render reputable outcomes in terms of minimal chemical utilization and no need of extreme physical conditions. Indiscriminate discharge of toxic organic pollutants in an aqueous environment by anthropogenic activities has posed major health implications for both human and aquatic lives. Hence, numerous research endeavours are in progress to improve the efficiency of degradation and mineralization of organic contaminants. Being an extensively used advanced oxidation process, ultrasonic irradiation can be utilized for complete mineralization of persistent organic pollutants by coupling/integrating it with homogeneous and heterogeneous photocatalytic processes. In this regard, scientists have reported on sonophotocatalysis as an effective strategy towards the degradation of many toxic environmental pollutants. The combined effect of sonolysis and photocatalysis has been proved to enhance the production of high reactive-free radicals in aqueous medium which aid in the complete mineralization of organic pollutants. In this manuscript, we provide an overview on the ultrasound-based hybrid technologies for the degradation of organic pollutants in an aqueous environment. Ultrasound ; Chemistry ; Computer science ; Nanotechnology ; Materials science ; Medicine ; Radiology Ultrasound and Cavitation Phenomena ; Advanced oxidation water treatment ; Catalysis and Oxidation Reactions