jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicolas Duchemin | Adam Skiredj | Justine Mansot | Karine Leblanc | Jean‐Jacques Vasseur | Mehdi A. Beniddir | Laurent Evanno | Erwan Poupon | Michaël Smietana | Stellios Arseniyadis
Source: Angewandte Chemie International Edition (article, 2018-07-10)
Field: Chemistry — Organic Chemistry

DNA‐Templated [2+2] Photocycloaddition: A Straightforward Entry into the Aplysinopsin Family of Natural Products Biosynthetic considerations inspired us to harness the templating properties offered by DNA to promote a [2+2] photoinduced cycloaddition. The method was developed based on the dimerization of (E)-aplysinopsin, which was previously shown to be unproductive in solution. In sharp contrast, exposure of this tryptophan-derived olefin to light in the presence of salmon testes DNA (st-DNA) reproducibly afforded the corresponding homo-dimerized spiro-fused cyclobutane in excellent yields. DNA provides unique templating interactions enabling a singular mimic of the solid-state aggregation necessary for the [2+2] photocycloaddition to occur. This method was ultimately used to promote the prerequisite dimerizations leading to both dictazole B and tubastrindole B, thus constituting the first example of a DNA-mediated transformation to be applied to the total synthesis of a natural product. Natural (archaeology) ; DNA ; Chemistry ; Combinatorial chemistry ; Biology ; Biochemistry Radical Photochemical Reactions ; Synthetic Organic Chemistry Methods ; Chemical Synthesis and Analysis
Authors: Wilfredo Ibarra-Hernández | Samad Hajinazar | Guillermo Avendaño-Franco | A. Bautista Hernández | Aleksey N. Kolmogorov | A. Romero
Source: Physical Chemistry Chemical Physics (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Structural search for stable Mg–Ca alloys accelerated with a neural network interatomic model We have combined a neural network formalism with metaheuristic structural global search algorithms to systematically screen the Mg-Ca binary system for new (meta)stable alloys. The combination of these methods allows for an efficient exploration of the potential energy surface beyond the possibility of the traditional searches based on ab initio energy evaluations. The identified pool of low-enthalpy structures was complemented with special quasirandom structures (SQS) at different stoichiometries. In addition to the only Mg-Ca phase known to form under standard synthesis conditions, C14-Mg2Ca, the search has uncovered several candidate materials that could be synthesized under elevated temperatures or pressures. We show that the vibrational entropy lowers the relative free energy of several phases with magnesium kagome layers: C15 and C36 Laves structures at the 2 : 1 composition and an orthorhombic oS36 structure at the 7 : 2 composition. The estimated phase transition temperatures close to the melting point leave open the possibility of synthesizing the predicted materials at high temperatures. At high pressures up to 10 GPa, two new phases at the 1 : 1 and 3 : 1 Mg : Ca stoichiometries become thermodynamically stable and should form in multi-anvil experiments. Artificial neural network ; Interatomic potential ; Materials science ; Network structure ; Molecular dynamics ; Statistical physics ; Computer science ; Biological system ; Chemistry ; Physics ; Computational chemistry ; Artificial intelligence ; Machine learning ; Biology Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics ; Rare-earth and actinide compounds
Authors: Weike Ye | Chi Chen | Shyam Dwaraknath | Anubhav Jain | Shyue Ping Ong | Kristin A. Persson
Source: MRS Bulletin (article, 2018-09-01)
Field: Materials Science — Materials Chemistry

Harnessing the Materials Project for machine-learning and accelerated discovery Computer science ; Nanotechnology ; Data science ; Materials science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: John E. Herr | Kevin J. Koh | Kun Yao | John Parkhill
Source: arXiv (Cornell University) (article, 2018-10-31)
Field: Materials Science — Materials Chemistry

Compressing physical properties of atomic species for improving predictive chemistry The answers to many unsolved problems lie in the intractable chemical space of molecules and materials. Machine learning techniques are rapidly growing in popularity as a way to compress and explore chemical space efficiently. One of the most important aspects of machine learning techniques is representation through the feature vector, which should contain the most important descriptors necessary to make accurate predictions, not least of which is the atomic species in the molecule or material. In this work we introduce a compressed representation of physical properties for atomic species we call the elemental modes. The elemental modes provide an excellent representation by capturing many of the nuances of the periodic table and the similarity of atomic species. We apply the elemental modes to several different tasks for machine learning algorithms and show that they enable us to make improvements to these tasks even beyond simply achieving higher accuracy predictions. Representation (politics) ; Autoencoder ; Chemical space ; Space (punctuation) ; Computer science ; Artificial intelligence ; Machine learning ; Popularity ; Table (database) ; Feature vector ; Theoretical computer science ; Data mining ; Chemistry ; Deep learning ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Gowoon Cheon | Ekin D. Cubuk | Evan R. Antoniuk | Lavi Blumberg | Joshua E. Goldberger | Evan J. Reed
Source: The Journal of Physical Chemistry Letters (article, 2018-11-27)
Field: Materials Science — Materials Chemistry

Revealing the Spectrum of Unknown Layered Materials with Superhuman Predictive Abilities We discover the chemical composition of over 1000 materials that are likely to exhibit layered and 2D phases but have yet to be synthesized. This includes two materials our calculations indicate can exist in distinct structures with different band gaps, expanding the short list of 2D phase-change materials. Whereas databases of over 1000 layered materials have been reported, we provide the first full database of materials that are likely layered but are yet to be synthesized, providing a roadmap for the synthesis community. We accomplish this by combining physics with machine learning on experimentally obtained data and verify a subset of candidates using density functional theory. We find that our model performs five times better than practitioners in the field at identifying layered materials and is comparable to or better than professional solid-state chemists. Finally, we find that semisupervised learning can offer benefits for materials design where labels for some of the materials are unknown. Field (mathematics) ; Computer science ; Double layered ; Artificial intelligence ; Materials science ; Mathematics Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: April M. Cooper | Philipp P. Hallmen | Johannes Kästner
Source: The Journal of Chemical Physics (article, 2018-03-01)
Field: Materials Science — Materials Chemistry

Potential energy surface interpolation with neural networks for instanton rate calculations Artificial neural networks are used to fit a potential energy surface (PES). We demonstrate the benefits of using not only energies but also their first and second derivatives as training data for the neural network. This ensures smooth and accurate Hessian surfaces, which are required for rate constant calculations using instanton theory. Our aim was a local, accurate fit rather than a global PES because instanton theory requires information on the potential only in the close vicinity of the main tunneling path. Elongations along vibrational normal modes at the transition state are used as coordinates for the neural network. The method is applied to the hydrogen abstraction reaction from methanol, calculated on a coupled-cluster level of theory. The reaction is essential in astrochemistry to explain the deuteration of methanol in the interstellar medium. Potential energy surface ; Instanton ; Artificial neural network ; Interpolation (computer graphics) ; Hessian matrix ; Potential energy ; Surface (topology) ; Quantum tunnelling Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Ivan Kopal | Ivan Labaj | Marta Harničárová | Ján Valíček | D. Hrubý
Source: Polymers (article, 2018-06-09)
Field: Materials Science — Polymers and Plastics

Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network The precise experimental estimation of mechanical properties of rubber blends can be a very costly and time-consuming process. The present work explores the possibilities of increasing its efficiency by using artificial neural networks to study the mechanical behavior of these widely used materials. A multilayer feed-forward back-propagation artificial neural network model, with a strain and the carbon black content as input parameters and stress as an output parameter, has been developed to predict the uniaxial tensile response of vulcanized natural rubber blends with different contents of carbon black in the form of engineering stress-strain curves. A novel procedure has been created for the simulation of the optimized artificial neural network model with input datasets generated by a regression model of an experimental dependence of tensile strain-at-break on the carbon black content in the investigated blends. Errors of the prediction of experimental stress-strain curves, as well as of tensile strain-at-break, tensile stress-at-break and M100 tensile modulus were estimated for all simulated stress-strain curves. The present study demonstrated that the performance of a developed neural network model to predict the stress-strain curves of rubber blends with different contents of carbon black is also exceptionally high in the case of a network that had never learned the input data, which makes it a suitable tool for extensive use in practice. Carbon black ; Materials science ; Ultimate tensile strength ; Natural rubber ; Vulcanization ; Composite material ; Artificial neural network ; Stress (linguistics) ; Modulus ; Stress–strain curve ; Biological system ; Deformation (meteorology) ; Computer science ; Artificial intelligence Polymer Nanocomposites and Properties ; Elasticity and Material Modeling ; Polymer crystallization and properties
Authors: Anna Theresa Cavasin | Alexander Hillisch | Felix Uellendahl | Sebastian Schneckener | Andreas H. Göller
Source: Journal of Chemical Information and Modeling (article, 2018-05-02)
Field: Materials Science — Materials Chemistry

Reliable and Performant Identification of Low-Energy Conformers in the Gas Phase and Water Prediction of compound properties from structure via quantitative structure-activity relationship and machine-learning approaches is an important computational chemistry task in small-molecule drug research. Though many such properties are dependent on three-dimensional structures or even conformer ensembles, the majority of models are based on descriptors derived from two-dimensional structures. Here we present results from a thorough benchmark study of force field, semiempirical, and density functional methods for the calculation of conformer energies in the gas phase and water solvation as a foundation for the correct identification of relevant low-energy conformers. We find that the tight-binding ansatz GFN-xTB shows the lowest error metrics and highest correlation to the benchmark PBE0-D3(BJ)/def2-TZVP in the gas phase for the computationally fast methods and that in solvent OPLS3 becomes comparable in performance. MMFF94, AM1, and DFTB+ perform worse, whereas the performance-optimized but far more expensive functional PBEh-3c yields energies almost perfectly correlated to the benchmark and should be used whenever affordable. On the basis of our findings, we have implemented a reliable and fast protocol for the identification of low-energy conformers of drug-like molecules in water that can be used for the quantification of strain energy and entropy contributions to target binding as well as for the derivation of conformer-ensemble-dependent molecular descriptors. Conformational isomerism ; Solvation ; Benchmark (surveying) ; Ansatz ; Maxima and minima ; Statistical physics ; Force field (fiction) ; Computational chemistry ; Density functional theory ; Gas phase ; Computer science ; Molecule ; Chemistry ; Physics ; Thermodynamics ; Mathematics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Sean M. Collins | Demie Kepaptsoglou | Keith T. Butler | Louis Longley | Thomas D. Bennett | Quentin M. Ramasse | Paul A. Midgley
Source: Journal of the American Chemical Society (article, 2018-12-07)
Field: Chemistry — Inorganic Chemistry

Subwavelength Spatially Resolved Coordination Chemistry of Metal–Organic Framework Glass Blends Microstructured metal-organic framework (MOF) glasses have been produced by combining two amorphous MOFs. However, the electronic structure of these materials has not been interrogated at the length scales of the chemical domains formed in these glasses. Here, we report a subwavelength spatially resolved physicochemical analysis of the electronic states at visible and UV energies in a blend of two zeolitic imidazolate frameworks (ZIFs), ZIF-4-Co and ZIF-62-Zn. By combining spectroscopy at visible and UV energies as well as at core ionization energies in electron energy loss spectroscopy in the scanning transmission electron microscope with density functional theory calculations, we show that domains less than 200 nm in size retain the electronic structure of the precursor crystalline ZIF phases. Prototypical signatures of coordination chemistry including d- d transitions in ZIF-4-Co are assigned and mapped with nanoscale precision. Chemistry ; Amorphous solid ; Zeolitic imidazolate framework ; Electronic structure ; Spectroscopy ; Nanoscopic scale ; Metal-organic framework ; Chemical physics ; Transmission electron microscopy ; Scanning transmission electron microscopy ; Coordination complex ; Ionization energy ; Imidazolate ; Electron energy loss spectroscopy ; Density functional theory ; Nanotechnology ; Ionization ; Metal ; Crystallography ; Computational chemistry ; Physical chemistry ; Inorganic chemistry ; Materials science ; Organic chemistry ; Ion Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes
Authors: Louise Summerton | Glenn A. Hurst | James H. Clark
Source: Current Opinion in Green and Sustainable Chemistry (article, 2018-04-06)
Field: Chemistry — Physical and Theoretical Chemistry

Facilitating active learning within green chemistry Active learning (machine learning) ; Scope (computer science) ; Subject matter ; Computer science ; Mathematics education ; Chemistry ; Artificial intelligence ; Psychology ; Pedagogy ; Curriculum Various Chemistry Research Topics ; Chemistry and Chemical Engineering ; Problem and Project Based Learning
Authors: Yang Xu | Haiyue Yang | Deli Zang | Yan Zhou | Feng Liu | Xiaochen Huang | Jo‐Shu Chang | Chengyu Wang | Shih‐Hsin Ho
Source: Bioresources and Bioprocessing (article, 2018-02-20)
Field: Materials Science — Surfaces, Coatings and Films

Preparation of a new superhydrophobic/superoleophilic corn straw fiber used as an oil absorbent for selective absorption of oil from water Oil leakages frequently occur during oil product development and oil transportation. These incidents are a vital factor in water contamination, thus leading to serious environmental destruction. Therefore, superhydrophobic/superoleophilic material is one of the solutions to treat oily wastewater. This study aimed to develop a simple, fast and low-cost method to treat oily wastewater by synthesizing a new superhydrophobic/superoleophilic corn straw fiber via conventional impregnation. The corresponding results illustrate that abundant homogeneous silica (SiO2) granules evenly accreted on the surface of the prepared fiber were conducive to high surface roughness. Meanwhile, (Heptadecafluoro-1,1,2,2-tetradecyl) trimethoxysilane, a sort of silane coupling agent, could greatly reduce surface free energy by grafting with SiO2 particles on the corn straw fiber surface. The obtained superhydrophobic/superoleophilic corn straw fiber exhibited a water contact angle of 152° and an oil contact angle of 0° for various oils, strongly demonstrating its considerable application as an oil absorbent that can be applied for oil cleanup. In addition, the prepared fiber displayed a great chemical stability and environmental durability. Due to its high absorption capacity and absorption efficiency, the prepared fiber has great potential as a new oil absorbent for treatment of oily water. Materials science ; Contact angle ; Fiber ; Water repellent ; Absorption (acoustics) ; Durability ; Chemical engineering ; Composite material Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Adhesion, Friction, and Surface Interactions
Authors: Stuart P. Atkinson | Zoraida Andreu | Marı́a J. Vicent
Source: Journal of Personalized Medicine (review, 2018-01-23)
Field: Materials Science — Biomaterials

Polymer Therapeutics: Biomarkers and New Approaches for Personalized Cancer Treatment Polymer therapeutics (PTs) provides a potentially exciting approach for the treatment of many diseases by enhancing aqueous solubility and altering drug pharmacokinetics at both the whole organism and subcellular level leading to improved therapeutic outcomes. However, the failure of many polymer-drug conjugates in clinical trials suggests that we may need to stratify patients in order to match each patient to the right PT. In this concise review, we hope to assess potential PT-specific biomarkers for cancer treatment, with a focus on new studies, detection methods, new models and the opportunities this knowledge will bring for the development of novel PT-based anti-cancer strategies. We discuss the various "hurdles" that a given PT faces on its passage from the syringe to the tumor (and beyond), including the passage through the bloodstream, tumor targeting, tumor uptake and the intracellular release of the active agent. However, we also discuss other relevant concepts and new considerations in the field, which we hope will provide new insight into the possible applications of PT-related biomarkers. Medicine ; Personalized medicine ; Cancer ; Bioinformatics ; Computational biology ; Internal medicine ; Biology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: S. Sundjono | Gadang Priyotomo | Lutviasari Nuraini | Siska Prifiharni
Source: Journal of Engineering and Technological Sciences (article, 2018-02-01)
Field: Materials Science — Materials Chemistry

Corrosion Behavior of Mild Steel in Seawater from Northern Coast of Java and Southern Coast of Bali, Indonesia The corrosion behavior of mild steel in seawater was investigated using an immersion test and energy-dispersive X-ray spectroscopy in solutions from the sites of Muara Baru, Suramadu and Tol Mandara. As reference, solutions of 3.5% and 5% NaCl were prepared. Dissolved oxygen, salinity, conductivity, and total dissolved solids (TDS) were measured. It was found that uniform corrosion occurred on the mild steel during the immersion test. The corrosion resistance of the steel decreased with increasing exposure time. Since the magnitudes of conductivity, salinity and TDS of all test solutions were similar, it was concluded that dissolved oxygen (DO) and chloride ions play an essential role in inducing the corrosion risk of mild steel. Both parameters hinder the formation of a stable passive film on the surface of corroded mild steel. Corrosion ; Seawater ; Salinity ; Chloride ; Metallurgy ; Immersion (mathematics) ; Materials science ; Conductivity ; Oxygen ; Total dissolved solids ; Environmental science ; Geology ; Chemistry ; Environmental engineering ; Oceanography Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Concrete Corrosion and Durability
Authors: Hendrik Goßler | Lubow Maier | Sofia Angeli | Steffen Tischer | Olaf Deutschmann
Source: Physical Chemistry Chemical Physics (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

CaRMeN: a tool for analysing and deriving kinetics in the real world This paper presents the concepts of an open software tool (CaRMeN) that can be used to rapidly analyse and derive models, in particular chemical kinetics. The software automates the workflow of comparing model vs. experiment, which must currently be done manually and is thus a time-consuming and error-prone task. The capabilities of the software are illustrated through a case study. Experimental data for the conversion of methane over rhodium catalysts in a wide range of conditions and experimental setups are numerically simulated using five different mechanisms from the literature. The applicability of the mechanisms as well as differences between flow and diffusion models are evaluated. The results show that no single mechanism reliably predicts the chemical conversions of all of the experiments. Although the software was initially developed for chemical kinetics applications, it can also be extended to run any simulation code, and can therefore be applied in other scenarios. Kinetics ; Computer science ; Chemistry ; Physics ; Classical mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Jingjing Chen | Lijuan Wang | Guiju Xu | Xia Wang | Ru‐Song Zhao
Source: Molecules (article, 2018-04-18)
Field: Chemistry — Inorganic Chemistry

Highly Stable Zr(IV)-Based Porphyrinic Metal−Organic Frameworks as an Adsorbent for the Effective Removal of Gatifloxacin from Aqueous Solution Water stable Zr-metal−organic framework nanoparticles (PCN-224 NPs, PCN refers to porous coordination network) have been solvothermally synthesized. PCN-224 NPs show spherical shape with smooth surface and particle size of approximately 200 nm. PCN-224 NPs can be stable in acid and aqueous solutions, as confirmed by powder X-ray diffraction. Gatifloxacin (GTF) adsorption measurements showed that PCN-224 NPs exhibit a high adsorption capacity of 876 mg·g<sup>−1</sup>. Meanwhile, the adsorption factors, adsorption characteristics, and mechanisms of GTF were investigated in batch adsorption experiments. Adsorption ; Aqueous solution ; Metal-organic framework ; Gatifloxacin ; Nanoparticle ; Chemistry ; Metal ; Porosity ; Chemical engineering ; Inorganic chemistry ; Materials science ; Organic chemistry ; Nanotechnology Metal-Organic Frameworks: Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Covalent Organic Framework Applications