jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Justin J. Dressler | Michael M. Haley
Source: Journal of Physical Organic Chemistry (article, 2020-07-29)
Field: Chemistry — Organic Chemistry

Learning how to fine‐tune diradical properties by structure refinement Abstract The literature has seen a large increase in the number of new carbon‐based organic diradicals/diradicaloids in recent years. While a plethora of new and exciting structures have been created, there seemingly is a gap in knowledge of what fundamental electronic parameters are in play and thus how to rationally manipulate said parameters to “fine tune” the resultant diradical properties. Since 2014, the Haley group has been exploring methods to systematically alter the diradical character and the singlet‐triplet energy gap in said class of molecules. Our entrance into organic diradicals began with the π‐expansion of the benzene core of indeno[1,2‐ b ]fluorene up to the anthracene core of diindenoanthracene (DIAn). DIAn possessed moderate diradical character ( y = 0.62) with a surprising level of stability (more than 2 months in solution). From this molecular blueprint for producing stable diradicals, the Haley lab has investigated how to fine tune diradical properties via structural changes in two key positions: (a) the length of the acene core and (b) thoughtful exchange of the outer arenes. With this strategy at our disposal, we can make large scale changes to the diradical character index and singlet‐triplet energy gap through changing the core length, and these properties can be further fine‐tuned in a series of closely related diradicals by careful exchange of the outer arenes utilizing the straightforward methods described in this mini‐review. Diradical ; Chemistry ; Singlet state ; Acene ; Chemical physics ; Antiaromaticity ; Molecule ; Computational chemistry ; Organic chemistry ; Aromaticity ; Atomic physics ; Excited state ; Physics Synthesis and Properties of Aromatic Compounds ; Fullerene Chemistry and Applications ; Axial and Atropisomeric Chirality Synthesis
Authors: Tomáš Lednický | Attila Bonyár
Source: ACS Applied Materials & Interfaces (article, 2020-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Large Scale Fabrication of Ordered Gold Nanoparticle–Epoxy Surface Nanocomposites and Their Application as Label-Free Plasmonic DNA Biosensors A robust and scalable technology to fabricate ordered gold nanoparticle arrangements on epoxy substrates is presented. The nanoparticles are synthesized by solid-state dewetting on nanobowled aluminum templates, which are prepared by the selective chemical etching of porous anodic alumina (PAA) grown on an aluminum sheet with controlled anodic oxidation. This flexible fabrication technology provides proper control over the nanoparticle size, shape, and interparticle distance over a large surface area (several cm<sup>2</sup>), which enables the fine-tuning and optimization of their plasmonic absorption spectra for LSPR and SERS applications between 535 and 625 nm. The nanoparticles are transferred to the surface of epoxy substrates, which are subsequently selectively etched. The resulting nanomushrooms arrangements consist of ordered epoxy nanopillars with flat, disk-shaped nanoparticles on top, and their bulk refractive index sensitivity is between 83 and 108 nm RIU<sup>-1</sup>. Label-free DNA detection is successfully demonstrated with the sensors by using a 20 base pair long specific DNA sequence from the parasite <i>Giardia lamblia</i>. A red-shift of 6.6 nm in the LSPR absorbance spectrum was detected after the 2 h hybridization with 1 μM target DNA, and the achievable LOD was around 5 nM. The reported plasmonic sensor is one of the first surface AuNP/polymer nanocomposites ever reported for the successful label-free detection of DNA. Materials science ; Nanotechnology ; Colloidal gold ; Nanoparticle ; Surface plasmon resonance ; Plasmon ; Biosensor ; Nanocomposite ; Fabrication ; Epoxy ; Optoelectronics ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Anodic Oxide Films and Nanostructures ; Advanced biosensing and bioanalysis techniques
Authors: Shengdong Wang | Guillaume Force | Régis Guillot | Jean‐François Carpentier | Yann Sarazin | Christophe Bour | Vincent Gandon | David Lebœuf
Source: ACS Catalysis (article, 2020-08-26)
Field: Chemistry — Organic Chemistry

Lewis Acid/Hexafluoroisopropanol: A Promoter System for Selective <i>ortho</i>-C-Alkylation of Anilines with Deactivated Styrene Derivatives and Unactivated Alkenes Aniline derivatives are frequently encountered in molecules of industrial relevance such as dyes or antioxidants, which make the development of synthetic methods for the functionalization of these privileged structures highly sought-after. A general protocol for the hydroarylation of electronically diverse alkenes with anilines would be ideal to provide densely functionalized compounds. However, this transformation has been underexplored compared to more traditional hydroarylation of unactivated alkenes because of the significant challenges associated with the control of the selectivity and its substrate tolerance. Herein, we describe a selective, versatile, and user-friendly ortho-C-alkylation of anilines with alkenes that hinges on the beneficial combination of a Lewis acid (Ca(II)) and hexafluoroisopropanol as a solvent. This protocol allows for the extension of this transformation to highly deactivated styrenes and demonstrates a remarkable improved reactivity regarding aliphatic alkenes, styrene derivatives, and dienes. In addition, DFT computations were performed which, combined with experimental observations, suggest a nearly concerted mechanism that impart the ortho-selectivity. Aniline ; Selectivity ; Chemistry ; Styrene ; Reactivity (psychology) ; Lewis acids and bases ; Combinatorial chemistry ; Alkylation ; Substrate (aquarium) ; Catalysis ; Organic chemistry ; Copolymer ; Polymer Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Synthesis and Catalytic Reactions
Authors: Sun-Ting Tsai | En-Jui Kuo | Pratyush Tiwary
Source: Nature Communications (article, 2020-10-09)
Field: Materials Science — Materials Chemistry

Learning molecular dynamics with simple language model built upon long short-term memory neural network Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysical trajectories. Our character-level language model learns a probabilistic model of 1-dimensional stochastic trajectories generated from higher-dimensional dynamics. The model captures Boltzmann statistics and also reproduces kinetics across a spectrum of timescales. We demonstrate how training the long short-term memory network is equivalent to learning a path entropy, and that its embedding layer, instead of representing contextual meaning of characters, here exhibits a nontrivial connectivity between different metastable states in the underlying physical system. We demonstrate our model's reliability through different benchmark systems and a force spectroscopy trajectory for multi-state riboswitch. We anticipate that our work represents a stepping stone in the understanding and use of recurrent neural networks for understanding the dynamics of complex stochastic molecular systems. Recurrent neural network ; Boltzmann machine ; Probabilistic logic ; Language model ; Benchmark (surveying) ; Embedding ; Trajectory ; Artificial neural network ; Path (computing) Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Yuanbin Liu | Jia‐Yue Yang | Gongming Xin | Linhua Liu | Gábor Cśanyi | Bing Cao
Source: The Journal of Chemical Physics (article, 2020-10-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga2O3 The thermal properties of β-Ga<sub>2</sub>O<sub>3</sub> can significantly affect the performance and reliability of high-power electronic devices. To date, due to the absence of a reliable interatomic potential, first-principles calculations based on density functional theory (DFT) have been routinely used to probe the thermal properties of β-Ga<sub>2</sub>O<sub>3</sub>. DFT calculations can only tackle small-scale systems due to the huge computational cost, while the thermal transport processes are usually associated with large time and length scales. In this work, we develop a machine learning based Gaussian approximation potential (GAP) for accurately describing the lattice dynamics of perfect crystalline β-Ga<sub>2</sub>O<sub>3</sub> and accelerating atomic-scale simulations. The GAP model shows excellent convergence, which can faithfully reproduce the DFT potential energy surface at a training data size of 32 000 local atomic environments. The GAP model is then used to predict ground-state lattice parameters, coefficients of thermal expansion, heat capacity, phonon dispersions at 0 K, and anisotropic thermal conductivity of β-Ga<sub>2</sub>O<sub>3</sub>, which are all in excellent agreement with either the DFT results or experiments. The accurate predictions of phonon dispersions and thermal conductivities demonstrate that the GAP model can well describe the harmonic and anharmonic interactions of phonons. Additionally, the successful application of our GAP model to the phonon density of states of a 2500-atom β-Ga<sub>2</sub>O<sub>3</sub> structure at elevated temperature indicates the strength of machine learning potentials to tackle large-scale atomic systems in long molecular simulations, which would be almost impossible to generate with DFT-based molecular simulations at present. Phonon ; Anharmonicity ; Density functional theory ; Interatomic potential ; Molecular dynamics ; Thermal conductivity ; Atomic units ; Lattice (music) ; Band gap ; Statistical physics ; Materials science ; Thermal ; Condensed matter physics ; Embedded atom model ; Physics ; Thermodynamics ; Quantum mechanics Ga2O3 and related materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Tien-Thinh Le
Source: Journal of Composite Materials (article, 2020-09-17)
Field: Materials Science — Materials Chemistry

Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method This paper is devoted to the development and construction of a practical Machine Learning (ML)-based model for the prediction of tensile strength of polymer carbon nanotube (CNTs) composites. To this end, a database was compiled from the available literature, composed of 11 input variables. The input variables for predicting tensile strength of nanocomposites were selected for the following main reasons: (i) type of polymer matrix, (ii) mechanical properties of polymer matrix, (iii) physical characteristics of CNTs, (iv) mechanical properties of CNTs and (v) incorporation parameters such as CNT weight fraction, CNT surface modification method and processing method. As the problem of prediction is highly dimensional (with 11 dimensions), the Gaussian Process Regression (GPR) model was selected and optimized by means of a parametric study. The correlation coefficient (R), Willmott’s index of agreement (IA), slope of regression, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were employed as error measurement criteria when training the GPR model. The GPR model exhibited good performance for both training and testing parts (RMSE = 5.982 and 5.327 MPa, MAE = 3.447 and 3.539 MPa, respectively). In addition, uncertainty analysis was also applied to estimate the prediction confidence intervals. Finally, the prediction capability of the GPR model with different ranges of values of input variables was investigated and discussed. For practical application, a Graphical User Interface (GUI) was developed in Matlab for predicting the tensile strength of nanocomposites. Ultimate tensile strength ; Materials science ; Mean squared error ; Composite material ; Mean absolute percentage error ; Carbon nanotube ; Coefficient of determination ; Computer science ; Mathematics ; Machine learning ; Statistics Carbon Nanotubes in Composites ; Smart Materials for Construction ; Fuel Cells and Related Materials
Authors: Ralf Meyer | Manuel Weichselbaum | Andreas Hauser
Source: Journal of Chemical Theory and Computation (article, 2020-08-10)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches toward Orbital-free Density Functional Theory: Simultaneous Training on the Kinetic Energy Density Functional and Its Functional Derivative Orbital-free approaches might offer a way to boost the applicability of density functional theory by orders of magnitude in system size. An important ingredient for this endeavor is the kinetic energy density functional. Snyder et al. [ <i>Phys. Rev. Lett.</i> 2012, 108, 253002] presented a machine learning approximation for this functional achieving chemical accuracy on a one-dimensional model system. However, a poor performance with respect to the functional derivative, a crucial element in iterative energy minimization procedures, enforced the application of a computationally expensive projection method. In this work we circumvent this issue by including the functional derivative into the training of various machine learning models. Besides kernel ridge regression, the original method of choice, we also test the performance of convolutional neural network techniques borrowed from the field of image recognition. Density functional theory ; Computer science ; Energy functional ; Functional derivative ; Orbital-free density functional theory ; Convolutional neural network ; Time-dependent density functional theory ; Kernel (algebra) ; Minification ; Energy minimization ; Hybrid functional ; Artificial intelligence ; Derivative (finance) ; Machine learning ; Projection (relational algebra) ; Energy (signal processing) ; Mathematical optimization ; Algorithm ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Jiasheng Huang | Janet Liew | A.S. Ademiloye | K.M. Liew
Source: Archives of Computational Methods in Engineering (article, 2020-10-11)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Materials Modeling and Design Computer science ; Field (mathematics) ; Artificial intelligence ; Principal (computer security) ; Deep learning ; Machine learning Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Wei Li | Craig Burkhart | Patrycja Polińska | Vagelis Harmandaris | Manolis Doxastakis
Source: The Journal of Chemical Physics (article, 2020-07-27)
Field: Materials Science — Materials Chemistry

Backmapping coarse-grained macromolecules: An efficient and versatile machine learning approach Multiscale modeling of polymers exchanges information between coarse and fine representations of molecules to capture material properties over a wide range of spatial and temporal scales. Restoring details at a finer scale requires us to generate information following embedded physics and statistics of the models at two different levels of description. Techniques designed to address this persistent challenge balance among accuracy, efficiency, and general applicability. In this work, we present an image-based approach for structural backmapping from coarse-grained to atomistic models with cis-1,4 polyisoprene melts as an illustrative example. Through machine learning, we train conditional generative adversarial networks on the correspondence between configurations at the levels considered. The trained model is subsequently applied to provide predictions of atomistic structures from the input coarse-grained configurations. The effect of different data representation schemes on training and prediction quality is examined. Our proposed backmapping approach shows remarkable efficiency and transferability over different molecular weights in the melt based on training sets constructed from oligomeric compounds. We anticipate that this versatile backmapping approach can be readily extended to other complex systems to provide high-fidelity initial configurations with minimal human intervention. Computer science ; Representation (politics) ; Fidelity ; Benchmark (surveying) ; Transferability ; Range (aeronautics) ; Generative grammar ; Scale (ratio) ; Machine learning ; Artificial intelligence ; Theoretical computer science ; Materials science Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Alejandro García‐Miranda Ferrari | Samuel J. Rowley‐Neale | Craig E. Banks
Source: Analytical and Bioanalytical Chemistry (review, 2020-12-07)
Field: Materials Science — Materials Chemistry

Recent advances in 2D hexagonal boron nitride (2D-hBN) applied as the basis of electrochemical sensing platforms 2D hexagonal boron nitride (2D-hBN) is a lesser utilised material than other 2D counterparts in electrochemistry due to initial reports of it being non-conductive. As we will demonstrate in this review, this common misconception is being challenged, and researchers are starting to utilise 2D-hBN in the field of electrochemistry, particularly as the basis of electroanalytical sensing platforms. In this critical review, we overview the use of 2D-hBN as an electroanalytical sensing platform summarising recent developments and trends and highlight future developments of this interesting, often overlooked, 2D material. Hexagonal boron nitride ; Nanotechnology ; Electrochemistry ; Boron nitride ; Materials science ; Chemistry ; Electrode ; Graphene Graphene research and applications ; 2D Materials and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Julius Hillenbrand | Markus Leutzsch | Ektoras Yiannakas | Christopher P. Gordon | Christian Wille | Nils Nöthling | Christophe Copéret | Alois Fürstner
Source: Journal of the American Chemical Society (article, 2020-05-28)
Field: Chemistry — Organic Chemistry

“Canopy Catalysts” for Alkyne Metathesis: Molybdenum Alkylidyne Complexes with a Tripodal Ligand Framework A new family of structurally well-defined molybdenum alkylidyne catalysts for alkyne metathesis, which is distinguished by a tripodal trisilanolate ligand architecture, is presented. Complexes of type <b>1</b> combine the virtues of previous generations of silanolate-based catalysts with a significantly improved functional group tolerance. They are easy to prepare on scale; the modularity of the ligand synthesis allows the steric and electronic properties to be fine-tuned and hence the application profile of the catalysts to be optimized. This opportunity is manifested in the development of catalyst <b>1f</b>, which is as reactive as the best ancestors but exhibits an unrivaled scope. The new catalysts work well in the presence of unprotected alcohols and various other protic groups. The chelate effect entails even a certain stability toward water, which marks a big leap forward in metal alkylidyne chemistry in general. At the same time, they tolerate many donor sites, including basic nitrogen and numerous heterocycles. This aspect is substantiated by applications to polyfunctional (natural) products. A combined spectroscopic, crystallographic, and computational study provides insights into structure and electronic character of complexes of type <b>1</b>. Particularly informative are a density functional theory (DFT)-based chemical shift tensor analysis of the alkylidyne carbon atom and <sup>95</sup>Mo NMR spectroscopy; this analytical tool had been rarely used in organometallic chemistry before but turns out to be a sensitive probe that deserves more attention. The data show that the podand ligands render a Mo-alkylidyne a priori more electrophilic than analogous monodentate triarylsilanols; proper ligand tuning, however, allows the Lewis acidity as well as the steric demand about the central atom to be adjusted to the point that excellent performance of the catalyst is ensured. Chemistry ; Ligand (biochemistry) ; Electrophile ; Alkyne ; Steric effects ; Catalysis ; Metathesis ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry Synthetic Organic Chemistry Methods ; Organometallic Complex Synthesis and Catalysis ; Catalytic Alkyne Reactions
Authors: José Manuel Ramos-Villaseñor | Esdrey Rodríguez-Cárdenas | Carlos Barrera-Díaz | Bernardo A. Frontana‐Uribe
Source: Journal of The Electrochemical Society (article, 2020-01-12)
Field: Chemistry — Organic Chemistry

Review—Use of 1,1,1,3,3,3–hexafluoro–2–propanol (HFIP) Co-Solvent Mixtures in Organic Electrosynthesis Because of the necessity of carry out electrolysis reactions with considerable quantity of organic molecules, the balance between solubility of starting material, solution conductivity and electrochemical stability of medium and intermediates are key factors in organic electrosynthesis. HFIP has several properties that favor its use in this research area as solvent, among them, its high hydrogen-bond donor has opened the possibility of fine tuning reactivity, mainly in anodic reactions because of the helpful effect on the stability of positive intermediates. The cost of this solvent has limited its broad application in chemistry, including electrosynthesis, but the possibility of using mixtures with other cosolvents has demonstrated to help to expand its use without losing the beneficial effect on the intermediates. In recent years several HFIP mixtures (HFIP/MeOH, HFIP/CH 2 Cl 2 , HFIP/H 2 O, HFIP/ACN, HFIP/MeNO 2 ) have permitted the control the chemical microstructure of the electrolysis media and have let to adjust the solvent properties to fulfill the necessity of electrosynthesis. In this review will be discussed the general properties of HFIP and the mixtures reported to carry out electrochemical synthetic transformations of organic molecules, as well as the reactions where has been demonstrated the beneficial effect of HFIP solvent mixtures in the control of the electrogenerated intermediates. This approach has succeeded in organic electrosynthesis. Electrosynthesis ; Chemistry ; Electrolysis ; Solvent ; Electrochemistry ; Solubility ; Reactivity (psychology) ; Acetonitrile ; Inorganic chemistry ; Organic chemistry ; Electrode ; Physical chemistry Oxidative Organic Chemistry Reactions ; Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques