jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew L. Kocen | Maurice Brookhart | Olafs Daugulis
Source: Nature Communications (article, 2019-01-25)
Field: Chemistry — Organic Chemistry

A highly active Ni(II)-triadamantylphosphine catalyst for ultrahigh-molecular-weight polyethylene synthesis Here we report that the tri-1-adamantylphosphine-nickel complex [Ad<sub>3</sub>PNiBr<sub>3</sub>]<sup>-</sup>[Ad<sub>3</sub>PH]<sup>+</sup> upon activation with an alkylaluminoxane catalyzes the polymerization of ethylene to ultrahigh-molecular-weight, nearly linear polyethylene (M<sub>n</sub> up to 1.68 × 10<sup>6</sup> g mol<sup>-1</sup>) with initial activities reaching 3.7 million turnovers per h<sup>-1</sup> at 10 °C. Copolymerizations of ethylene with α-olefins such as 1-hexene and 1-octadecene, as well as tert-butyldimethyl(dec-9-en-1-yloxy)silane give the corresponding copolymers with no decrease in activity. Ethylene ; Polyethylene ; Copolymer ; Catalysis ; Polymerization ; Silane ; Nickel ; Polymer chemistry ; Linear low-density polyethylene ; Materials science ; Chemistry ; Organic chemistry ; Polymer Organometallic Complex Synthesis and Catalysis ; Synthetic Organic Chemistry Methods ; Carbon dioxide utilization in catalysis
Authors: Stefan Wild | Michael Fickert | Aleksandra Mitrović | Vicent Lloret | Christian Neiß | Alejandro Vidal‐Moya | Miguel A. Rivero‐Crespo | Antonio Leyva‐Pérez | Katharina Werbach | Herwig Peterlik | Mathias Grabau | Haiko Wittkämper | Christian Papp | Hans‐Peter Steinrück | Thomas Pichler | Andreas Görling | Frank Hauke | Gonzalo Abellán | Andreas Hirsch
Source: Angewandte Chemie International Edition (article, 2019-01-24)
Field: Materials Science — Materials Chemistry

Lattice Opening upon Bulk Reductive Covalent Functionalization of Black Phosphorus The chemical bulk reductive covalent functionalization of thin-layer black phosphorus (BP) using BP intercalation compounds has been developed. Through effective reductive activation, covalent functionalization of the charged BP by reaction with organic alkyl halides is achieved. Functionalization was extensively demonstrated by means of several spectroscopic techniques and DFT calculations; the products showed higher functionalization degrees than those obtained by neutral routes. Surface modification ; Covalent bond ; Intercalation (chemistry) ; Halide ; Chemistry ; Chemical modification ; Alkyl ; Materials science ; Inorganic chemistry ; Organic chemistry ; Polymer chemistry ; Physical chemistry 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Jorge A. M. Pereira | João Gonçalves | Priscilla Porto-Figueira | José A. Figueira | Vera Lúcia dos Santos Alves | Rosa Perestrelo | Sónia Medina | José S. Câmara
Source: The Analyst (review, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Current trends on microextraction by packed sorbent – fundamentals, application fields, innovative improvements and future applications MEPS, the acronym of microextraction by packed sorbent, is a simple, fast and user- and environmentally-friendly miniaturization of the popular solid-phase extraction technique (SPE). In fact, it has been widely shown that MEPS can easily replace SPE for most, if not all, previous applications. It can attain this with obvious gains in sample and solvent usage, which is greatly reduced without compromising the extraction efficiency. Furthermore, MEPS can be operated with semiautomatic electronic syringes, making it very reliable and versatile, particularly to handle very low and very high sample volumes. This review will focus on the strengths and weaknesses of this technique and the different MEPS architectures commercially available in the context of the MEPS applications reported in the last five years. Additionally, innovative improvements will be highlighted, particularly those related with new applications and recent MEPS configurations and sorbents, such as the controlled directional flow or the innovative μSPEed variant. Sorbent ; Miniaturization ; Extraction (chemistry) ; Solid-phase microextraction ; Acronym ; Chromatography ; Process engineering ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Engineering ; Gas chromatography–mass spectrometry ; Adsorption ; Organic chemistry Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Yi‐Pei Li | Kehang Han | Colin A. Grambow | William H. Green
Source: The Journal of Physical Chemistry A (article, 2019-02-13)
Field: Materials Science — Materials Chemistry

Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry Because collecting precise and accurate chemistry data is often challenging, chemistry data sets usually only span a small region of chemical space, which limits the performance and the scope of applicability of data-driven models. To address this issue, we integrated an active learning machine with automatic ab initio calculations to form a self-evolving model that can continuously adapt to new species appointed by the users. In the present work, we demonstrate the self-evolving concept by modeling the formation enthalpies of stable closed-shell polycyclic species calculated at the B3LYP/6-31G(2df,p) level of theory. By combining a molecular graph convolutional neural network with a dropout training strategy, the model we developed can predict density functional theory (DFT) enthalpies for a broad range of polycyclic species and assess the quality of each predicted value. For the species which the current model is uncertain about, the automatic ab initio calculations provide additional training data to improve the performance of the model. For a test set composed of 2858 cyclic and polycyclic hydrocarbons and oxygenates, the enthalpies predicted by the model agree with the reference DFT values with a root-mean-square error of 2.62 kcal/mol. We found that a model originally trained on hydrocarbons and oxygenates can broaden its prediction coverage to nitrogen-containing species via an active learning process, suggesting that the continuous learning strategy is not only able to improve the model accuracy but is also capable of expanding the predictive capacity of a model to unseen species domains. Thermochemistry ; Oxygenate ; Ab initio ; Chemical space ; Computer science ; Density functional theory ; Machine learning ; Artificial intelligence ; Computational chemistry ; Chemistry ; Physics ; Drug discovery ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Byung Chul Yeo | Donghun Kim | Chansoo Kim | Sang Soo Han
Source: Scientific Reports (article, 2019-04-10)
Field: Materials Science — Materials Chemistry

Pattern Learning Electronic Density of States Electronic density of states (DOS) is a key factor in condensed matter physics and material science that determines the properties of metals. First-principles density-functional theory (DFT) calculations have typically been used to obtain the DOS despite the considerable computation cost. Herein, we report a fast machine learning method for predicting the DOS patterns of not only bulk structures but also surface structures in multi-component alloy systems by a principal component analysis. Within this framework, we use only four features to define the composition, atomic structure, and surfaces of alloys, which are the d-orbital occupation ratio, coordination number, mixing factor, and the inverse of miller indices. While the DFT method scales as O(N<sup>3</sup>) in which N is the number of electrons in the system size, our pattern learning method can be independent on the number of electrons. Furthermore, our method provides a pattern similarity of 91 ~ 98% compared to DFT calculations. This reveals that our learning method will be an alternative that can break the trade-off relationship between accuracy and speed that is well known in the field of electronic structure calculations. Electronic structure ; Computation ; Similarity (geometry) ; Field (mathematics) ; Key (lock) ; Mixing (physics) ; Principal component analysis ; Electron ; Density functional theory Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Shanlin Rao | Gianni Klesse | Phillip J. Stansfeld | Stephen J. Tucker | Mark S.P. Sansom
Source: Proceedings of the National Academy of Sciences (article, 2019-06-24)
Field: Chemistry — Spectroscopy

A heuristic derived from analysis of the ion channel structural proteome permits the rapid identification of hydrophobic gates Ion channel proteins control ionic flux across biological membranes through conformational changes in their transmembrane pores. An exponentially increasing number of channel structures captured in different conformational states are now being determined; however, these newly resolved structures are commonly classified as either open or closed based solely on the physical dimensions of their pore, and it is now known that more accurate annotation of their conductive state requires additional assessment of the effect of pore hydrophobicity. A narrow hydrophobic gate region may disfavor liquid-phase water, leading to local dewetting, which will form an energetic barrier to water and ion permeation without steric occlusion of the pore. Here we quantify the combined influence of radius and hydrophobicity on pore dewetting by applying molecular dynamics simulations and machine learning to nearly 200 ion channel structures. This allows us to propose a simple simulation-free heuristic model that rapidly and accurately predicts the presence of hydrophobic gates. This not only enables the functional annotation of new channel structures as soon as they are determined, but also may facilitate the design of novel nanopores controlled by hydrophobic gates. Dewetting ; Ion channel ; Ion ; Nanotechnology ; Chemical physics ; Biological system ; Materials science ; Channel (broadcasting) ; Membrane ; Chemistry ; Computer science Mass Spectrometry Techniques and Applications ; Lipid Membrane Structure and Behavior ; Antimicrobial Peptides and Activities
Authors: Анна В. Вологжанина
Source: Crystals (article, 2019-09-13)
Field: Chemistry — Physical and Theoretical Chemistry

Intermolecular Interactions in Functional Crystalline Materials: From Data to Knowledge Intermolecular interactions of organic, inorganic, and organometallic compounds are the key to many composition–structure and structure–property networks. In this review, some of these relations and the tools developed by the Cambridge Crystallographic Data Center (CCDC) to analyze them and design solid forms with desired properties are described. The potential of studies supported by the Cambridge Structural Database (CSD)-Materials tools for investigation of dynamic processes in crystals, for analysis of biologically active, high energy, optical, (electro)conductive, and other functional crystalline materials, and for the prediction of novel solid forms (polymorphs, co-crystals, solvates) are discussed. Besides, some unusual applications, the potential for further development and limitations of the CCDC software are reported. Intermolecular force ; Materials science ; Nanotechnology ; Chemistry ; Molecule ; Organic chemistry Crystallography and molecular interactions ; Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods
Authors: Sangmin Lee | Erin G. Teich | Michael Engel | Sharon C. Glotzer
Source: Proceedings of the National Academy of Sciences (article, 2019-07-08)
Field: Materials Science — Materials Chemistry

Entropic colloidal crystallization pathways via fluid–fluid transitions and multidimensional prenucleation motifs Complex crystallization pathways are common in protein crystallization, tetrahedrally coordinated systems, and biomineralization, where single or multiple precursors temporarily appear before the formation of the crystal. The emergence of precursors is often explained by a unique property of the system, such as short-range attraction, directional bonding, or ion association. But, structural characteristics of the prenucleation phases found in multistep crystallization remain unclear, and models are needed for testing and expanding the understanding of fluid-to-solid ordering pathways. Here, we report 3 instances of 2-step crystallization of hard-particle fluids. Crystallization in these systems proceeds via a high-density precursor fluid phase with prenucleation motifs in the form of clusters, fibers and layers, and networks, respectively. The density and diffusivity change across the fluid-fluid phase transition increases with motif dimension. We observe crystal nucleation to be catalyzed by the interface between the 2 fluid phases. The crystals that form are complex, including, notably, a crystal with 432 particles in the cubic unit cell. Our results establish the existence of complex crystallization pathways in entropic systems and reveal prenucleation motifs of various dimensions. Crystallization ; Colloid ; Chemical physics ; Materials science ; Chemistry ; Thermodynamics ; Physics ; Physical chemistry Material Dynamics and Properties ; Calcium Carbonate Crystallization and Inhibition ; Crystallization and Solubility Studies
Authors: Lena Quambusch | Ina Landel | Laura Depta | Jörn Weisner | Niklas Uhlenbrock | Matthias Müller | Franziska Glanemann | Kristina Althoff | Jens T. Siveke | Daniel Rauh
Source: Angewandte Chemie International Edition (article, 2019-10-04)
Field: Chemistry — Organic Chemistry

Covalent‐Allosteric Inhibitors to Achieve Akt Isoform‐Selectivity Isoforms of protein kinase Akt are involved in essential processes including cell proliferation, survival, and metabolism. However, their individual roles in health and disease have not been thoroughly evaluated. Thus, there is an urgent need for perturbation studies, preferably mediated by highly selective bioactive small molecules. Herein, we present a structure-guided approach for the design of structurally diverse and pharmacologically beneficial covalent-allosteric modifiers, which enabled an investigation of the isoform-specific preferences and the important residues within the allosteric site of the different isoforms. The biochemical, cellular, and structural evaluations revealed interactions responsible for the selective binding profiles. The isoform-selective covalent-allosteric Akt inhibitors that emerged from this approach showed a conclusive structure-activity relationship and broke ground in the development of selective probes to delineate the isoform-specific functions of Akt kinases. Allosteric regulation ; Gene isoform ; Covalent bond ; Chemistry ; Protein kinase B ; Selectivity ; Functional selectivity ; Biochemistry ; Pharmacology ; Enzyme ; Signal transduction ; Medicine ; Gene ; Organic chemistry ; G protein-coupled receptor Synthesis and Catalytic Reactions ; Click Chemistry and Applications ; Synthesis and Biological Evaluation
Authors: Ady Suwardi | Daniil Bash | Hong Kuan Ng | Jose Recatala‐Gomez | D. V. Maheswar Repaka | Pawan Kumar | Kedar Hippalgaonkar
Source: Journal of Materials Chemistry A (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Inertial effective mass as an effective descriptor for thermoelectrics <i>via</i> data-driven evaluation Data-driven analysis shows that low effective mass is favorable for high band degeneracy and power factor towards new thermoelectric materials. Effective mass (spring–mass system) ; Thermoelectric materials ; Degeneracy (biology) ; Thermoelectric effect ; Inertial frame of reference ; Materials science ; Physics ; Classical mechanics ; Thermodynamics ; Bioinformatics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Y. Ashok Kumar Reddy | B. Ajitha | Adem Sreedhar | Eswaraiah Varrla
Source: Applied Surface Science (article, 2019-07-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhanced UV photodetector performance in bi-layer TiO2/WO3 sputtered films Materials science ; Photocurrent ; Photodetector ; Optoelectronics ; Layer (electronics) ; Ohmic contact ; Thin film ; Nanotechnology Ga2O3 and related materials ; ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors
Authors: Junhan Xia | Xiaoyu Wang | Shuxian Zhu | Lu Liu | Lidong Li
Source: ACS Applied Materials & Interfaces (article, 2019-01-23)
Field: Materials Science — Materials Chemistry

Gold Nanocluster-Decorated Nanocomposites with Enhanced Emission and Reactive Oxygen Species Generation Ligand-protected gold nanoclusters (AuNCs) show promise for high performance in biological applications, such as imaging and therapeutics. The assembly of AuNCs with biological macromolecules represents a simple but effective approach to fine-tuning of material functionalities. Thus, these materials might enable intracellular applications of AuNCs. Herein, we prepared a new AuNC-based nanometric system through a self-assembly approach mediated by hydrophobic and electrostatic effects. We show that hydrophobic and electrostatic effects between fluorescent AuNCs with protamine and hyaluronic acid contribute to the formation of small nanocomposites with acceptable colloidal stability. More importantly, the AuNC-decorated nanocomposites show assembly enhanced emission and singlet oxygen generation. In vitro experiments showed that our nanocomposites labeled specific cells by targeting CD44 and induced cell death by producing singlet oxygen. Hence, our AuNC-decorated nanocomposites show great potential as theranostic fluorescent nanomaterials. Materials science ; Nanoclusters ; Nanocomposite ; Singlet oxygen ; Nanomaterials ; Nanotechnology ; Fluorescence ; Oxygen ; Chemistry ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Chun‐Teh Chen | Grace X. Gu
Source: Advanced Theory and Simulations (article, 2019-04-05)
Field: Materials Science — Materials Chemistry

Effect of Constituent Materials on Composite Performance: Exploring Design Strategies via Machine Learning Abstract Nature assembles a range of biological composites with remarkable mechanical properties despite being composed of relatively weak polymeric and ceramic components. However, the architectures of biomaterials cannot be considered as optimal designs for engineering applications since biomaterials are constantly evolving for multiple functions beyond carrying external loading. Here, it is aimed to develop an intelligent approach to design superior composites from scratch—starting from constituent materials. A systematic computational investigation of the effect of constituent materials (assumed to be perfectly brittle) on the behavior of composites using an integrated approach combining finite element method, molecular dynamics, and machine learning (ML) is reported. It is demonstrated that instead of using brute‐force methods, machine learning is a much more efficient approach and can generate optimal designs with similar performance to those obtained from an exhaustive search. Furthermore, it is shown that the toughening and strengthening mechanism observed in composites at the continuum‐scale by combining stiff and soft constituents is valid for nanomaterials as well. Results show that high‐performing designs of graphene nanocomposites can be generated using our ML approach. This novel ML‐based design framework can be applied to other material systems to study a variety of structure–property relationships over several length‐scales. Brittleness ; Materials science ; Material Design ; Nanocomposite ; Computer science ; Ceramic ; Composite number ; Nanomaterials ; Scratch ; Mechanical engineering ; Composite material ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Calcium Carbonate Crystallization and Inhibition
Authors: Lixue Cheng | Nikola Kovachki | Matthew Welborn | Thomas F. Miller
Source: Journal of Chemical Theory and Computation (article, 2019-10-22)
Field: Materials Science — Materials Chemistry

Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the current work, we address this problem by introducing a clustering/regression/classification implementation of MOB-ML. In the first step, regression clustering (RC) is used to partition the training data to best fit an ensemble of linear regression (LR) models; in the second step, each cluster is regressed independently, using either LR or GPR; and in the third step, a random forest classifier (RFC) is trained for the prediction of cluster assignments based on MOB feature values. Upon inspection, RC is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations of MOB-ML are found to provide good prediction accuracy with greatly reduced wall-clock training times. For a data set of thermalized (350 K) geometries of 7211 organic molecules of up to seven heavy atoms (QM7b-T), both RC/LR/RFC and RC/GPR/RFC reach chemical accuracy (1 kcal/mol prediction error) with only 300 training molecules, while providing 35000-fold and 4500-fold reductions in the wall-clock training time, respectively, compared to MOB-ML without clustering. The resulting models are also demonstrated to retain transferability for the prediction of large-molecule energies with only small-molecule training data. Finally, it is shown that capping the number of training data points per cluster leads to further improvements in prediction accuracy with negligible increases in wall-clock training time. Computer science ; Cluster analysis ; Training (meteorology) ; Machine learning ; Regression ; Artificial intelligence ; Training set ; Data mining ; Statistics ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography