jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. A. Chin | Cecilia Cisneros | Stephanie M. Araiza | Kevin M. Vargas | Kristi M. Ishihara | Fangyuan Tian
Source: RSC Advances (article, 2018-01-01)
Field: Chemistry — Inorganic Chemistry

Rhodamine B degradation by nanosized zeolitic imidazolate framework-8 (ZIF-8) Adsorption and photodegradation of rhodamine B by ZIF-8 nanocrystals were studied using spectroscopic techniques coupled with density functional theory (DFT) cluster calculations. A fast adsorption rate was observed in the dark, but upon exposure to visible light or UV irradiation the adsorption rate noticeably increased. Although several studies previously reported this phenomenon involving bulk ZIF-8 powder, this is the first mechanistic study to our knowledge that demonstrates adsorption and degradation of rhodamine B by nanosized ZIF-8 under various light conditions. The combined study of N<sub>2</sub> sorption pore analysis and surface-sensitive X-ray photoelectron spectroscopy (XPS) confirmed the surface adsorption was mainly due to the open metal sites and surface groups of nanoporous ZIF-8. The fluorescence studies suggested ZIF-8 nanocrystals were able to generate hydroxyl radicals in water but only under UV illumination. The work presented here provides an insight into understanding nanoscale metal-organic frameworks (MOFs) in the removal of organic molecules from wastewater. Zeolitic imidazolate framework ; Imidazolate ; Degradation (telecommunications) ; Rhodamine B ; Chemical engineering ; Chemistry ; Materials science ; Nuclear chemistry ; Metal-organic framework ; Inorganic chemistry ; Computer science ; Physical chemistry ; Organic chemistry ; Catalysis ; Adsorption ; Photocatalysis ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Sulfur Compounds in Biology ; Electrochemical sensors and biosensors
Authors: Benjamin Nebgen | Nicholas Lubbers | Justin S. Smith | Andrew E. Sifain | Andrey Y. Lokhov | Olexandr Isayev | Adrián E. Roitberg | Kipton Barros | Sergei Tretiak
Source: Journal of Chemical Theory and Computation (article, 2018-07-31)
Field: Materials Science — Materials Chemistry

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks The ability to accurately and efficiently compute quantum-mechanical partial atomistic charges has many practical applications, such as calculations of IR spectra, analysis of chemical bonding, and classical force field parametrization. Machine learning (ML) techniques provide a possible avenue for the efficient prediction of atomic partial charges. Modern ML advances in the prediction of molecular energies [i.e., the hierarchical interacting particle neural network (HIP-NN)] has provided the necessary model framework and architecture to predict transferable, extensible, and conformationally dynamic atomic partial charges based on reference density functional theory (DFT) simulations. Utilizing HIP-NN, we show that ML charge prediction can be highly accurate over a wide range of molecules (both small and large) across a variety of charge partitioning schemes such as the Hirshfeld, CM5, MSK, and NBO methods. To demonstrate transferability and size extensibility, we compare ML results with reference DFT calculations on the COMP6 benchmark, achieving errors of 0.004e<sup>-</sup> (elementary charge). This is remarkable since this benchmark contains two proteins that are multiple times larger than the largest molecules in the training set. An application of our atomic charge predictions on nonequilibrium geometries is the generation of IR spectra for organic molecules from dynamical trajectories on a variety of organic molecules, which show good agreement with calculated IR spectra with reference method. Critically, HIP-NN charge predictions are many orders of magnitude faster than direct DFT calculations. These combined results provide further evidence that ML (specifically HIP-NN) provides a pathway to greatly increase the range of feasible simulations while retaining quantum-level accuracy. Charge (physics) ; Computer science ; Artificial neural network ; Deep neural networks ; Artificial intelligence ; Nanotechnology ; Materials science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Sida Huang | Cheng Shang | Pei‐Lin Kang | Zhi‐Pan Liu
Source: Chemical Science (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Atomic structure of boron resolved using machine learning and global sampling Boron crystals, despite their simple composition, must rank top for complexity: even the atomic structure of the ground state of β-B remains uncertain after 60 years' study. This makes it difficult to understand the many exotic photoelectric properties of boron. The presence of self-doping atoms in the crystal interstitial sites forms an astronomical configurational space, making the determination of the real configuration virtually impossible using current techniques. Here, by combining machine learning with the latest stochastic surface walking (SSW) global optimization, we explore for the first time the potential energy surface of β-B, revealing 15 293 distinct configurations out of the 2 × 10<sup>5</sup> minima visited, and reveal the key rules governing the filling of the interstitial sites. This advance is only allowed by the construction of an accurate and efficient neural network (NN) potential using a new series of structural descriptors that can sensitively discriminate the complex boron bonding environment. We show that, in contrast to the conventional views on the numerous energy-degenerate configurations, only 40 minima of β-B are identified to be within 7 meV per atom in energy above the global minimum of β-B, most of them having been discovered for the first time. These low energy structures are classified into three types of skeletons and six patterns of doping configurations, with a clear preference for a few characteristic interstitial sites. The observed β-B and its properties are influenced strongly by a particular doping site, the B19 site that neighbors the B18 site, which has an exceptionally large vibrational entropy. The configuration with this B19 occupancy, which ranks only 15<sup>th</sup> at 0 K, turns out to be dominant at high temperatures. Our results highlight the novel SSW-NN architecture as the leading problem solver for complex material phenomena, which would then expedite substantially the building of a material genome database. Boron ; Potential energy surface ; Sampling (signal processing) ; Surface (topology) ; Global optimization ; Computer science ; Energy (signal processing) ; Artificial intelligence ; Nanotechnology ; Machine learning ; Materials science ; Chemical physics ; Chemistry ; Physics ; Algorithm ; Molecule ; Mathematics ; Quantum mechanics ; Nuclear physics ; Telecommunications Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; X-ray Diffraction in Crystallography
Authors: Martin Simonovsky | Nikos Komodakis
Source: arXiv (Cornell University) (preprint, 2018-02-09)
Field: Materials Science — Materials Chemistry

GraphVAE: Towards Generation of Small Graphs Using Variational\n Autoencoders Deep learning on graphs has become a popular research topic with many\napplications. However, past work has concentrated on learning graph embedding\ntasks, which is in contrast with advances in generative models for images and\ntext. Is it possible to transfer this progress to the domain of graphs? We\npropose to sidestep hurdles associated with linearization of such discrete\nstructures by having a decoder output a probabilistic fully-connected graph of\na predefined maximum size directly at once. Our method is formulated as a\nvariational autoencoder. We evaluate on the challenging task of molecule\ngeneration.\n Autoencoder ; Embedding ; Computer science ; Generative grammar ; Probabilistic logic ; Linearization ; Theoretical computer science ; Graph ; Transfer of learning ; Deep learning ; Artificial intelligence ; Generative model ; Contrast (vision) ; Task (project management) ; Domain (mathematical analysis) ; Algorithm ; Nonlinear system ; Mathematics ; Engineering Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Topic Modeling
Authors: Achal Rastogi | Uma Maheswari | Richard G. Dorrell | Fabio Rocha Jimenez Vieira | Florian Maumus | Adam B. Kustka | James K. McCarthy | Andrew E. Allen | Paul Kersey | Chris Bowler | Leı̈la Tirichine
Source: Scientific Reports (article, 2018-03-15)
Field: Materials Science — Biomaterials

Integrative analysis of large scale transcriptome data draws a comprehensive landscape of Phaeodactylum tricornutum genome and evolutionary origin of diatoms Diatoms are one of the most successful and ecologically important groups of eukaryotic phytoplankton in the modern ocean. Deciphering their genomes is a key step towards better understanding of their biological innovations, evolutionary origins, and ecological underpinnings. Here, we have used 90 RNA-Seq datasets from different growth conditions combined with published expressed sequence tags and protein sequences from multiple taxa to explore the genome of the model diatom Phaeodactylum tricornutum, and introduce 1,489 novel genes. The new annotation additionally permitted the discovery of extensive alternative splicing in diatoms, including intron retention and exon skipping, which increase the diversity of transcripts generated in changing environments. In addition, we have used up-to-date reference sequence libraries to dissect the taxonomic origins of diatom genes. We show that the P. tricornutum genome is enriched in lineage-specific genes, with up to 47% of the gene models present only possessing orthologues in other stramenopile groups. Finally, we have performed a comprehensive de novo annotation of repetitive elements showing novel classes of transposable elements such as SINE, MITE and TRIM/LARD. This work provides a solid foundation for future studies of diatom gene function, evolution and ecology. Phaeodactylum tricornutum ; Biology ; Genome ; Diatom ; Gene ; Computational biology ; Evolutionary biology ; Transcriptome ; Gene Annotation ; Genetics ; Ecology ; Gene expression Diatoms and Algae Research ; Protist diversity and phylogeny ; Genomics and Phylogenetic Studies
Authors: Takahiro Hayashi | Matthias Tinzl | Takahiro Mori | Ute Krengel | Jonny Proppe | Janne Soetbeer | Daniel Klose | Gunnar Jeschke | Markus Reiher | Donald Hilvert
Source: Nature Catalysis (article, 2018-07-13)
Field: Chemistry — Organic Chemistry

Capture and characterization of a reactive haem–carbenoid complex in an artificial metalloenzyme Carbenoid ; Cyclopropanation ; Chemistry ; Carbene ; Reactive intermediate ; Stereochemistry ; Electrophile ; Histidine ; Biocatalysis ; Combinatorial chemistry ; Catalysis ; Amino acid ; Organic chemistry ; Reaction mechanism ; Rhodium ; Biochemistry Cyclopropane Reaction Mechanisms ; Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Hyun‐Joong Chung | Dmytro Dudenko | Fengjiao Zhang | Gabriele D’Avino | Christian Ruzié | Audrey Richard | Guillaume Schweicher | Jérôme Cornil | David Beljonne | Yves Geerts | Ying Diao
Source: Nature Communications (article, 2018-01-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rotator side chains trigger cooperative transition for shape and function memory effect in organic semiconductors Martensitic transition is a solid-state phase transition involving cooperative movement of atoms, mostly studied in metallurgy. The main characteristics are low transition barrier, ultrafast kinetics, and structural reversibility. They are rarely observed in molecular crystals, and hence the origin and mechanism are largely unexplored. Here we report the discovery of martensitic transition in single crystals of two different organic semiconductors. In situ microscopy, single-crystal X-ray diffraction, Raman and nuclear magnetic resonance spectroscopy, and molecular simulations combined indicate that the rotating bulky side chains trigger cooperative transition. Cooperativity enables shape memory effect in single crystals and function memory effect in thin film transistors. We establish a molecular design rule to trigger martensitic transition in organic semiconductors, showing promise for designing next-generation smart multifunctional materials. Materials science ; Semiconductor ; Phase transition ; Raman spectroscopy ; Chemical physics ; Martensite ; Organic semiconductor ; Cooperativity ; Diffusionless transformation ; Nanotechnology ; Condensed matter physics ; Optoelectronics ; Chemistry ; Microstructure ; Optics ; Composite material ; Physics Organic and Molecular Conductors Research ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Christopher R. Collins | Geoffrey J. Gordon | O. Anatole von Lilienfeld | David Yaron
Source: The Journal of Chemical Physics (article, 2018-03-27)
Field: Materials Science — Materials Chemistry

Constant size descriptors for accurate machine learning models of molecular properties Two different classes of molecular representations for use in machine learning of thermodynamic and electronic properties are studied. The representations are evaluated by monitoring the performance of linear and kernel ridge regression models on well-studied data sets of small organic molecules. One class of representations studied here counts the occurrence of bonding patterns in the molecule. These require only the connectivity of atoms in the molecule as may be obtained from a line diagram or a SMILES string. The second class utilizes the three-dimensional structure of the molecule. These include the Coulomb matrix and Bag of Bonds, which list the inter-atomic distances present in the molecule, and Encoded Bonds, which encode such lists into a feature vector whose length is independent of molecular size. Encoded Bonds' features introduced here have the advantage of leading to models that may be trained on smaller molecules and then used successfully on larger molecules. A wide range of feature sets are constructed by selecting, at each rank, either a graph or geometry-based feature. Here, rank refers to the number of atoms involved in the feature, e.g., atom counts are rank 1, while Encoded Bonds are rank 2. For atomization energies in the QM7 data set, the best graph-based feature set gives a mean absolute error of 3.4 kcal/mol. Inclusion of 3D geometry substantially enhances the performance, with Encoded Bonds giving 2.4 kcal/mol, when used alone, and 1.19 kcal/mol, when combined with graph features. Constant (computer programming) ; Artificial intelligence ; Computer science ; Machine learning ; Statistical physics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: T. Jacobsen | Mathias Jørgensen | Bjørk Hammer
Source: Physical Review Letters (article, 2018-01-12)
Field: Materials Science — Materials Chemistry

On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization Machine learning (ML) is used to derive local stability information for density functional theory calculations of systems in relation to the recently discovered SnO_{2}(110)-(4×1) reconstruction. The ML model is trained on (structure, total energy) relations collected during global minimum energy search runs with an evolutionary algorithm (EA). While being built, the ML model is used to guide the EA, thereby speeding up the overall rate by which the EA succeeds. Inspection of the local atomic potentials emerging from the model further shows chemically intuitive patterns. Density functional theory ; On the fly ; Computer science ; Physics ; Statistical physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Vinh X. Truong | Fanyi Li | Francesca Ercole | John S. Forsythe
Source: ACS Macro Letters (article, 2018-03-26)
Field: Materials Science — Materials Chemistry

Wavelength-Selective Coupling and Decoupling of Polymer Chains via Reversible [2 + 2] Photocycloaddition of Styrylpyrene for Construction of Cytocompatible Photodynamic Hydrogels Reversible photocycloaddition reactions have previously been employed in chemical cross-linking for the preparation of biomaterial scaffolds. However, the processes require activation by high-energy UV light, rendering them unsuitable for modification in biological environments. Here we demonstrate that the [2 + 2] photocycloaddition of styrylpyrene can be activated by visible light at λ = 400-500 nm, enabling rapid and effective conjugation and cross-linking of poly(ethylene glycol) (PEG) in water and under mild irradiation conditions (<i>I</i> = 20 mW cm<sup>-2</sup>). Notably, the reversion of the cycloaddition can be triggered by low-energy UV light at λ = 340 nm, which allows for efficient cleavage of the dimer adduct. Using this wavelength-gated reversible photochemical reaction we are able to prepare PEG hydrogels and modulate their mechanical properties in a bidirectional manner. We also demonstrate healing of the fractured hydrogel by external light triggers. Furthermore, we show that human mesenchymal stem cells can be encapsulated within the gels with high viability post encapsulation. This photochemical approach is therefore anticipated to be highly useful in studies of cell mechanotransduction, with relevance to disease progression and tissue regeneration. Self-healing hydrogels ; Ethylene glycol ; Polymer ; Photochemistry ; Materials science ; Chemistry ; Cycloaddition ; Biomaterial ; Chemical engineering ; Nanotechnology ; Polymer chemistry ; Organic chemistry Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Nanoplatforms for cancer theranostics
Authors: Shuying Wu | Shuhua Peng | Chunhui Wang
Source: Polymers (review, 2018-05-17)
Field: Materials Science — Materials Chemistry

Multifunctional Polymer Nanocomposites Reinforced by Aligned Carbon Nanomaterials Carbon nanomaterials such as carbon black (CB), carbon nanotubes (CNTs), and graphene have demonstrated significant potential as fillers to improve the electrical, thermal, and mechanical properties of polymers and their fiber-reinforced polymer composites. The level of improvement has been found to depend significantly on the degree of alignment of carbon nanomaterials. Due to the very small scale and complex interactions of carbon nanomaterials with polymers and structural fibers, alignment in a given direction has been a major challenge. Over the past decade, considerable effort has been devoted to developing effective strategies to align carbon nanomaterials in polymer matrices. However, significant technological challenges remain, and there is still a lack of understanding of the alignment mechanisms and their effects on the properties of polymers and composites. This paper reviews in situ alignment techniques including shear deformation, mechanical stretching, electrospinning, and application of an external magnetic or electric field, and ex situ techniques including using vertically grown CNTs or graphene. This review particularly focuses on physical mechanisms underpinning the magnetic or electric field-induced alignment and theoretical analyses that describe the different motions occurring and the major parameters controlling alignment. Moreover, this review highlights the recent research findings of the effects of alignment on the properties of polymer nanocomposites. The outlook towards the challenges and opportunities in this field are also discussed in this review. Carbon nanotube ; Materials science ; Nanomaterials ; Nanocomposite ; Graphene ; Nanotechnology ; Polymer ; Polymer nanocomposite ; Composite material ; Electrospinning ; Carbon fibers ; Carbon black ; Composite number ; Natural rubber Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials ; Carbon Nanotubes in Composites
Authors: Christoph Brenninger | John D. Jolliffe | Thorsten Bach
Source: Angewandte Chemie International Edition (review, 2018-05-28)
Field: Chemistry — Organic Chemistry

Chromophore Activation of α,β‐Unsaturated Carbonyl Compounds and Its Application to Enantioselective Photochemical Reactions The first law of photochemistry, as described by Theodor von Grotthuß and John W. Draper, states that only the light absorbed by the irradiated matter can effect photochemical change. Consequently, the photochemical behavior of a molecule can be controlled by bringing its absorbance properties in line with the emission of the light source. A compound with a chromophore that only absorbs light at short wavelengths will not be excited by light of longer wavelengths. If one can reversibly modify the photophysical properties of a compound with a chemical activator, then it is possible to photoexcite only the activated species. For α,β-unsaturated carbonyl compounds, the use of Lewis acids, Brønsted acids, or the formation of the respective iminium ions can bring about the desired chromophore activation to catalyze a photochemical reaction at a given wavelength. In this Minireview, the concept of chromophore activation will be illustrated, and examples of its implementation in enantioselective catalysis will be discussed. Chromophore ; Photochemistry ; Chemistry ; Enantioselective synthesis ; Iminium ; Absorbance ; Excited state ; Catalysis ; Organic chemistry Radical Photochemical Reactions ; Oxidative Organic Chemistry Reactions ; Photochromic and Fluorescence Chemistry
Authors: Sven H. C. Askes | Sylvestre Bonnet
Source: Nature Reviews Chemistry (review, 2018-11-26)
Field: Materials Science — Materials Chemistry

Solving the oxygen sensitivity of sensitized photon upconversion in life science applications Photon upconversion ; Photosensitizer ; Excited state ; Chemistry ; Photochemistry ; Nanotechnology ; Optoelectronics ; Materials science ; Physics ; Luminescence Luminescence and Fluorescent Materials ; Luminescence Properties of Advanced Materials ; Nanoplatforms for cancer theranostics
Authors: Jelmer Sjollema | Sebastian A. J. Zaat | Véronique Fontaine | Madeleine Ramstedt | Reto Luginbuehl | Karin Thevissen | Jiuyi Li | Henny C. van der Mei | Henk J. Busscher
Source: Acta Biomaterialia (review, 2018-02-09)
Field: Chemistry — Organic Chemistry

In vitro methods for the evaluation of antimicrobial surface designs Antimicrobial ; Antimicrobial drug ; Biochemical engineering ; Biofilm ; Nanotechnology ; Biomedical engineering ; Computer science ; Materials science ; Medicine ; Engineering ; Biology ; Microbiology ; Bacteria Antimicrobial agents and applications ; Polymer Surface Interaction Studies ; Orthopedic Infections and Treatments