jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jakob Vowinckel | Aleksej Zelezniak | Roland Bruderer | Michael Mülleder | Lukas Reiter | Markus Ralser
Source: Scientific Reports (article, 2018-03-06)
Field: Chemistry — Spectroscopy

Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition Quantitative proteomics is key for basic research, but needs improvements to satisfy an increasing demand for large sample series in diagnostics, academia and industry. A switch from nanoflowrate to microflowrate chromatography can improve throughput and reduce costs. However, concerns about undersampling and coverage have so far hampered its broad application. We used a QTOF mass spectrometer of the penultimate generation (TripleTOF5600), converted a nanoLC system into a microflow platform, and adapted a SWATH regime for large sample series by implementing retention time- and batch correction strategies. From 3 µg to 5 µg of unfractionated tryptic digests that are obtained from proteomics-typical amounts of starting material, microLC-SWATH-MS quantifies up to 4000 human or 1750 yeast proteins in an hour or less. In the acquisition of 750 yeast proteomes, retention times varied between 2% and 5%, and quantified the typical peptide with 5-8% signal variation in replicates, and below 20% in samples acquired over a five-months period. Providing precise quantities without being dependent on the latest hardware, our study demonstrates that the combination of microflow chromatography and data-independent acquisition strategies has the potential to overcome current bottlenecks in academia and industry, enabling the cost-effective generation of precise quantitative proteomes in large scale. Proteome ; Computer science ; Throughput ; Sample (material) ; Proteomics ; Undersampling ; Mass spectrometry ; Data acquisition ; Quantitative proteomics ; Chromatography ; Chemistry ; Artificial intelligence ; Bioinformatics ; Biology ; Telecommunications Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Sha-Sha Ge | Biao Chen | Yuanyuan Wu | Qing-Su Long | Yongliang Zhao | Peiyi Wang | Song Yang
Source: RSC Advances (review, 2018-01-01)
Field: Chemistry — Organic Chemistry

Current advances of carbene-mediated photoaffinity labeling in medicinal chemistry Photoaffinity labeling (PAL) in combination with a chemical probe to covalently bind its target upon UV irradiation has demonstrated considerable promise in drug discovery for identifying new drug targets and binding sites. In particular, carbene-mediated photoaffinity labeling (cmPAL) has been widely used in drug target identification owing to its excellent photolabeling efficiency, minimal steric interference and longer excitation wavelength. Specifically, diazirines, which are among the precursors of carbenes and have higher carbene yields and greater chemical stability than diazo compounds, have proved to be valuable photolabile reagents in a diverse range of biological systems. This review highlights current advances of cmPAL in medicinal chemistry, with a focus on structures and applications for identifying small molecule-protein and macromolecule-protein interactions and ligand-gated ion channels, coupled with advances in the discovery of targets and inhibitors using carbene precursor-based biological probes developed in recent decades. Carbene ; Photoaffinity labeling ; Chemistry ; Combinatorial chemistry ; Biochemistry ; Binding site ; Catalysis Click Chemistry and Applications ; Synthesis and Catalytic Reactions ; Cyclopropane Reaction Mechanisms
Authors: Anunay Gupta | Om Prakash Yadav | Douglas DeVoto | Joshua Major
Source:  (review, 2018-08-27)
Field: Materials Science — Materials Chemistry

A Review of Degradation Behavior and Modeling of Capacitors This paper firstly reviews the failure causes, modes and mechanisms of two major types of capacitors used in power electronic systems — metallized film capacitors and electrolytic capacitors. The degradation modeling related to these capacitors is then presented. Both physics-of-failure and data-driven degradation models for reliability and lifetime estimation are discussed. Based on the exhaustive literature review on degradation modeling of capacitors, we provide a critical assessment and future research directions. Capacitor ; Electrolytic capacitor ; Degradation (telecommunications) ; Reliability (semiconductor) ; Film capacitor ; Reliability engineering ; Filter capacitor ; Computer science ; Electronic engineering ; Materials science ; Power (physics) ; Engineering ; Electrical engineering ; Voltage ; Physics High voltage insulation and dielectric phenomena ; Advanced Battery Technologies Research ; Electrical Contact Performance and Analysis
Authors: Hui‐Min Wen | Libo Li | Rui‐Biao Lin | Bin Li | Bin Hu | Wei Zhou | Jun Hu | Banglin Chen
Source: Journal of Materials Chemistry A (article, 2018-01-01)
Field: Chemistry — Inorganic Chemistry

Fine-tuning of nano-traps in a stable metal–organic framework for highly efficient removal of propyne from propylene A stable MOF material (ZJUT-1) with fine-tuned nanocages, exhibiting the desired size, shape, and functionalities for trapping a single C<sub>3</sub>H<sub>4</sub>molecule, can efficiently separate trace amounts of C<sub>3</sub>H<sub>4</sub>from a 1/99 C<sub>3</sub>H<sub>4</sub>/C<sub>3</sub>H<sub>6</sub>mixture. Nanocages ; Propyne ; Nano- ; Materials science ; Metal ; Molecule ; TRACE (psycholinguistics) ; Trapping ; Nanotechnology ; Analytical Chemistry (journal) ; Chemistry ; Organic chemistry ; Catalysis ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Gregory H. Teichert | Krishna Garikipati
Source: Computer Methods in Applied Mechanics and Engineering (article, 2018-10-28)
Field: Materials Science — Materials Chemistry

Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics Field (mathematics) ; Fidelity ; Phase (matter) ; Morphology (biology) ; Algorithm ; Statistical physics ; Nanotechnology ; Materials science ; Computer science ; Biological system ; Physics ; Mathematics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Advanced Materials Characterization Techniques
Authors: Andrew E. Bruno | Patrick Charbonneau | Janet Newman | Edward H. Snell | David R. So | Vincent Vanhoucke | Christopher J. Watkins | Shawn P. Williams | Julie Wilson
Source: PLoS ONE (article, 2018-06-20)
Field: Materials Science — Materials Chemistry

Classification of crystallization outcomes using deep convolutional neural networks The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications. Crystallization ; Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Set (abstract data type) ; Key (lock) ; Data set ; Deep learning ; Crystal (programming language) ; Machine learning ; Physics Enzyme Structure and Function ; Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Naruki Yoshikawa | Kei Terayama | Masato Sumita | Teruki Homma | Kenta Oono | Koji Tsuda
Source: Chemistry Letters (article, 2018-10-10)
Field: Materials Science — Materials Chemistry

Population-based De Novo Molecule Generation, Using Grammatical Evolution Automatic molecule design with machine learning and simulations has shown a remarkable ability to generate new and promising drug candidates. We propose a new population-based approach using a grammatical evolution named ChemGE, that can update a large population of molecules concurrently and evaluate with multiple simulators in parallel. In computational experiments, ChemGE succeeded in finding hundreds of candidate molecules whose affinity for thymidine kinase is better than that of known binding molecules in a database (DUD-E). We propose a new population-based approach using grammatical evolution named ChemGE, that can update a large population of molecules concurrently and evaluate with multiple simulators in parallel. In computational experiments, ChemGE succeeded in finding hundreds of candidate molecules whose affinities for thymidine kinase are better than those of known binding molecules in a database (DUD-E). Molecule ; Population ; Affinities ; Small molecule ; Grammatical evolution ; Set (abstract data type) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Evolutionary Algorithms and Applications
Authors: Edgar A. Engel | Andrea Anelli | Michele Ceriotti | Chris J. Pickard | R. J. Needs
Source: Nature Communications (article, 2018-05-30)
Field: Chemistry — Inorganic Chemistry

Mapping uncharted territory in ice from zeolite networks to ice structures Ice is one of the most extensively studied condensed matter systems. Yet, both experimentally and theoretically several new phases have been discovered over the last years. Here we report a large-scale density-functional-theory study of the configuration space of water ice. We geometry optimise 74,963 ice structures, which are selected and constructed from over five million tetrahedral networks listed in the databases of Treacy, Deem, and the International Zeolite Association. All prior knowledge of ice is set aside and we introduce "generalised convex hulls" to identify configurations stabilised by appropriate thermodynamic constraints. We thereby rediscover all known phases (I-XVII, i, 0 and the quartz phase) except the metastable ice IV. Crucially, we also find promising candidates for ices XVIII through LI. Using the "sketch-map" dimensionality-reduction algorithm we construct an a priori, navigable map of configuration space, which reproduces similarity relations between structures and highlights the novel candidates. By relating the known phases to the tractably small, yet structurally diverse set of synthesisable candidate structures, we provide an excellent starting point for identifying formation pathways. Sketch ; Set (abstract data type) ; Curse of dimensionality ; Space (punctuation) ; Tetrahedron ; Computer science ; Mathematics ; Algorithm ; Geometry ; Artificial intelligence Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Complex Network Analysis Techniques
Authors: Xu Hu | Dahong Li | Chun Chu | Xu Li | Xianhua Wang | Ying Jia | Huiming Hua | Fanxing Xu
Source: International Journal of Molecular Sciences (review, 2018-10-30)
Field: Chemistry — Organic Chemistry

Antiproliferative Effects of Alkaloid Evodiamine and Its Derivatives Alkaloids, a category of natural products with ring structures and nitrogen atoms, include most U.S. Food and Drug Administration approved plant derived anti-cancer agents. Evodiamine is an alkaloid with attractive multitargeting antiproliferative activity. Its high content in the natural source ensures its adequate supply on the market and guarantees further medicinal study. To the best of our knowledge, there is no systematic review about the antiproliferative effects of evodiamine derivatives. Therefore, in this article the review of the antiproliferative activities of evodiamine will be updated. More importantly, the antiproliferative activities of structurally modified new analogues of evodiamine will be summarized for the first time. Evodiamine ; Alkaloid ; Rutaecarpine ; Chemistry ; Anticancer drug ; Pharmacology ; Food and drug administration ; Drug ; Stereochemistry ; Medicine Quinazolinone synthesis and applications ; Synthesis and Biological Evaluation ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Xinghua Wu | Xin Zhao | Jeffrey Weng Chye Ho | Zhong Chen
Source: Chemical Engineering Journal (article, 2018-07-30)
Field: Materials Science — Surfaces, Coatings and Films

Design and durability study of environmental-friendly room-temperature processable icephobic coatings Durability ; Materials science ; Silicone ; Polydimethylsiloxane ; Coating ; Composite material ; Environmentally friendly ; Epoxy Surface Modification and Superhydrophobicity ; Fiber-reinforced polymer composites ; Icing and De-icing Technologies
Authors: Huifang Li | Haoyuan Li | Qing-Qing Dai | Hong Li | Jean‐Luc Brédas
Source: Advanced Theory and Simulations (article, 2018-01-30)
Field: Materials Science — Materials Chemistry

Hydrolytic Stability of Boronate Ester‐Linked Covalent Organic Frameworks Abstract The stability of covalent organic frameworks (COFs) is essential to their applications. However, the common boronate ester‐linked COFs are susceptible to attack by nucleophiles (such as water molecules) at the electron‐deficient boron sites. To provide an understanding of the hydrolytic stability of the representative boronate ester‐linked COF‐5 and of the associated hydrolysis mechanisms, density functional theory (DFT) calculations were performed to characterize the hydrolysis reactions of the molecule formed by the condensation of 1,4‐phenylenebis(boronic acid) (PBBA) and 2,3,6,7,10,11‐hexahydroxytriphenylene (HHTP) monomers; two cases were considered, one dealing with the freestanding molecule and the other with the molecule interacting with COF layers. It was found that the boronate ester (B–O) bond dissociation, which requires one H 2 O molecule, has a relatively high energy barrier of 22.3 kcal mol −1 . However, the presence of an additional H 2 O molecule significantly accelerates hydrolysis by reducing the energy barrier by a factor of 3. Importantly, the hydrolysis of boronate ester bonds situated in a COF environment follows reaction pathways that are different and have increased energy barriers. These results point to an enhanced hydrolytic stability of COF‐5 crystals. Chemistry ; Hydrolysis ; Covalent bond ; Molecule ; Nucleophile ; Monomer ; Bond-dissociation energy ; Dissociation (chemistry) ; Chemical stability ; Organic chemistry ; Combinatorial chemistry ; Computational chemistry ; Polymer chemistry ; Catalysis ; Polymer Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Anna Eibel | David E. Fast | Georg Gescheidt
Source: Polymer Chemistry (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Choosing the ideal photoinitiator for free radical photopolymerizations: predictions based on simulations using established data A modelling toolbox for classifying and predicting photoinitiator efficiencies is presented, considering absorption properties, dissociation quantum yields, light intensities, irradiation wavelengths, kinetics of monomer addition and side reactions. Photoinitiator ; Photochemistry ; Photopolymer ; Dissociation (chemistry) ; Irradiation ; Monomer ; Ideal (ethics) ; Wavelength ; Kinetics ; Chemistry ; Materials science ; Polymer ; Optoelectronics ; Physical chemistry ; Composite material ; Physics Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization
Authors: Lorenzo Rovigatti | Nicoletta Gnan | Letizia Tavagnacco | Angel J. Moreno | Emanuela Zaccarelli
Source: Soft Matter (review, 2018-12-13)
Field: Chemistry — Organic Chemistry

Numerical modelling of non-ionic microgels: an overview Microgels are complex macromolecules. These colloid-sized polymer networks possess internal degrees of freedom and, depending on the polymer(s) they are made of, can acquire a responsiveness to variations of the environment (temperature, pH, salt concentration, etc.). Besides being valuable for many practical applications, microgels are also extremely important to tackle fundamental physics problems. As a result, these last years have seen a rapid development of protocols for the synthesis of microgels, and more and more research has been devoted to the investigation of their bulk properties. However, from a numerical standpoint the picture is more fragmented, as the inherently multi-scale nature of microgels, whose bulk behaviour crucially depends on the microscopic details, cannot be handled at a single level of coarse-graining. Here we present an overview of the methods and models that have been proposed to describe non-ionic microgels at different length-scales, from the atomistic to the single-particle level. We especially focus on monomer-resolved models, as these have the right level of details to capture the most important properties of microgels, responsiveness and softness. We suggest that these microscopic descriptions, if realistic enough, can be employed as starting points to develop the more coarse-grained representations required to investigate the behaviour of bulk suspensions. Ionic bonding ; Chemical physics ; Materials science ; Chemistry ; Nanotechnology ; Chemical engineering ; Statistical physics ; Ion ; Physics ; Engineering ; Organic chemistry Surfactants and Colloidal Systems ; Heat and Mass Transfer in Porous Media ; Fluid Dynamics and Thin Films
Authors: Anna Denchai | Daniele Tartarini | Elisa Mele
Source: Frontiers in Bioengineering and Biotechnology (review, 2018-10-24)
Field: Materials Science — Biomaterials

Cellular Response to Surface Morphology: Electrospinning and Computational Modeling Surface properties of biomaterials, such as chemistry and morphology, have a major role in modulating cellular behavior and therefore impact on the development of high-performance devices for biomedical applications, such as scaffolds for tissue engineering and systems for drug delivery. Opportunely-designed micro- and nanostructures provides a unique way of controlling cell-biomaterial interaction. This mini-review discusses the current research on the use of electrospinning (extrusion of polymer nanofibers upon the application of an electric field) as effective technique to fabricate patterns of micro- and nano-scale resolution, and the corresponding biological studies. The focus is on the effect of morphological cues, including fiber alignment, porosity and surface roughness of electrospun mats, to direct cell migration and to influence cell adhesion, differentiation and proliferation. Experimental studies are combined with computational models that predict and correlate the surface composition of a biomaterial with the response of cells in contact with it. The use of predictive models can facilitate the rational design of new bio-interfaces. Electrospinning ; Biomaterial ; Materials science ; Nanotechnology ; Nanofiber ; Tissue engineering ; Adhesion ; Cell adhesion ; Surface roughness ; Biomedical engineering ; Polymer ; Composite material ; Engineering Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Bone Tissue Engineering Materials
Authors: Ashutosh Srivastava | Tetsuro Nagai | Arpita Srivastava | Osamu Miyashita | Florence Tama
Source: International Journal of Molecular Sciences (review, 2018-10-30)
Field: Materials Science — Materials Chemistry

Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes. Myoglobin ; Protein dynamics ; Dynamics (music) ; Molecular dynamics ; Protein structure ; Biomolecular structure ; Structural biology ; Computational biology ; Computer science ; Chemistry ; Physics ; Biology ; Computational chemistry ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Hemoglobin structure and function
Authors: Mohammad Mehdi Ghahremanpour | Paul J. van Maaren | David van der Spoel
Source: Scientific Data (article, 2018-04-10)
Field: Chemistry — Physical and Theoretical Chemistry

The Alexandria library, a quantum-chemical database of molecular properties for force field development Data quality as well as library size are crucial issues for force field development. In order to predict molecular properties in a large chemical space, the foundation to build force fields on needs to encompass a large variety of chemical compounds. The tabulated molecular physicochemical properties also need to be accurate. Due to the limited transparency in data used for development of existing force fields it is hard to establish data quality and reusability is low. This paper presents the Alexandria library as an open and freely accessible database of optimized molecular geometries, frequencies, electrostatic moments up to the hexadecupole, electrostatic potential, polarizabilities, and thermochemistry, obtained from quantum chemistry calculations for 2704 compounds. Values are tabulated and where available compared to experimental data. This library can assist systematic development and training of empirical force fields for a broad range of molecules. Chemical space ; Force field (fiction) ; Thermochemistry ; Database ; Computer science ; Quality (philosophy) ; Chemistry ; Field (mathematics) ; Computational chemistry ; Nanotechnology ; Physics ; Materials science ; Physical chemistry ; Quantum mechanics ; Mathematics ; Drug discovery Various Chemistry Research Topics ; Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Physics Studies