jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Callum J. Court | Jacqueline M. Cole
Source: Scientific Data (article, 2018-06-19)
Field: Materials Science — Materials Chemistry

Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction Large auto-generated databases of magnetic materials properties have the potential for great utility in materials science research. This article presents an auto-generated database of 39,822 records containing chemical compounds and their associated Curie and Néel magnetic phase transition temperatures. The database was produced using natural language processing and semi-supervised quaternary relationship extraction, applied to a corpus of 68,078 chemistry and physics articles. Evaluation of the database shows an estimated overall precision of 73%. Therein, records processed with the text-mining toolkit, ChemDataExtractor, were assisted by a modified Snowball algorithm, whose original binary relationship extraction capabilities were extended to quaternary relationship extraction. Consequently, its machine learning component can now train with ≤ 500 seeds, rather than the 4,000 originally used. Data processed with the modified Snowball algorithm affords 82% precision. Database records are available in MongoDB, CSV and JSON formats which can easily be read using Python, R, Java and MatLab. This makes the database easy to query for tackling big-data materials science initiatives and provides a basis for magnetic materials discovery. Computer science ; Database ; Java ; Python (programming language) ; Information retrieval ; Data mining ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Shuai‐Shuai Li | Xintong Lv | Didi Ren | Chang‐Lun Shao | Qing Liu | Jian Xiao
Source: Chemical Science (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Redox-triggered cascade dearomative cyclizations enabled by hexafluoroisopropanol An unprecedented cascade dearomative cyclization through hydrogen-bonding-assisted hydride transfer is realized. The aggregate effect of HFIP enables the rapid buildup of polycyclic amines directly from phenols and <i>o</i>-aminobenzaldehydes <i>via</i> a cascade dearomatization/rearomatization/dearomatization sequence. This unique transformation addressed the drawbacks of hydride transfer-involved redox-neutral reactions. Hydride ; Aromatization ; Cascade ; Redox ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Catalysis ; Chromatography Synthesis of Indole Derivatives ; Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions
Authors: Peter Zaspel | Bing Huang | Helmut Harbrecht | O. Anatole von Lilienfeld
Source: Journal of Chemical Theory and Computation (article, 2018-12-05)
Field: Materials Science — Materials Chemistry

Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited Inspired by Pople diagrams popular in quantum chemistry, we introduce a hierarchical scheme, based on the multilevel combination (C) technique, to combine various levels of approximations made when molecular energies are calculated. When combined with quantum machine learning (QML) models, the resulting CQML model is a generalized unified recursive kernel ridge regression that exploits correlations implicitly encoded in training data composed of multiple levels in multiple dimensions. Here, we have investigated up to three dimensions: chemical space, basis set, and electron correlation treatment. Numerical results have been obtained for atomization energies of a set of ∼7000 organic molecules with up to 7 atoms (not counting hydrogens) containing CHONFClS, as well as for ∼6000 constitutional isomers of C<sub>7</sub>H<sub>10</sub>O<sub>2</sub>. CQML learning curves for atomization energies suggest a dramatic reduction in necessary training samples calculated with the most accurate and costly method. In order to generate millisecond estimates of CCSD(T)/cc-pvdz atomization energies with prediction errors reaching chemical accuracy (∼1 kcal/mol), the CQML model requires only ∼100 training instances at CCSD(T)/cc-pvdz level, rather than thousands within conventional QML, while more training molecules are required at lower levels. Our results suggest a possibly favorable trade-off between various hierarchical approximations whose computational cost scales differently with electron number. Quantum machine learning ; Computer science ; Boosting (machine learning) ; Quantum ; Kernel (algebra) ; Set (abstract data type) ; Chemical space ; Quantum chemistry ; Basis set ; Basis (linear algebra) ; Algorithm ; Statistical physics ; Molecule ; Machine learning ; Physics ; Mathematics ; Chemistry ; Quantum mechanics ; Quantum algorithm ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Mohammad Saleheen | Andreas Heyden
Source: ACS Catalysis (article, 2018-01-30)
Field: Materials Science — Materials Chemistry

Liquid-Phase Modeling in Heterogeneous Catalysis A perspective of liquid-phase modeling in heterogeneous catalysis together with new results on O-H bond cleavage in ethylene glycol over Pt(111) obtained by explicit solvation models. Catalysis ; Heterogeneous catalysis ; Phase (matter) ; Chemistry ; Chemical engineering ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Xiaoyuan Ji | Houyu Wang | Bin Song | Binbin Chu | Yao He
Source: Frontiers in Chemistry (review, 2018-02-28)
Field: Materials Science — Materials Chemistry

Silicon Nanomaterials for Biosensing and Bioimaging Analysis Biochemical analysis in reliable, low-toxicity, and real-time manners are essentially important for exploring and unraveling biological events and related mechanisms. Silicon nanomaterial-based sensors and probes have potentiality to satisfy the above-mentioned requirements. Herein, we present an overview of the recent significant improvement in large-scale and facile synthesis of high-quality silicon nanomaterials and the research progress of biosensing and bioimaging analysis based on silicon nanomaterials. We especially illustrate the advanced applications of silicon nanomaterials in the field of ultrasensitive biomolecular detection and dynamic biological imaging analysis, with a focus on real-time and long-term detection. In the final section of this review, we discuss the major challenges and promising development in this domain. Nanomaterials ; Nanotechnology ; Biosensor ; Silicon ; Materials science ; Computer science Silicon Nanostructures and Photoluminescence ; Advanced biosensing and bioanalysis techniques ; Nanowire Synthesis and Applications
Authors: Xiangguo Li | Chongze Hu | Chi Chen | Zhi Deng | Jian Luo | Shyue Ping Ong
Source: Physical review. B./Physical review. B (article, 2018-09-11)
Field: Materials Science — Materials Chemistry

Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals In recent years, efficient interatomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, for translation, rotation, permutation of homonuclear atoms, among others. In this paper, we generalize the spectral neighbor analysis potential (SNAP) model to bcc-fcc binary alloy systems. We demonstrate that machine-learned SNAP models can yield significant improvements even over the well-established high-performing embedded atom method (EAM) and modified EAM potentials for fcc Cu and Ni. We also report on the development of a SNAP model for the fcc Ni-bcc Mo binary system by machine learning a carefully constructed large computed data set of elemental and intermetallic compounds. We demonstrate that this binary Ni-Mo SNAP model can achieve excellent agreement with experiments in the prediction of a Ni-Mo phase diagram as well as near-DFT accuracy in the prediction of many key properties, such as elastic constants, formation energies, melting points, etc., across the entire binary composition range. In contrast, the existing Ni-Mo EAM has significant errors in the prediction of the phase diagram and completely fails in binary compounds. This paper provides a systematic model development process for multicomponent alloy systems, including an efficient procedure to optimize the hyperparameters in the model fitting, and paves the way for long-time large-scale simulations of such systems. Binary number ; Materials science ; Phase diagram ; Embedded atom model ; Statistical physics ; Intermetallic ; Density functional theory ; Atom (system on chip) ; Thermodynamics ; Algorithm ; Phase (matter) ; Computer science ; Alloy ; Physics ; Molecular dynamics ; Mathematics ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Volker L. Deringer | Céline Merlet | Yuchen Hu | Tae Hoon Lee | John A. Kattirtzi | Oliver Pecher | Gábor Cśanyi | Stephen R. Elliott | Clare P. Grey
Source: Chemical Communications (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Towards an atomistic understanding of disordered carbon electrode materials Disordered nanoporous and "hard" carbons are widely used in batteries and supercapacitors, but their atomic structures are poorly determined. Here, we combine machine learning and DFT to obtain new atomistic insight into carbonaceous energy materials. We study structural models of porous and graphitic carbons, and Na intercalation as relevant for sodium-ion batteries. Reactivity (psychology) ; Carbon fibers ; Yield (engineering) ; Materials science ; Electrode ; Chemical physics ; Nanotechnology ; Chemistry ; Physical chemistry ; Composite number ; Metallurgy ; Composite material Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Kamal Choudhary | Qin Zhang | Andrew Reid | Sugata Chowdhury | Nhan V. Nguyen | Zachary Trautt | Marcus Newrock | Faical Y. Congo | Francesca Tavazza
Source: Scientific Data (article, 2018-05-08)
Field: Materials Science — Materials Chemistry

Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms We perform high-throughput density functional theory (DFT) calculations for optoelectronic properties (electronic bandgap and frequency dependent dielectric function) using the OptB88vdW functional (OPT) and the Tran-Blaha modified Becke Johnson potential (MBJ). This data is distributed publicly through JARVIS-DFT database. We used this data to evaluate the differences between these two formalisms and quantify their accuracy, comparing to experimental data whenever applicable. At present, we have 17,805 OPT and 7,358 MBJ bandgaps and dielectric functions. MBJ is found to predict better bandgaps and dielectric functions than OPT, so it can be used to improve the well-known bandgap problem of DFT in a relatively inexpensive way. The peak positions in dielectric functions obtained with OPT and MBJ are in comparable agreement with experiments. The data is available on our websites http://www.ctcms.nist.gov/~knc6/JVASP.html and https://jarvis.nist.gov. NIST ; Dielectric ; Rotation formalisms in three dimensions ; Density functional theory ; Band gap ; Experimental data ; Dielectric function ; Materials science ; Computer science ; Optoelectronics ; Function (biology) ; Computational physics ; Physics ; Mathematics ; Quantum mechanics ; Biology ; Statistics Machine Learning in Materials Science ; 2D Materials and Applications ; Semiconductor materials and devices
Authors: Samuel Suárez‐Pantiga | Raquel Hernández‐Ruiz | Cintia Virumbrales | M.R. Pedrosa | Roberto Sanz
Source: Angewandte Chemie International Edition (article, 2018-12-21)
Field: Chemistry — Inorganic Chemistry

Reductive Molybdenum‐Catalyzed Direct Amination of Boronic Acids with Nitro Compounds The synthesis of aromatic amines is of utmost importance in a wide range of chemical contexts. We report a direct amination of boronic acids with nitro compounds to yield (hetero)aryl amines. The novel combination of a dioxomolybdenum(VI) catalyst and triphenylphosphine as inexpensive reductant has revealed to be decisive to achieve this new C-N coupling. Our methodology has proven to be scalable, air and moisture tolerant, highly chemoselective and engages both aliphatic and aromatic nitro compounds. Moreover, this general and step-economical synthesis of aromatic secondary amines showcases orthogonality to other aromatic amine syntheses as it tolerates aryl halides and carbonyl compounds. Amination ; Chemistry ; Aryl ; Nitro ; Catalysis ; Organic chemistry ; Combinatorial chemistry ; Amine gas treating ; Reductive amination ; Yield (engineering) ; Materials science Asymmetric Hydrogenation and Catalysis ; Catalytic Cross-Coupling Reactions ; Organoboron and organosilicon chemistry
Authors: Max T. B. Clabbers | Tim Gruene | James M. Parkhurst | Jan Pieter Abrahams | David G. Waterman
Source: Acta Crystallographica Section D Structural Biology (article, 2018-05-30)
Field: Materials Science — Materials Chemistry

Electron diffraction data processing with<i>DIALS</i> Electron diffraction is a relatively novel alternative to X-ray crystallography for the structure determination of macromolecules from three-dimensional nanometre-sized crystals. The continuous-rotation method of data collection has been adapted for the electron microscope. However, there are important differences in geometry that must be considered for successful data integration. The wavelength of electrons in a TEM is typically around 40 times shorter than that of X-rays, implying a nearly flat Ewald sphere, and consequently low diffraction angles and a high effective sample-to-detector distance. Nevertheless, the DIALS software package can, with specific adaptations, successfully process continuous-rotation electron diffraction data. Pathologies encountered specifically in electron diffraction make data integration more challenging. Errors can arise from instrumentation, such as beam drift or distorted diffraction patterns from lens imperfections. The diffraction geometry brings additional challenges such as strong correlation between lattice parameters and detector distance. These issues are compounded if calibration is incomplete, leading to uncertainty in experimental geometry, such as the effective detector distance and the rotation rate or direction. Dynamic scattering, absorption, radiation damage and incomplete wedges of data are additional factors that complicate data processing. Here, recent features of DIALS as adapted to electron diffraction processing are shown, including diagnostics for problematic diffraction geometry refinement, refinement of a smoothly varying beam model and corrections for distorted diffraction images. These novel features, combined with the existing tools in DIALS, make data integration and refinement feasible for electron crystallography, even in difficult cases. Diffraction ; Detector ; Optics ; Electron diffraction ; Electron ; Physics ; Rotation (mathematics) ; Powder diffraction ; Computational physics ; Geometry ; Mathematics ; Nuclear magnetic resonance Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Advanced X-ray Imaging Techniques
Authors: Ningjing Liu | Hector Aya Parra | A.M. Pustjens | Kasper Hettinga | Philippe Mongondry | Saskia M. van Ruth
Source: Talanta (article, 2018-02-28)
Field: Chemistry — Analytical Chemistry

Evaluation of portable near-infrared spectroscopy for organic milk authentication Organic products are vulnerable to fraud due to their premium price. Analytical methodology helps to manage the risk of fraud and due to the miniaturization of equipment, tests may nowadays even be rapidly applied on-site. The current study aimed to evaluate portable near infrared spectroscopy (NIRS) in combination with chemometrics to distinguish organic milk from other types of milk, and compare its performance with benchtop NIRS and fatty acid profiling by gas chromatography. The sample set included 37 organic retail milks and 50 non-organic retail milks (of which 36 conventional and 14 green 'pasture' milks). Partial least squares discriminant analysis was performed to build classification models and kernel density estimation (KDE) functions were calculated to generate non-parametric distributions for samples' class probabilities. These distributions showed that portable NIRS was successful to distinguish organic milks from conventional milks, and so were benchtop NIRS and fatty acid profiling procedures. However, it was less successful when 'pasture' milks were considered too, since their patterns occasionally resembled those of the organic milk group. Fatty acid profiling was capable of distinguishing organic milks from both non-organic milks though, including the 'pasture' milks. This comparative study revealed that the classification performance of the portable NIRS for this application was similar to that of the benchtop NIRS. Chemistry ; Chemometrics ; Partial least squares regression ; Adulterant ; Linear discriminant analysis ; Profiling (computer programming) ; Chromatography ; Artificial intelligence ; Statistics ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Jelle Wieme | Kurt Lejaeghere | Georg Kresse | Véronique Van Speybroeck
Source: Nature Communications (article, 2018-11-15)
Field: Chemistry — Inorganic Chemistry

Tuning the balance between dispersion and entropy to design temperature-responsive flexible metal-organic frameworks Temperature-responsive flexibility in metal-organic frameworks (MOFs) appeals to the imagination. The ability to transform upon thermal stimuli while retaining a given crystalline topology is desired for specialized sensors and actuators. However, rational design of such shape-memory nanopores is hampered by a lack of knowledge on the nanoscopic interactions governing the observed behavior. Using the prototypical MIL-53(Al) as a starting point, we show that the phase transformation between a narrow-pore and large-pore phase is determined by a delicate balance between dispersion stabilization at low temperatures and entropic effects at higher ones. We present an accurate theoretical framework that allows designing breathing thermo-responsive MOFs, based on many-electron data for the dispersion interactions and density-functional theory entropy contributions. Within an isoreticular series of materials, MIL-53(Al), MIL-53(Al)-FA, DUT-4, DUT-5 and MIL-53(Ga), only MIL-53(Al) and MIL-53(Ga) are proven to switch phases within a realistic temperature range. Metal-organic framework ; Materials science ; Nanopore ; Density functional theory ; Nanotechnology ; Dispersion (optics) ; Entropy (arrow of time) ; Topology (electrical circuits) ; Chemical physics ; Thermodynamics ; Chemistry ; Physical chemistry ; Physics ; Computational chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Boron and Carbon Nanomaterials Research
Authors: Sabrina Gioria | Fanny Caputo | Patricia Urbán | Ciarán Manus Maguire | Susanne Bremer‐Hoffmann | Adriele Prina‐Mello | Luigi Calzolai | Dóra Méhn
Source: Nanomedicine (article, 2018-01-30)
Field: Materials Science — Biomaterials

Are Existing Standard Methods Suitable for the Evaluation of Nanomedicines: Some Case Studies The use of nanotechnology in medical products has been demonstrated at laboratory scale, and many resulting nanomedicines are in the translational phase toward clinical applications, with global market trends indicating strong growth of the sector in the coming years. The translation of nanomedicines toward the clinic and subsequent commercialization may require the development of new or adaptation of existing standards to ensure the quality, safety and efficacy of such products. This work addresses some identified needs, and illustrates the shortcomings of currently used standardized methods when applied to medical-nanoparticles to assess particle size, drug loading, drug release and in vitro safety. Alternative physicochemical, and in vitro toxicology methods, with the potential to qualify as future standards supporting the evaluation of nanomedicine are provided. Commercialization ; Nanomedicine ; Nanotechnology ; Risk analysis (engineering) ; Quality (philosophy) ; Biochemical engineering ; Computer science ; Engineering ; Medicine ; Business ; Materials science ; Nanoparticle ; Physics Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Drug Solubulity and Delivery Systems
Authors: Helge S. Stein | Dan Guevarra | Paul F. Newhouse | Edwin Soedarmadji | John M. Gregoire
Source: Chemical Science (article, 2018-10-25)
Field: Materials Science — Materials Chemistry

Machine learning of optical properties of materials – predicting spectra from images and images from spectra As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 178 994 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80 000 unique quinary oxide and 67 000 unique quaternary oxide compositions, making it the largest and most diverse experimental materials set utilized in machine learning studies. The extensive dataset enabled training and validation of 3 distinct models for mapping between sample images and absorption spectra, including a conditional variational autoencoder that generates images of hypothetical materials with tailored absorption properties. The absorption patterns auto-generated from sample images capture the salient features of ground truth spectra, and band gap energies extracted from these auto-generated patterns are quite accurate with a mean absolute error of 180 meV, which is the approximate uncertainty from traditional extraction of the band gap energy from measurements of the full transmission and reflection spectra. Optical properties of materials are not only ubiquitous in materials applications but also emblematic of the confluence of underlying physical phenomena yielding the type of complex data relationships that merit and benefit from neural network-type modelling. Spectral line ; Optical spectra ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Materials science ; Chemistry ; Physics ; Astronomy Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Thermography and Photoacoustic Techniques