jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew Jacobs
Source: cIRcle (University of British Columbia) (article, 2019-01-01)
Field: Chemistry — Spectroscopy

Collision induced dissociation and mass spectrometry with the TITAN Multiple-Reflection Time-of-Flight Mass-Spectrometer To better understand nuclear structure, high-precision mass spectrometry of radioactive nuclei is required. However, as the nuclei of interest move further away from stability, the half-lives and production yields drop. Furthermore, the cocktail beams are frequently contaminated with unwanted isobars and molecules which can obscure the species of interest and impede the measurement. To overcome these obstacles, the Multiple-Reflection Time-of-Flight Mass-Spectrometer (MR-ToF-MS) was commissioned at TRIUMF's Ion Trap for Atomic and Nuclear science (TITAN). The device can purify beams through the process of mass selective re-trapping and provide fast, precise, and high-sensitivity mass measurements. These two modes of operation can be used sequentially to conduct mass measurements of exceedingly contaminated beams. Furthermore, the technique of collision induced dissociation (CID) has been investigated and developed to allow for further suppression of molecular contamination. With CID, the molecular contamination can be suppressed by one to two orders of magnitude. This development was undertaken following a Ne experiment in which a large amount of molecular contamination was found. However, the measurement capabilities of the MR-ToF-MS was demonstrated during the mass measurement campaign of neutron-rich ²⁴-²⁶Ne in which relative uncertainties of approximately 10-⁷ were achieved. These measurements began to approach the "Island of Inversion" for the Ne isotope chain, and they further motivate the investigation of this region in the future, ultimately ending with the measurement of 31Ne. Mass spectrometry ; Titan (rocket family) ; Collision-induced dissociation ; Dissociation (chemistry) ; Time-of-flight mass spectrometry ; Time of flight ; Chemistry ; Hybrid mass spectrometer ; Ion-mobility spectrometry–mass spectrometry ; Selected reaction monitoring ; Tandem mass spectrometry ; Physics ; Astrobiology ; Chromatography ; Ion ; Ionization Mass Spectrometry Techniques and Applications ; Atomic and Molecular Physics ; Ion-surface interactions and analysis
Authors: Ephraim Haffner-Staton | A. La Rocca | Alasdair Cairns | Michael McGhee | Michael W. Fay
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2019-04-02)
Field: Materials Science — Surfaces, Coatings and Films

Assessing the Accuracy of Soot Nanoparticle Morphology Measurements Using Three-Dimensional Electron Tomography &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Morphology plays an important role in determining behaviour and impact of soot nanoparticles, including effect on human health, atmospheric optical properties, contribution to engine wear, and role in marine ecology. However, its nanoscopic size has limited the ability to directly measure useful morphological parameters such as surface area and effective volume. Recently, 3D morphology characterization of soot nanoparticles via electron tomography has been the subject of several introductory studies. So-called ‘3D-TEM’ has been posited as an improvement over traditional 2D-TEM characterization due to the elimination of the error-inducing information gap that exists between 3-dimensional soot structures and 2-dimensional TEM projections. Little follow-up work has been performed due to difficulties with developing methodologies into robust high-throughput techniques. Recent work by the authors has exhibited significant improvements in efficiency, though as yet due consideration has not been given to assessing fidelity of the technique. This is vital to confirm significant and tangible improvements in soot-characterization accuracy that will establish 3D-TEM as a legitimate tool. Synthetic ground-truth data was developed to closely mimic real soot structures and the 3D-TEM volume-reconstruction process. A variety of procedures were tested to assess the magnitude and nuances of deviations from ground-truth values. Results showed average Z-elongation due to the ‘missing-wedge’ at 3.5% for the previously developed optimized procedure. Mean deviations from ground-truth in volume and surface area were 2.0% and -0.1% respectively. Results indicate highly accurate 3D-reconstruction can be achieved with an optimized procedure that can bridge the gap to permit high-throughput 3D morphology characterization of soot.&lt;/div&gt;&lt;/div&gt; Soot ; Nanoparticle ; Electron tomography ; Materials science ; Morphology (biology) ; Tomography ; Electron ; Nanotechnology ; Scanning electron microscope ; Optics ; Composite material ; Physics ; Chemistry ; Combustion ; Geology ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; nanoparticles nucleation surface interactions
Authors: Annu Annu
Source: The Pharma Innovation (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Supramolecular chemistry: Concepts and applications Supramolecular chemistry, a dynamic field at the intersection of chemistry and nanoscience, investigates the assembly of molecular building blocks into intricate, functional architectures governed by non-covalent interactions. This paper delves into the fundamental principles and applications of supramolecular chemistry, highlighting its significance in molecular recognition, self-assembly, and material design. By elucidating the diverse range of non-covalent interactions such as hydrogen bonding, π-π stacking, and host-guest interactions, this research elucidates the underlying mechanisms driving supramolecular phenomena. Furthermore, it explores the applications of supramolecular chemistry in various fields including drug delivery, catalysis, and materials science, emphasizing its potential to address contemporary challenges in nanotechnology and beyond. Through comprehensive analysis and synthesis of current literature, this paper offers insights into the evolving landscape of supramolecular chemistry and its promising prospects for future advancements. Supramolecular chemistry ; Chemistry ; Computer science ; Nanotechnology ; Materials science ; Organic chemistry ; Molecule Inorganic and Organometallic Chemistry
Authors: Blanca Guasch | Sergi Cortiñas‐Rovira | M. González | Santiago Justel | Javier Peña
Source: RIUVic (article, 2019-02-26)
Field: Materials Science — Materials Chemistry

The Representation of Graphene in the Online Press of the United States, the United Kingdom, and Spain Graphene is the first two-dimensional material discovered by humans. It was isolated for the first time in 2004 and is being established as a revolutionary material for the 21st century. This study presents an analysis of the quantitative and qualitative contents from all news about graphene published online in The New York Times (United States), The Guardian (United Kingdom), and El País (Spain) from October 2004 to October 2017. We evaluate the media’s coverage of a new, revolutionary material such as graphene in terms of the volume of news items per year and the authorship and treatment, thematic focus, and content of the articles—from both a quantitative and a qualitative perspective. This analysis offers insights into the knowledge transfer about graphene and can serve as a model to the materials science communication field. Guardian ; Graphene ; Content analysis ; Thematic analysis ; Perspective (graphical) ; Representation (politics) ; Political science ; Media studies ; Sociology ; Regional science ; Social science ; Qualitative research ; Law ; Nanotechnology ; Computer science ; Politics ; Artificial intelligence ; Materials science Graphene research and applications ; Graphene and Nanomaterials Applications
Authors: Prabha Ranasinghe
Source: Science Journal of Chemistry (article, 2019-01-01)
Field: Chemistry — Spectroscopy

Atropisomeric Separation of PCB-95 by HPLC 2,2’,3,5’,6-Pentachlorobiphenyl (PCB-95) is an environmentally significant chiral PCB, of which enantioselective toxicity, biodegradation and chiral stability studies have been limited to date, as no commercially available enantiomers exist for PCB-95 and due to the lack of an efficient preparatory chiral separation method. A selective, sensitive, and rapid high-performance liquid chromatography with UV detection (HPLC-UV) method has been developed and validated for the chromatographic separation and quantitation of PCB-95 enantiomers. In this study, we resolved enantiomers of PCB-95 using a cellulose tris (4-methylbenzoate) Chiralcel OJ- <i>H</i> column. After evaluating mobile phase compositions and temperatures, optimum separation and detection were obtained with isocratic 100% n-hexane as the mobile phase, a column temperature of 20°C, a flow rate of 1 mL/min, and a detection wavelength of 280 nm. The total run time was 8 minutes. Enantiomer purity was confirmed using enantioselective gas capillary chromatography-electron capture detection. The developed method was validated as per International Conference on Harmonization (ICH) guidelines with respect to limit of detection, limit of quantification, precision, linearity, robustness and ruggedness. Chromatography ; Separation (statistics) ; High-performance liquid chromatography ; Chemistry ; Mathematics ; Statistics Analytical Chemistry and Chromatography ; Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Rainier Barrett | Maghesree Chakraborty | Dilnoza Amirkulova | Heta A. Gandhi | Andrew Dickson White
Source: ChemRxiv (preprint, 2019-10-16)
Field: Materials Science — Materials Chemistry

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: HOOMD-blue with TensorFlow As interest grows in applying machine learning force-fields and methods to molecular simulation, there is a need for state-of-the-art inference methods to use trained models within efficient molecular simulation engines. We have designed and implemented software that enables integration of a scalable GPU-accelerated molecular mechanics engine, HOOMD-blue, with the machine learning (ML) TensorFlow package. TensorFlow is a GPU-accelerated, scalable, graph-based tensor computation model building package that has been the implementation of many recent innovations in deep learning and other ML tasks. TensorFlow models are constructed in Python and can be visualized or debugged using the rich set of tools implemented in the TensorFlow package. In this article, we present four major examples of tasks this software can accomplish which would normally require multiple different tools: (1) we train a neural network to reproduce a force field of a Lennard-Jones simulation; (2) we perform online force matching of methanol; (3) we compute the maximum entropy bias of a Lennard-Jones collective variable; (4) we calculate the scattering profile of an ongoing TIP4P water molecular dynamics simulation. This work should accelerate both the design of new neural network based models in computational chemistry research and reproducible model specification by leveraging a widely-used ML package. Python (programming language) ; Computer science ; Scalability ; Artificial intelligence ; Machine learning ; Deep learning ; Artificial neural network ; Computational science ; Software ; Computation ; General-purpose computing on graphics processing units ; Inference ; Algorithm ; Programming language ; Computer graphics (images) ; Graphics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Parallel Computing and Optimization Techniques
Authors: Rainier Barrett | Maghesree Chakraborty | Dilnoza Amirkulova | Heta A. Gandhi | Andrew Dickson White
Source: ChemRxiv (preprint, 2019-08-26)
Field: Materials Science — Materials Chemistry

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: Hoomd-Blue with TensorFlow We have designed and implemented software that enables integration of a scalable GPU-accelerated molecular mechanics engine, Hoomd-blue, with the machine learning (ML) TensorFlow package. TensorFlow is a GPU accelerated, scalable, graph-based tensor computation model building package that has been the implementation of many recent innovations in deep learning and other ML tasks. Tensor computation graphs allow for designation of robust, flexible, and easily replicated computational models for a variety of tasks. Our plugin leverages the generality and speed of computational tensor graphs in TensorFlow to enable four previously challenging tasks in molecular dynamics: (1) the calculation of arbitrary force-fields including neural-network-based, stochastic, and/or automatically-generated force-fields which are differentiated from potential functions; (2) the efficient computation of arbitrary collective variables; (3) the biasing of simulations via automatic differentiation of collective variables and consequently the implementation of many free energy biasing methods; (4) ML on any of the above tasks, including coarse grain force fields, on-the-fly learned biases, and collective variable calculations. The TensorFlow models are constructed in Python and can be visualized or debugged using the rich set of tools implemented in the TensorFlow package. In this article, we present examples of the four major tasks this method can accomplish, benchmark data, and describe the architecture of our implementation. This method should lead to both the design of new models in computational chemistry research and reproducible model specification without requiring recompiling or writing low-level code. Computer science ; Python (programming language) ; Scalability ; Automatic differentiation ; Artificial intelligence ; Plug-in ; Computation ; Computational science ; Machine learning ; Software ; Benchmark (surveying) ; Tensor (intrinsic definition) ; Theoretical computer science ; Computer engineering ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Pascal Friederich | Gabriel dos Passos Gomes | Riccardo De Bin | Alán Aspuru‐Guzik | David Balcells
Source: ChemRxiv (preprint, 2019-11-27)
Field: Materials Science — Materials Chemistry

Machine Learning Reactivity in the Chemical Space Surrounding Vaska's Complex Machine learning models, including neural networks, Bayesian optimization, gradient boosting and Gaussian processes, were trained with DFT data for the accurate, affordable and explainable prediction of hydrogen activation barriers in the chemical space surrounding Vaska's complex. Chemical space ; Computer science ; Artificial intelligence ; Bayesian optimization ; Machine learning ; Space (punctuation) ; Boosting (machine learning) ; Artificial neural network ; Bayesian network ; Gaussian process ; Bayesian probability ; Gradient boosting ; Gaussian ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Ryther Anderson | Achay Biong | Diego A. Gómez‐Gualdrón
Source: ChemRxiv (preprint, 2019-09-27)
Field: Materials Science — Materials Chemistry

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model &lt;div&gt;Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.&lt;/div&gt; Adsorption ; Krypton ; Molecule ; Artificial neural network ; Xenon ; Metal-organic framework ; Molecular descriptor ; Force field (fiction) ; Combining rules ; Molecular dynamics ; Chemistry ; Materials science ; Computer science ; Nanotechnology ; Chemical physics ; Computational chemistry ; Artificial intelligence ; Machine learning ; Physical chemistry ; Organic chemistry ; Mathematics ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Seoin Back | Kevin Tran | Zachary W. Ulissi
Source: ChemRxiv (preprint, 2019-04-02)
Field: Materials Science — Materials Chemistry

Towards a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;div&gt;&lt;p&gt;Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.&lt;br&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;div&gt;&lt;div&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Catalysis ; Rutile ; Overpotential ; Oxide ; Transition metal ; Oxygen ; Chemistry ; Density functional theory ; Nanotechnology ; Materials science ; Chemical engineering ; Physical chemistry ; Computational chemistry ; Electrochemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Jocelyn Sunseri | David Ryan Koes
Source: arXiv (Cornell University) (preprint, 2019-12-10)
Field: Materials Science — Materials Chemistry

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning\n Applications There are many ways to represent a molecule as input to a machine learning\nmodel and each is associated with loss and retention of certain kinds of\ninformation. In the interest of preserving three-dimensional spatial\ninformation, including bond angles and torsions, we have developed libmolgrid,\na general-purpose library for representing three-dimensional molecules using\nmultidimensional arrays. This library also provides functionality for composing\nbatches of data suited to machine learning workflows, including data\naugmentation, class balancing, and example stratification according to a\nregression variable or data subgroup, and it further supports temporal and\nspatial recurrences over that data to facilitate work with recurrent neural\nnetworks, dynamical data, and size extensive modeling. It was designed for\nseamless integration with popular deep learning frameworks, including Caffe,\nPyTorch, and Keras, providing good performance by leveraging graphical\nprocessing units (GPUs) for computationally-intensive tasks and efficient\nmemory usage through the use of memory views over preallocated buffers.\nlibmolgrid is a free and open source project that is actively supported,\nserving the growing need in the molecular modeling community for tools that\nstreamline the process of data ingestion, representation construction, and\nprincipled machine learning model development.\n Computer science ; Workflow ; Deep learning ; Artificial intelligence ; Machine learning ; Artificial neural network ; Process (computing) ; Class (philosophy) ; Programming language ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Federico Pittino | Pietro Bonfà | Andrea Bartolini | Fabio Affinito | Luca Benini | Carlo Cavazzoni
Source:  (article, 2019-06-04)
Field: Materials Science — Materials Chemistry

Prediction of Time-to-Solution in Material Science Simulations Using Deep Learning Predicting the time to solution for massively parallel scientific codes is a complex task. The reason for this is the presence of multiple, strongly interconnected algorithms that possibly react differently to the changes in compute power, vectorization length, memory and network bandwidth and latency and I/O throughput. A reliable prediction of execution time is however of great importance to the user who wants to plan on large scale simulations or virtual screening procedures characteristic of high throughput computing. In this article we present a practical approach based on machine learning techniques to achieve very accurate predictions of the time to solution for a DFT-based material science code. We compare our results with the predictions provided by a parametrized analytical performance model showing that deep learning solutions allow for a greater accuracy without the need of domain knowledge to introduce an explicit description of the algorithms implemented in the code. Computer science ; Deep learning ; Deep time ; Artificial intelligence ; Machine learning ; Geology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Model Reduction and Neural Networks