jablonkagroup/rise_ai_scientists huggingface dataset:
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 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. Catalysis ; Rutile ; Overpotential ; Oxide ; Density functional theory ; Transition metal ; Materials science ; Oxygen evolution ; Nanotechnology ; Computer science ; Chemistry ; Chemical engineering ; Computational chemistry ; Physical chemistry ; Engineering ; Electrochemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Yuma Iwasaki | Masahiko Ishida | Masayuki Shirane
Source: Science and Technology of Advanced Materials (article, 2019-12-20)
Field: Materials Science — Materials Chemistry

Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning High-throughput experiments (HTEs) have been powerful tools to obtain many materials data. However, HTEs often require expensive equipment. Although high-throughput ab-initio calculation (HTC) has the potential to make materials big data easier to collect, HTC does not represent the actual materials data obtained by HTEs in many cases. Here we propose using a combination of simple HTEs, HTC, and machine learning to predict material properties. We demonstrate that our method enables accurate and rapid prediction of the Kerr rotation mapping of an Fe<sub>x</sub>Co<sub>y</sub>Ni<sub>1-x-y</sub> composition spread alloy. Our method has the potential to quickly predict the properties of many materials without a difficult and expensive HTE and thereby accelerate materials development. Throughput ; Computer science ; Ab initio ; Computational science ; Chemistry ; Operating system Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electronic and Structural Properties of Oxides
Authors: Helge S. Stein | Edwin Soedarmadji | Paul F. Newhouse | Dan Guevarra | John M. Gregoire
Source: Scientific Data (article, 2019-03-27)
Field: Materials Science — Materials Chemistry

Synthesis, optical imaging, and absorption spectroscopy data for 179072 metal oxides Optical absorption spectroscopy is an important materials characterization for applications such as solar energy generation. This data descriptor describes the to date (Dec 2018) largest publicly available curated materials science dataset for near infrared to near UV (UV-Vis) light absorbance, composition and processing properties of metal oxides. By supplying the complete synthesis and processing history of each of the 179072 samples from 99965 unique compositions we believe the dataset will enable the community to develop predictive models for materials, such as prediction of optical properties based on composition and processing, and ultimately serve as a benchmark dataset for continued integration of machine learning in materials science. The dataset is also a resource for identifying materials composition and synthesis to attain specific optical properties. Characterization (materials science) ; Absorbance ; Spectroscopy ; Benchmark (surveying) ; Absorption (acoustics) ; Computer science ; Materials science ; Near-infrared spectroscopy ; Nanotechnology ; Optics ; Physics ; Geology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced X-ray and CT Imaging
Authors: Alexander M. Chang | Jessica Freeze | Víctor S. Batista
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Hammett neural networks: prediction of frontier orbital energies of tungsten–benzylidyne photoredox complexes The successful application of Hammett parameters as input features for regressive machine learning models is demonstrated and applied to predict energies of frontier orbitals of highly reducing tungsten-benzylidyne complexes of the form W([triple bond, length as m-dash]CArR)L<sub>4</sub>X. Using a reference molecular framework and the <i>meta</i>- and <i>para</i>-substituent Hammett parameters of the ligands, the models predict energies of frontier orbitals that correlate with redox potentials. The regressive models capture the multivariate character of electron-donating trends as influenced by multiple substituents even for non-aryl ligands, harnessing the breadth of Hammett parameters in a generalized model. We find a tungsten catalyst with tetramethylethylenediamine (tmeda) equatorial ligands and axial methoxyl substituents that should attract significant experimental interest since it is predicted to be highly reducing when photoactivated with visible light. The utilization of Hammett parameters in this study presents a generalizable and compact representation for exploring the effects of ligand substitutions. Frontier ; Tungsten ; Artificial neural network ; Computational chemistry ; Chemistry ; Computer science ; Artificial intelligence ; Political science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Hyunsoo Kim | Yoseop Kim | Buhm Han | Jin‐Young Jang | Youngsoo Kim
Source: Journal of Proteome Research (article, 2019-07-17)
Field: Chemistry — Spectroscopy

Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic Data Deep learning (DL), a type of machine learning approach, is a powerful tool for analyzing large sets of data that are derived from biomedical sciences. However, it remains unknown whether DL is suitable for identifying contributing factors, such as biomarkers, in quantitative proteomics data. In this study, we describe an optimized DL-based analytical approach using a data set that was generated by selected reaction monitoring-mass spectrometry (SRM-MS), comprising SRM-MS data from 1008 samples for the diagnosis of pancreatic cancer, to test its classification power. Its performance was compared with that of 5 conventional multivariate and machine learning methods: random forest (RF), support vector machine (SVM), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes (NB). The DL method yielded the best classification (AUC 0.9472 for the test data set) of all approaches. We also optimized the parameters of DL individually to determine which factors were the most significant. In summary, the DL method has advantages in classifying the quantitative proteomics data of pancreatic cancer patients, and our results suggest that its implementation can improve the performance of diagnostic assays in clinical settings. Random forest ; Support vector machine ; Naive Bayes classifier ; Artificial intelligence ; Computer science ; Machine learning ; Data set ; Test set ; Logistic regression ; Algorithm ; Test data ; Multivariate statistics ; Proteomics ; Data mining ; Chemistry Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Machine Learning and Data Classification
Authors: Sean Heffernan | Nhat-Cuong Ly | Brock J. Mower | Clement Vachet | Ian J. Schwerdt | Tolga Taşdizen | Luther W. McDonald
Source: Radiochimica Acta (article, 2019-05-24)
Field: Materials Science — Materials Chemistry

Identifying surface morphological characteristics to differentiate between mixtures of U<sub>3</sub>O<sub>8</sub> synthesized from ammonium diuranate and uranyl peroxide Abstract In the present study, surface morphological differences of mixtures of triuranium octoxide (U 3 O 8 ), synthesized from uranyl peroxide (UO 4 ) and ammonium diuranate (ADU), were investigated. The purity of each sample was verified using powder X-ray diffractometry (p-XRD), and scanning electron microscopy (SEM) images were collected to identify unique morphological features. The U 3 O 8 from ADU and UO 4 was found to be unique. Qualitatively, both particles have similar features being primarily circular in shape. Using the morphological analysis of materials (MAMA) software, particle shape and size were quantified. UO 4 was found to produce U 3 O 8 particles three times the area of those produced from ADU. With the starting morphologies quantified, U 3 O 8 samples from ADU and UO 4 were physically mixed in known quantities. SEM images were collected of the mixed samples, and the MAMA software was used to quantify particle attributes. As U 3 O 8 particles from ADU were unique from UO 4 , the composition of the mixtures could be quantified using SEM imaging coupled with particle analysis. This provides a novel means of quantifying processing histories of mixtures of uranium oxides. Machine learning was also used to help further quantify characteristics in the image database through direct classification and particle segmentation using deep learning techniques based on Convolutional Neural Networks (CNN). It demonstrates that these techniques can distinguish the mixtures with high accuracy as well as showing significant differences in morphology between the mixtures. Results from this study demonstrate the power of quantitative morphological analysis for determining the processing history of nuclear materials. Uranyl ; Scanning electron microscope ; Chemistry ; Particle size ; Uranium ; Particle (ecology) ; Morphology (biology) ; Analytical Chemistry (journal) ; Mineralogy ; Materials science ; Chromatography ; Physical chemistry ; Metallurgy ; Composite material Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: Guangyan Liu | Lu Wang | Yanan Yi | Libin Sun | Li Shi | Shaopeng Ma
Source: Materials & Design (article, 2019-08-20)
Field: Materials Science — Materials Chemistry

Inverse identification of graphite damage properties under complex stress states As a key material in high-temperature gas-cooled reactors (HTGR), nuclear graphite is a type of quasi-brittle material with complex damage mechanisms. However, it is difficult to characterise the damage properties of nuclear graphite under complex stress states using conventional testing approaches or inverse methods. In this study, a hybrid identification method based on 8-node quadrilateral element digital image correlation (Q8-DIC), a double iterative finite element model updating (FEMU) technique, and artificial neural networks (ANNs) is presented to characterise the damage properties of IG11 graphite material under complex stress states. First, this method is verified using simulated tests on a ring under diametrical compression, then applied to actual mechanical testing of graphite material to evaluate its damage properties. Finally, factors affecting the evolution of damage are discussed. The results indicate that the first principal strain has the most significant effect on the damage evolution of the graphite material. Keywords: Nuclear graphite, Damage, Complex stress states, Inverse identification, Artificial neural networks Materials science ; Graphite ; Digital image correlation ; Finite element method ; Damage mechanics ; Composite material ; Brittleness ; Material properties ; Inverse problem ; Nuclear graphite ; Stress (linguistics) ; Artificial neural network ; Structural engineering ; Computer science ; Mathematics ; Mathematical analysis ; Artificial intelligence Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Fatigue and fracture mechanics
Authors: David Ochoa | Andrew F. Jarnuczak | Maja Gehre | Margaret Soucheray | Askar A. Kleefeldt | Cristina Viéitez | Anthony D. Hill | Luz García‐Alonso | Danielle L. Swaney | Juan Antonio Vizcaíno | Kyung‐Min Noh | Pedro Beltrão
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-02-05)
Field: Chemistry — Spectroscopy

The functional landscape of the human phosphoproteome Abstract Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. While tens of thousands of phosphorylation sites have been identified in human cells to date, the extent and functional importance of the phosphoproteome remains largely unknown. Here, we have analyzed 6,801 publicly available phospho-enriched mass spectrometry proteomics experiments, creating a state-of-the-art phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, 59 features indicative of proteomic, structural, regulatory or evolutionary relevance were integrated into a single functional score using machine learning. We demonstrate how this prioritization identifies regulatory phosphosites across different molecular mechanisms and pinpoint genetic susceptibilities at a genomic scale. Several novel regulatory phosphosites were experimentally validated including a role in neuronal differentiation for phosphosites present in the SWI/SNF SMARCC2 complex member. The scored reference phosphoproteome and its annotations identify the most relevant phosphorylations for a given process or disease addressing a major bottleneck in cell signaling studies. Computational biology ; Phosphorylation ; Proteomics ; Phosphoproteomics ; Bottleneck ; Biology ; Human disease ; Prioritization ; Function (biology) ; Proteome ; Protein phosphorylation ; Bioinformatics ; Computer science ; Genetics ; Gene ; Protein kinase A Advanced Proteomics Techniques and Applications ; Protein Kinase Regulation and GTPase Signaling ; Enzyme Structure and Function
Authors: Ana Sayago | Raúl González‐Domínguez | Juan Urbano | Ángeles Fernández‐Recamales
Source: LWT (article, 2019-05-07)
Field: Chemistry — Organic Chemistry

Combination of vintage and new-fashioned analytical approaches for varietal and geographical traceability of olive oils Squalene ; Campesterol ; Stigmasterol ; Unsaponifiable ; Sterol ; Fraction (chemistry) ; Food science ; Tocopherol ; Chemistry ; Phytosterol ; Mathematics ; Organic chemistry ; Chromatography ; Cholesterol ; Biochemistry ; Vitamin E Edible Oils Quality and Analysis ; Identification and Quantification in Food ; Spectroscopy and Chemometric Analyses
Authors: Yogesh A. Patil | Biswajit Sadhu | Deepak R. Boraste | Atul L. Borkar | Ganapati S. Shankarling
Source: Journal of Molecular Structure (article, 2019-10-26)
Field: Chemistry — Organic Chemistry

Utilization of Cucurbit[6]uril as an effective adsorbent for the remediation of Phthalocyanine and Procion golden yellow dyes Chemistry ; Adsorption ; Cavitand ; Langmuir adsorption model ; Phthalocyanine ; Langmuir ; Organic chemistry ; Supramolecular chemistry ; Molecule Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Simoní Da Ros | Rayany Stôcco Braido | Nathany Lisbôa de Souza e Castro | Amanda L. T. Brandão | Marcio Schwaab | José Carlos Pinto
Source: Journal of Analytical and Applied Pyrolysis (article, 2019-10-06)
Field: Materials Science — Materials Chemistry

Modelling the chemical recycling of crosslinked poly (methyl methacrylate): Kinetics of depolymerisation Thermogravimetric analysis ; Pyrolysis ; Isothermal process ; Kinetics ; Chemical engineering ; Materials science ; Ethylene glycol ; Thermogravimetry ; Methyl methacrylate ; Methacrylate ; Polymer chemistry ; Chemistry ; Organic chemistry ; Polymerization ; Polymer ; Thermodynamics ; Composite material Thermal and Kinetic Analysis ; Recycling and Waste Management Techniques ; Antimicrobial agents and applications
Authors: Mariano Campoy‐Quiles
Source: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2019-07-08)
Field: Materials Science — Materials Chemistry

Will organic thermoelectrics get hot? The generally low energy density from most heat sources-the Sun, Earth as well as most human activities-implies that solid-state thermoelectric devices are the most versatile heat harvesters since, unlike steam engines, they can be used on a small scale and at small temperature differences. In this opinion piece, we first discuss the materials requirements for the widespread use of thermoelectrics. We argue that carbon-based materials, such as conducting polymers and carbon nanotubes, are particularly suited for large area and low-temperature operation applications, as they are abundant, low-toxicity and easy to process. We combine experimentally observed macro-trends and basic thermoelectric relations to evaluate the major performance limitations of this technology thus far and propose a number of avenues to take the thermoelectric efficiency of organic materials beyond the state of the art. First, we emphasize how charge carrier mobility, rather than charge density, is currently limiting performance, and discuss how to improve mobility by exploiting anisotropy, high persistence length materials and composites with long and well-dispersed carbon nanotubes. We also show that reducing thermal conductivity could double efficiency while reducing doping requirements. Finally, we discuss several ways in which composites could further boost performance, introducing the concept of interface engineering to produce phonon stack-electron tunnel composites. This article is part of a discussion meeting issue 'Energy materials for a low carbon future'. Thermoelectric materials ; Thermoelectric effect ; Engineering physics ; Materials science ; Carbon nanotube ; Nanotechnology ; Thermal conductivity ; Carbon fibers ; Charge carrier ; Mechanical engineering ; Composite material ; Optoelectronics ; Composite number ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: H. Mehmet Taşdemir | Alpay Şahin | Ahmet F. Karabulut | Metin Gürü
Source: Cellulose Chemistry and Technology (article, 2019-06-28)
Field: Materials Science — Polymers and Plastics

PRODUCTION OF USEFUL COMPOSITE PARTICLEBOARD FROM WASTE ORANGE PEEL In this study, polymeric composite particleboard has been manufactured from orange peel, which is an abundant and promising agricultural waste in Turkey. An urea-formaldehyde (95 wt%) and phenol-formaldehyde (5% wt) mixture was used as polymeric binder and waste orange peel as a filler for the production of particleboard. The effect of polymeric binder/filler ratio on the tensile strength, limit oxygen index and water absorption capacity of the composite materials were determined. The molding temperature and pressure were set to 120 C and 2 Mton, respectively. The highest tensile strength was obtained as 15 MPa at an equal polymer/orange peel ratio. Both mechanical strength and LOI (limiting oxygen index) values increased with increasing polymeric binder amount in the particleboard. However, water absorption capacity values decreased from 0.35 g water/g material to 0.16 g water/g material, with increasing amount of the polymeric binder. Considering the properties of the developed polymeric composite material, it can be concluded that orange peel waste can be used as a filler to replace wood-based materials in the production of particleboard. Ultimate tensile strength ; Absorption of water ; Composite number ; Composite material ; Limiting oxygen index ; Chemistry ; Filler (materials) ; Formaldehyde ; Materials science ; Pulp and paper industry ; Pyrolysis ; Organic chemistry Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials
Authors: Katarzyna Krukiewicz | Agnieszka Kowalik | Dominika Czerwińska-Główka | Manus Biggs
Source: Electrochimica Acta (article, 2019-02-07)
Field: Materials Science — Polymers and Plastics

Electrodeposited poly(3,4-ethylenedioxypyrrole) films as neural interfaces: Cytocompatibility and electrochemical studies PEDOT:PSS ; Materials science ; Biocompatibility ; Polystyrene sulfonate ; Conductive polymer ; Poly(3,4-ethylenedioxythiophene) ; Electrochemistry ; Chemical engineering ; Polymer ; Polypyrrole ; Dopant ; Electrode ; Nanotechnology ; Doping ; Polymerization ; Chemistry ; Composite material ; Optoelectronics Conducting polymers and applications ; Neuroscience and Neural Engineering ; Advanced Sensor and Energy Harvesting Materials
Authors: Leonardo Medrano Sandonas | Rafael Gutiérrez | Alessandro Pecchia | Alexander Croy | Gianaurelio Cuniberti
Source: Entropy (review, 2019-07-27)
Field: Materials Science — Materials Chemistry

Quantum Phonon Transport in Nanomaterials: Combining Atomistic with Non-Equilibrium Green’s Function Techniques A crucial goal for increasing thermal energy harvesting will be to progress towards atomistic design strategies for smart nanodevices and nanomaterials. This requires the combination of computationally efficient atomistic methodologies with quantum transport based approaches. Here, we review our recent work on this problem, by presenting selected applications of the PHONON tool to the description of phonon transport in nanostructured materials. The PHONON tool is a module developed as part of the Density-Functional Tight-Binding (DFTB) software platform. We discuss the anisotropic phonon band structure of selected puckered two-dimensional materials, helical and horizontal doping effects in the phonon thermal conductivity of boron nitride-carbon heteronanotubes, phonon filtering in molecular junctions, and a novel computational methodology to investigate time-dependent phonon transport at the atomistic level. These examples illustrate the versatility of our implementation of phonon transport in combination with density functional-based methods to address specific nanoscale functionalities, thus potentially allowing for designing novel thermal devices. Phonon ; Thermal conductivity ; Materials science ; Nanomaterials ; Molecular dynamics ; Nanotechnology ; Quantum ; Density functional theory ; Statistical physics ; Computer science ; Condensed matter physics ; Physics ; Quantum mechanics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science