jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gaëlle Delaizir | A. Piarristeguy | A. Pradel | Olivier Masson | Assil Bouzid
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Short range order and network connectivity in amorphous AsTe<sub>3</sub>: a first principles, machine learning, and XRD study The atomic scale structure of amorphous AsTe<sub>3</sub> is investigated through X-ray diffraction, first-principles molecular dynamics (FPMD), and machine learning interatomic potentials (ML-GAP) obtained by exploiting the ab initio data. We obtain good agreement between the measured and modelled diffraction patterns. Our FPMD results show that As and Te obey the 8-N rule with average coordination numbers of 3 and 2, respectively. We find that small fractions of under and over coordinated As and Te atoms are present in the amorphous phase with about 6% (FPMD), and 13% (ML-GAP) of 3-fold Te. As is found at the center of pyramidal structures predominantly linked through Te<sub>n</sub> chains rather than rings. Despite the low As concentration in AsTe<sub>3</sub>, its local environment features a very high chemical disorder that manifests through the occurrence of homopolar bonds including at least 57% of As atoms. Homopolar motor ; Amorphous solid ; Diffraction ; Coordination number ; Ab initio ; Atomic units ; Molecular dynamics ; Materials science ; Crystallography ; Chemical physics ; Molecular physics ; Physics ; Chemistry ; Computational chemistry ; Optics ; Quantum mechanics ; Ion Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Crystal Structures and Properties
Authors: Fabrizio, Alberto | Briling, Ksenia R. | Girardier, David D. | Corminboeuf, Clemence
Source: Open MIND (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation The on-top pair density [Π(r)] is a local quantum chemical property, which reflects the probability of two electrons of any spin to occupy the same position in space. Simplest quantity related to the two-particles density matrix, the on-top pair density is a powerful indicator of electron correlation effects and, as such, it has been extensively used to combine density functional theory and multireference wavefunction theory. The widespread application of Π(r) is currently hindered by the need for post-Hartree-Fock or multireference computations for its accurate evaluation. In this work, we propose the construction of a machine learning model capable of predicting the CASSCF-quality on-top pair density of a molecule only from its structure and composition. Our model, trained on the GDB11-AD-3165 database, is able to predict with minimal error the on-top pair density of organic molecules bypassing completely the need for ab-initio computations. The accuracy of the regression is demonstrated using the on-top ratio as a visual metric of electron correlation effects and bond-breaking in real-space. In addition, we report the construction of a specialized basis set, built to fit the on-top pair density in a single, atom-centered expansion. This basis, cornerstone of the regression, could be potentially used also in the same spirit of the resolution-of-the-identity approximation for the electron density. Electron density ; Wave function ; Electronic correlation ; Density functional theory ; Statistical physics ; Quantum mechanics ; Chemistry ; Density matrix ; Computation ; Physics ; Electron ; Mathematics ; Quantum ; Algorithm Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Can Eyüpoğlu | Şeyda Eyüpoğlu | Nigar Merdan
Source: Journal of the Textile Institute (article, 2020-09-15)
Field: Materials Science — Polymers and Plastics

A multilayer perceptron artificial neural network model for estimation of ultraviolet protection properties of polyester microfiber fabric In this study, the use of a polyester fabric produced from microfibers as a siding material for construction industry was investigated. In this context, an ultraviolet (UV) absorber was applied to the polyester fabric samples with and without dyeing process. These samples were treated with UV absorber at 1–4% concentration and then an accelerated aging test was carried out. The UV absorbance capacity of the samples was investigated before and after the accelerated aging test. Furthermore, the effects of dyeing process on UV absorbance capacity of samples were analyzed. Afterwards, a multilayer perceptron artificial neural network (MLP-ANN) model was proposed and utilized to predict the UV protection properties which are UV protection factor, UV-A and UV-B in polyester microfiber fabric. The MLP-ANN based results demonstrate that the regression (R) values are almost 1 for all UV protection properties. Accordingly, it was seen that the proposed MLP-ANN is correctly modeled and the prediction of UV protection properties is successfully performed. Microfiber ; Polyester ; Dyeing ; Materials science ; Absorbance ; Artificial neural network ; Ultraviolet ; Multilayer perceptron ; Composite material ; Context (archaeology) ; Biological system ; Computer science ; Chromatography ; Chemistry ; Machine learning ; Optoelectronics Textile materials and evaluations ; Color Science and Applications ; Industrial Vision Systems and Defect Detection
Authors: Kookjin Lee | Sangjin Nam | Hyojun Kim | Dae‐Young Jeon | Dong-Ha Shin | Hyeong‐Gyun Lim | Chul Min Kim | Doyoon Kim | Yeonsu Kim | Sang‐Hoon Byeon | Gyu‐Tae Kim
Source: Advanced Theory and Simulations (article, 2020-06-05)
Field: Materials Science — Materials Chemistry

Detection and Accurate Classification of Mixed Gases Using Machine Learning with Impedance Data Abstract An inexpensive and effective technique based on machine learning (ML) algorithms with impedance characterization to sense and classify mixed gases is presented. Specifically, this method demonstrates that ML algorithms can distinguish hidden and valuable feature information such as different gas molecules from surface‐charged activated carbon fibers. The feature information used for ML is obtained by measuring the impedance and fitting the measured values to an equivalent circuit model. The mixed gases are classified using such feature information to train various automatic classifiers. The collected data consist of the resistances and capacitances extracted from best fitting results in Cole–Cole plots, and they are 5D vectors. The data processed with unsupervised learning are clustered, evaluated with Silhouette scores, and then the unique hidden patterns of individual gases in the mixed gases are obtained. When the supervised ML algorithm, k ‐nearest neighbor classifier, is used for the analytical features, all combinations of gases have 94% classification accuracy, demonstrating the superiority of the proposed technique. Pattern recognition (psychology) ; Silhouette ; Artificial intelligence ; Computer science ; Feature (linguistics) ; Classifier (UML) ; k-nearest neighbors algorithm ; Unsupervised learning ; Electrical impedance ; Machine learning ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Chemical Sensor Technologies
Authors: Shuling Xiong | Luohan Wang
Source: Advances in Materials Science and Engineering (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Research Progress and Development Trends of Materials Genome Technology Materials genome is a subversive frontier technology emerging in the field of international materials in recent years and also a propeller for the development of new materials. It brings fundamental changes to the traditional material research mode, aiming to accelerate the research and development of new materials and reduce costs, so as to support the development of electronic information, energy and environmental protection, aerospace, and other industries. In this paper, we introduce the strategic significance, national layout, and methods of materials genome technology and emphatically introduce the design idea and development status of materials database method. Then we summarize the development trends of materials genome and put forward suggestions for its future research, aiming to provide references for the development direction of materials genome technology in various countries, especially in developing countries. Aerospace ; Frontier ; Nanotechnology ; Systems engineering ; Materials science ; Engineering ; Aerospace engineering ; Political science Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; X-ray Diffraction in Crystallography
Authors: Kyle Mills | Corneel Casert | Isaac Tamblyn
Source: The Journal of Physical Chemistry C (article, 2020-09-24)
Field: Materials Science — Materials Chemistry

Adversarial Generation of Mesoscale Surfaces from Small-Scale Chemical Motifs We demonstrate the use of a regressive upscaling generative adversarial network (RUGAN) as an effective way to sample state space for hexagonal porous graphene sheets. The RUGAN can, after being trained on a set of small-scale examples, generate new, energetically relevant microstates (atomic configurations). The RUGAN can generate configurations across a continuum of total energy values and produce configurations at requested energy values. The microstates produced respect periodic boundary conditions, and importantly, the fully convolutional nature of the generator allows the generation of arbitrarily large microstates, after being trained on only a small-scale data set. Generative grammar ; Scale (ratio) ; Generator (circuit theory) ; Statistical physics ; Boundary (topology) ; Set (abstract data type) ; Computer science ; Artificial intelligence ; Mathematics ; Physics ; Mathematical analysis ; Power (physics) ; Quantum mechanics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Block Copolymer Self-Assembly
Authors: Seyyed Ali Davari | Anthony S. Wexler
Source: Atmospheric measurement techniques (article, 2020-10-09)
Field: Chemistry — Analytical Chemistry

Quantification of toxic metals using machine learning techniques and spark emission spectroscopy The United States Environmental Protection Agency (US EPA) list of hazardous air pollutants (HAPs) includes toxic metal suspected or associated with development of cancer. Traditional techniques for detecting and quantifying toxic metals in the atmosphere are either not real time, hindering identification of sources, or limited by instrument costs. Spark emission spectroscopy is a promising and cost-effective technique that can be used for analyzing toxic metals in real time. Here, we have developed a cost-effective spark emission spectroscopy system to quantify the concentration of toxic metals targeted by the US EPA. Specifically, Cr, Cu, Ni, and Pb solutions were diluted and deposited on the ground electrode of the spark emission system. The least absolute shrinkage and selection operator (LASSO) was optimized and employed to detect useful features from the spark-generated plasma emissions. The optimized model was able to detect atomic emission lines along with other features to build a regression model that predicts the concentration of toxic metals from the observed spectra. The limits of detections (LODs) were estimated using the detected features and compared to the traditional single-feature approach. LASSO is capable of detecting highly sensitive features in the input spectrum; however, for some toxic metals the single-feature LOD marginally outperforms LASSO LOD. The combination of low-cost instruments with advanced machine learning techniques for data analysis could pave the path forward for data-driven solutions to costly measurements. SPARK (programming language) ; Lasso (programming language) ; Hazardous air pollutants ; Spectroscopy ; Environmental science ; Computer science ; Atomic emission spectroscopy ; Feature selection ; Emission spectrum ; Process engineering ; Chemistry ; Artificial intelligence ; Pollutant ; Spectral line ; Physics ; Inductively coupled plasma ; Engineering ; Plasma Analytical chemistry methods development ; Laser-induced spectroscopy and plasma ; Electrochemical Analysis and Applications
Authors: Matteo Cialone | Aliona Nicolenco | Shauna Robbennolt | Enric Menéndez | Gemma Rius | Jordi Sort
Source: Advanced Materials Interfaces (article, 2020-11-20)
Field: Materials Science — Materials Chemistry

Voltage‐Induced ON Switching of Magnetism in Ordered Arrays of Non‐Ferrimagnetic Nanoporous Iron Oxide Microdisks Abstract Tailoring the magnetic properties of ordered arrays of patterned structures usually requires stringent control of their size, pitch, microstructure, and composition. Here, a fundamentally different approach to manipulate the magnetic behavior of lithographed microdisks, based on the application of electrical voltage, is demonstrated. First, highly porous iron oxide films with virtually no magnetic response (OFF state) are grown by sol–gel chemistry. Subsequently, arrays of microdisks (8 µm in diameter) are obtained combining lithography with wet chemical etching processes. Electrolyte‐gating (with an anhydrous electrolyte) is then employed to induce a tunable (i.e., “on‐demand”) ferromagnetic response in these disks (OFF–ON switching of magnetism) at room temperature. The changes in magnetic properties are attributed to magnetoelectrically‐driven oxygen ion migration, which is enhanced due to nanoporosity. This causes partial reduction of the oxide phases to metallic Fe. The effect can be considerably reversed by applying voltage of opposite polarity. These results are appealing for diverse technological applications that require the use of patterned structures with easily tunable magnetic properties, such as magnetic micro‐electro‐mechanical systems, microfluidic, and lab‐on‐a‐chip platforms for biomedical therapies and, ultimately, energy‐efficient magnetic memories or neuromorphic computing. Materials science ; Magnetism ; Ferrimagnetism ; Nanoporous ; Nanotechnology ; Oxide ; Electrolyte ; Ferromagnetism ; Optoelectronics ; Electrode ; Magnetization ; Magnetic field ; Condensed matter physics Nanoporous metals and alloys ; Anodic Oxide Films and Nanostructures ; Magnetic properties of thin films
Authors: Marta Guembe-García | Saúl Vallejos | Israel Carreira‐Barral | Saturnino Ibeas | Félix C. García | Victoria Santaolalla-García | Natalia Moradillo-Renuncio | José M. García
Source: Reactive and Functional Polymers (article, 2020-07-03)
Field: Chemistry — Spectroscopy

Zn(II) detection in biological samples with a smart sensory polymer Aqueous solution ; Monomer ; Sensory system ; Polymer ; Context (archaeology) ; Chemistry ; Zinc ; Materials science ; Organic chemistry Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Advanced Chemical Sensor Technologies
Authors: Chence Shi | Minkai Xu | Hongyu Guo | Ming Zhang | Jian Tang
Source: NPARC (article, 2020-07-12)
Field: Materials Science — Materials Chemistry

A Graph to Graphs Framework for Retrosynthesis Prediction A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computationally expensive and also suffer from the problem of coverage. In this paper, we propose a novel template-free approach called G2Gs by transforming a target molecular graph into a set of reactant molecular graphs. G2Gs first splits the target molecular graph into a set of synthons by identifying the reaction centers, and then translates the synthons to the final reactant graphs via a variational graph translation framework. Experimental results show that G2Gs significantly outperforms existing template-free approaches by up to 63% in terms of the top-1 accuracy and achieves a performance close to that of state-of-the-art template-based approaches, but does not require domain knowledge and is much more scalable. Retrosynthetic analysis ; Template ; Computer science ; Molecular graph ; Scalability ; Synthon ; Theoretical computer science ; Graph ; Set (abstract data type) ; Artificial intelligence ; Algorithm ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Volodymyr Tkach | Marta V. Kushnir | Sílvio De Oliveira | Yana G. Ivanushko | Alla Velyka | Anzhelika Molodianu | Petró Yagodynets | Жолт Кормош | Lucinda V. Reis | Olga V. Luganska | Каріна Паламарек | Yuliia Bredikhina | Y Kumar | V Vashistha | D Das | X Long | M Sant'anna | S Carvalho | A Gevaerd | J Silva | E Santos | I Carregosa | A Wisniewski | L Marcolino-Junior | M Bergamini | E Sussuchi | F Laghrib | M Bakasse | S Lahrich | M El Mhammedi | B Prez-Fernndez | A Costa-Garca | A Muiz | H Xiang | Q Cai | Y Li | Z Zhang | L Cao | K Li | H Yang | M Arregui | A Fernndez | Y Paz-Snchez | Santana | S Sacchini | E Sierra | M Arbelo | Y De Quirs | W; Pyon | D Gray | C Barnes | B Lochocki | T Morrema | F Ariese | J Hoozemans | J De Boer | B Lellis | C Fvaro-Polonio | J Pamphile | J Polonio | S Benkhaya | S Mrabet | A El Harfi | T Ishchi | G Sibi | M Iammarino | A Mentana | D Centonze | C Palermo | M Mangiacotti | A Chiaravalle | D Clofent | M De Homdedeu | M Muoz-Esquerre | M Cruz | X Muoz | S Andoh | K Nyave | B Asamoah | B Kanyathare | T Nuutinen | C Mingle | K.-E Peiponen | M Roussey | S Homaeigohar | M Reza | M Hasan | M Kamruzzaman | M Hossain | M Zubair | L Bari | M Abedin | K Khalid-Bin-Ferdaus | K Haque | K Islam | M Ahmed | A Sebaei | M Youssif
Source: Letters in Applied NanoBioScience (article, 2020-08-15)
Field: Chemistry — Analytical Chemistry

Electrochemical Determination of Sudan Dyes and Two Manner to Realize it: a Theoretical Investigation In this work, a general theoretical description of the electrochemical determination of Sudan dyes by cathodic and anodic route has been made. Two mathematical models have been developed for each case, being, afterward, analyzed by means of linear stability theory and bifurcation analysis. It has been shown that in both cases, the systems are efficient from the electroanalytical point of view. The possibility for the oscillatory and monotonic instability has also been verified. Electrochemistry ; Monotonic function ; Cathodic protection ; Instability ; Bifurcation theory ; Point (geometry) ; Bifurcation ; Anode ; Work (physics) ; Computer science ; Materials science ; Applied mathematics ; Mathematics ; Chemistry ; Mechanics ; Thermodynamics ; Mathematical analysis ; Physics ; Electrode ; Physical chemistry ; Nonlinear system ; Geometry Dye analysis and toxicity ; Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors
Authors: Morad Biagooi | SeyedEhsan Nedaaee Oskoee
Source: Scientific Reports (article, 2020-04-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The effects of slit-pore geometry on capacitive properties: a molecular dynamics study Ionic-liquids (IL) inside conductive porous media can be used to make electrical energy storage units. Many parameters such as the shape of the pores and the type of IL affect the storage performance. In this work, a simple IL model inside two geometrically different slit-pores is simulated and their capacitive properties are measured. The pores were of finite length, one of them was linear and the other had a convex extra space in the center. The molecular dynamics simulations are done for two, qualitatively, low and high molarities. The pores have been simulated for both initially filled or empty conditions. Differential capacitance, induced charge density, and IL dynamics are calculated for all of the systems. Molecular dynamics ; Materials science ; Capacitance ; Capacitive sensing ; Work (physics) ; Chemical physics ; Porous medium ; Slit ; Dynamics (music) ; Charge density ; Charge (physics) ; Porosity ; Chemistry ; Physics ; Optics ; Composite material ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Computer science Supercapacitor Materials and Fabrication ; Ionic liquids properties and applications ; Advanced Battery Materials and Technologies
Authors: Godliver Owomugisha | Ephraim Nuwamanya | John A. Quinn | Michael Biehl | Ernest Mwebaze
Source:  (article, 2020-01-07)
Field: Chemistry — Analytical Chemistry

Early detection of plant diseases using spectral data Early detection of crop disease is an essential step in food security. Usually, the detection becomes possible in a stage where disease symptoms are already visible on the aerial part of the plant. However, once the disease has manifested in different parts of the plant, little can be done to salvage the situation. Here, we suggest that the use of visible and near infrared spectral information facilitates disease detection in cassava crops before symptoms can be seen by the human eye. To test this hypothesis, we grow cassava plants in a screen house where they are inoculated with disease viruses. We monitor the plants over time collecting both spectra and plant tissue for wet chemistry analysis. Our results demonstrate that suitably trained classifiers are indeed able to detect cassava diseases. Specifically, we consider Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) applied to original spectra and, alternatively, in combination with dimension reduction by Principal Component Analysis (PCA). We show that successful detection is possible shortly after the infection can be confirmed by wet lab chemistry, several weeks before symptoms manifest on the plants. Principal component analysis ; Computer science ; Learning vector quantization ; Crop ; Plant disease ; Artificial intelligence ; Pattern recognition (psychology) ; Disease ; Biological system ; Biology ; Horticulture ; Vector quantization ; Agronomy ; Biotechnology ; Medicine ; Pathology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Pernille Rose Jensen | Sebastián Meier
Source: Chemical Communications (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Catalytic cycle of carbohydrate dehydration by Lewis acids: structures and rates from synergism of conventional and DNP NMR Lewis acids play key roles in many chemical reactions. Structural and functional (kinetic) detail in Lewis acid catalysed fructose conversion are derived herein by the combined use of conventional and dissolution dynamic nuclear polarization (D-DNP) NMR. Structural information obtained with D-DNP NMR was used to identify conditions that stabilize an elusive initial intermediate and to determine its chemical structure. Carbohydrate dehydration through this intermediate had been predicted computationally. Complementary kinetic NMR assays yielded rate constants spanning three orders of magnitude for the three biggest energy barriers in the catalytic cycle. Dehydration ; Catalysis ; Chemistry ; Carbohydrate ; Lewis acids and bases ; Organic chemistry ; Biochemistry Advanced NMR Techniques and Applications ; Catalysis for Biomass Conversion ; Zeolite Catalysis and Synthesis
Authors: Mohamed Ali al‐Badri | Paul Smith | Robert C. Sinclair | Khuloud T. Al‐Jamal | Christian D. Lorenz
Source: Carbon (article, 2020-12-16)
Field: Materials Science — Materials Chemistry

Accurate large scale modelling of graphene oxide: Ion trapping and chaotropic potential at the interface Graphene ; Bespoke ; Molecular dynamics ; Chemical physics ; Oxide ; Materials science ; Nanotechnology ; Adsorption ; Hydrogen bond ; Ion ; Electronic structure ; Computational chemistry ; Chemistry ; Molecule ; Physical chemistry ; Organic chemistry Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Graphene and Nanomaterials Applications