jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhufeng Hou | Thaer M. Dieb | Koji Tsuda
Source: Institutional Repositories DataBase (IRDB) (article, 2018-06-28)
Field: Materials Science — Materials Chemistry

Structure prediction of boron-doped graphene by machine learning Heteroatom doping has endowed graphene with manifold aspects of material properties and boosted its applications. The atomic structure determination of doped graphene is vital to understand its material properties. Motivated by the recently synthesized boron-doped graphene with relatively high concentration, here we employ machine learning methods to search the most stable structures of doped boron atoms in graphene, in conjunction with the atomistic simulations. From the determined stable structures, we find that in the free-standing pristine graphene, the doped boron atoms energetically prefer to substitute for the carbon atoms at different sublattice sites and that the para configuration of boron-boron pair is dominant in the cases of high boron concentrations. The boron doping can increase the work function of graphene by 0.7 eV for a boron content higher than 3.1% Graphene ; Support vector machine ; Feature (linguistics) ; Pattern recognition (psychology) Graphene research and applications ; Machine Learning in Materials Science ; Graphene and Nanomaterials Applications
Authors: Võ Thành Công
Source: Vietnam Journal of Science and Technology/Science and Technology (article, 2018-03-22)
Field: Chemistry — Spectroscopy

CALCULATION OF ENVIRONMENTAL PROPERTIES FOR ORGANIC COMPOUNDS USING QUANTITATIVE STRUCTURE-SOLUBILITY RELATIONSHIPS The solubility of organic compounds in water was related to the environmental behaviors. In this work, the solubility values of 27 organic compounds were calculated by using the different molecular descriptors. The quantitative structure-solubility relationships (QSSRs) were constructed by incorporating the multivariable technique and the genetic algorithm. The important molecular descriptors such as logP, SsCH3_acnt, ABSQ, nelem, nrings, SHBa, Gmax, Gmin, Xvp6, and Xvpc4 were selected to construct the linear models QSSRs with the genetic algorithm. The best four-variable linear model QSSR was obtained from those descriptors. The quality of QSSR linear model indicated in statistical values R2training of 96.60, standard error of estimation, SE of 0.2961, F-statistic of 156.0, P-value of 0.0, R2test of 95.02, and RSS value of 2.823. The architecture of neural network I(4)-HL(4)-O(1) with R2training of 99.03 was constructed by the molecular descriptors in the four-variable linear model. The predicted solubility values of organic substances in test group resulting from these models are in good agreement with those from literature. Solubility ; Quantitative structure–activity relationship ; Molecular descriptor ; Biological system ; Chemistry ; Mathematics ; Thermodynamics ; Organic chemistry ; Stereochemistry ; Physics Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2018-02-19)
Field: Materials Science — Materials Chemistry

Machine learning predicts inorganic properties … Machine learning, in which computers train on large data sets to make predictions, can be a fast way to find promising molecules for various applications, but it’s only as good as the data it trains on. A new strategy could make the method more useful for identifying leads among inorganic complexes, for which reliable data can be harder to come by (J. Phys. Chem. Lett. 2018, DOI: 10.1021/acs.jpclett.8b00170). Heather J. Kulik and colleagues at Massachusetts Institute of Technology wanted to use machine learning to find new inorganic compounds with a small energy gap between their high- and low-electron-spin states. Because light or heat can boost these molecules, called spin-crossover complexes (SCCs), into a high-spin state, they could be useful as switches and sensors. Finding new SCCs computationally presents a particular challenge for machine-learning models. Spin states and other properties of inorganic molecules are complicated, and less data is available to Computer science ; Artificial intelligence ; Psychology ; Machine learning X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2018-02-19)
Field: Materials Science — Materials Chemistry

Machine learning predicts inorganic properties … Machine learning, in which computers train on large data sets to make predictions, can be a fast way to find promising molecules for various applications, but it’s only as good as the data it trains on. A new strategy could make the method more useful for identifying leads among inorganic complexes, for which reliable data can be harder to come by (J. Phys. Chem. Lett. 2018, DOI: 10.1021/acs.jpclett.8b00170). Heather J. Kulik and colleagues at Massachusetts Institute of Technology wanted to use machine learning to find new inorganic compounds with a small energy gap between their high- and low-electron-spin states. Because light or heat can boost these molecules, called spin-crossover complexes (SCCs), into a high-spin state, they could be useful as switches and sensors. Finding new SCCs computationally presents a particular challenge for machine-learning models. Spin states and other properties of inorganic molecules are complicated, and less data is available to Computer science X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Sally C. Johnson
Source: Scilight (article, 2018-03-26)
Field: Materials Science — Materials Chemistry

AI characterizes the interface between zinc oxide and water Machine learning technique interprets and resolves the vibrational fingerprints of molecules to characterize the interface of metal oxides and water. Interface (matter) ; Zinc ; Oxide ; Computer science ; Metal ; Materials science ; Molecule ; Chemistry ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Water Quality Monitoring and Analysis
Authors: John F. Wambaugh | Kristin Isaacs | Katherine A. Phillips | Caroline Ring | Jon A. Arnot | Deborah H. Bennett | Peter Egeghy | Peter Fantke | Lei Huang | Olivier Jolliet | Hyeong‐Moo Shin | John N. Westgate | R. Woodrow Setzer
Source: ISEE Conference Abstracts (article, 2018-09-24)
Field: Chemistry — Physical and Theoretical Chemistry

Systematic Empirical Evaluation of Models for Risk Prioritization Prioritizing the potential risk posed to human health by the thousands of chemicals that occur in commerce and the environment requires tools that can estimate exposure from limited information. High-throughput exposure (HTE) models exist to predict exposure via specific pathways. We consider an &amp;#8220;exposure pathway&amp;#8221; to include a chemical source, interaction with the environment, and a receptor (such as a person). Data for identifying the relevant pathways for chemicals in a high throughput manner are limited. Both expert opinion and conservative assumptions (all chemicals via all pathways) have drawbacks. Here we present a consensus HTE model based on 15 models and databases drawn from a collaboration involving five universities, a research consultancy, and the U.S. EPA. Machine learning predicts the probability that a chemical is associated with four assumed pathways: near-field (residential), dietary, far-field industrial, and far-field pesticide. On a pathway basis, we examine inferred chemical intake rates for the median U.S. population, from the National Health and Nutrition Examination Survey (NHANES). The intake rate for each pathway is either higher (residential, dietary) or lower (pesticidal, industrial) than the average. We use multivariate linear regression to evaluate the predictive ability of each model and database to raise or lower exposure with respect to the pathway averages. We can explain ~70% of the chemical-to-chemical variance in NHANES using the consensus model. We extrapolate model predictions to other chemicals by assuming that chemicals without biomonitoring data will have similar intake rates to those within NHANES. Predictions with quantified confidence intervals may allow risk-based prioritization using the margins between putative bioactive doses and intake rate predictions. This abstract may not reflect U.S. EPA policy. National Health and Nutrition Examination Survey ; Variance (accounting) ; Population ; Multivariate statistics ; Prioritization ; Environmental health ; Biomonitoring ; Computer science ; Risk assessment ; Machine learning ; Medicine ; Biology ; Business ; Management science ; Engineering ; Ecology History and advancements in chemistry ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Mehrdad Shahpar | Sharmin Esmaeilpoor
Source: Asian Journal of Green Chemistry (article, 2018-04-01)
Field: Chemistry — Spectroscopy

Quantitative structure-retention relationships applied to chromatographic retention of ecotoxicity of anilines and phenols Aniline, phenol, and their derivatives are widely used in industrial chemicals that consequently have a high potential for environmental pollution. Genetic algorithm and partial least square (GA-PLS), kernel partial least square (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between chromatographic retention (log k) and descriptors for modelling the toxicity to fathead minnows of anilines and phenols. Descriptors of GA-PLS model were selected as inputs in L-M ANN model. The described model does not require experimental parameters and potentially provides useful prediction for log k of new compounds. Finally a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. The stability and prediction ability of L-M ANN model was validated using external test set techniques. Phenols ; Ecotoxicity ; Correlation coefficient ; Kernel (algebra) ; Quantitative structure–activity relationship ; Artificial neural network ; Aniline ; Genetic algorithm ; Chemistry ; Phenol ; Partial least squares regression ; Molecular descriptor ; Biological system ; Mathematics ; Artificial intelligence ; Computer science ; Statistics ; Machine learning ; Organic chemistry ; Stereochemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Analytical Methods in Pharmaceuticals
Authors: V. Homola
Source: Digital Repository (National Repository of Grey Literature) (article, 2018-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Preparation of ppHMDSO Thin Films in Capacitively Coupled RF Glow Discharges under Dusty Plasma Conditions The deposition of organosilicone thin films from mixture of hexamethyldisiloxane (HMDSO) and oxygen by using capacitively coupled R.F. glow discharges under dusty plasma conditions was investigated. High resolution topography and mechanical property maps of the prepared films were acquired by using atomic force microscopy techniques. The chemical bond and composition of the deposited films were analyzed by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The mechanical properties of the films were studied using quasistatic as well as dynamic nanoindentation tests and their surface free energies were evaluated by means of contact angle measuring technique using several testing liquids exhibiting various surface tensions. The thermal stability of the films was studied using thermal desorption spectroscopy. Neural network modelling was used to study the effect of plasma parameters on the hardness of ppHMDSO films Hexamethyldisiloxane ; Thin film ; Analytical Chemistry (journal) ; X-ray photoelectron spectroscopy ; Nanoindentation ; Contact angle ; Glow discharge ; Spectroscopy ; Plasma Surface Modification and Superhydrophobicity ; Diamond and Carbon-based Materials Research ; Copper Interconnects and Reliability
Authors: Guido Rademaker | Yoann Blancquaert | Lucie Mourier | Thibault Labbaye | Nivea Figueiró | Francisco Sanchez | Roy Koret | Jonathan Pradelles | S. Landis | S. Rey | Ronny Haupt | Barak Bringoltz | Michael Shifrin | Daniel Kandel | Avron Ger | Matthew Sendelbach | Shay Wolfling | Laurent Pain
Source:  (article, 2018-09-19)
Field: Materials Science — Surfaces, Coatings and Films

Feasibility of monitoring a multiple e-beam tool using scatterometry and machine learning: stitching error detection Multiple electron beam direct write lithography is an emerging technology promising to address new markets, such as truly unique chips for security applications. The tool under consideration, the Mapper FLX-1200, exposes long 2.2 &mu;m-wide zones called stripes by groups of 49 beams. The critical dimensions inside and the registration errors between the stripes, called stitching, are controlled by internal tool metrology. Additionally, there is great need for on-wafer metrology of critical dimension and stitching to monitor Mapper tool performance and validate the internal metrology. Optical Critical Dimension (OCD) metrology is a workhorse technique for various semiconductor manufacturing tools, such as deposition, etching, chemical-mechanical polishing and lithography machines. Previous works have shown the feasibility to measure the critical dimension of non-uniform targets by introducing an effective CD and shown that the non-uniformity can be quantified by a machine learning approach. This paper seeks to extend the previous work and presents a preliminary feasibility study to monitor stitching errors by measuring on a scatterometry tool with multiple optical channels. A wafer with OCD targets that mimic the various lithographic errors typical to the Mapper technology was created by variable shaped beam (VSB) e-beam lithography. The lithography process has been carefully tuned to minimize optically active systematic errors such as critical dimension gradients. The OCD targets contain horizontal and vertical gratings with a pitch of 100 nm and a nominal CD of 50 nm, and contain various stitching error types such as displacement in X, Y and diagonal gratings. Sensitivity to all stitching types has been shown. The DX targets showed non-linearity with respect to error size and typically were a factor of 3 less sensitive than the promising performance of DY targets. A similar performance difference has seen in nominally identical diagonal gratings exposed with vertical and horizontal lines, suggesting that OCD metrology for DX cannot be fully characterized due to lithography errors in gratings with vertical lines. Image stitching ; Computer science ; Artificial intelligence ; Computer vision Optical Coatings and Gratings
Authors: Hani. A. Al Rawashdeh | Asaad Sattar Manduf Al- Hasan | Kamis Issa | Jehad bin yamin | Ubeidulla F. Al‐Qawabeha
Source: Journals & Books Hosting (International Knowledge Sharing Platform) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Modelling of Fatigue Failure for Plasma Coated Members Using Artificial Intelligence Technique Coating materials in form of powder such as Magnesium Zirconate, Aluminum Bronze and Molybdenum were mixed in different portions and sprayed on steel specimen to find the fatigue properties of steel using plasma technique. The effect of coating mixture on the number of cycles needed for failure under different loads was done experimentally. A cyclic loading was applied to it repeatedly until failure occurs. The results were compared with those for the same specimen without coating. The results were then modelled using Artificial Intelligence Technique then optimized for maximum cycles of coated substance failure. The results showed significant improvement to the specimen’s resistance to failure with coating. Further, models were developed out of the experimental data and tested for accuracy and gave satisfactory results. However, the time consumed by the GA method was greater than that consumed by the same software for the ANN model development.Also, sensitivity analysis showed that the key effect for the variables studied was for the load while the least effect was for the Molybdenum mixture. On the other hand, using GA method, the importance of variables was maximum for the load and minimum for Magnesium oxide and Zirconate oxide mixture Further, using the correlation method, there was strong negative (i.e. inverse relationship) correlation between the number of cycles and load and weak with Magnesium oxide and Zirconate oxide mixture while strong positive correlation was shown with Molybdenum and least positive for Aluminum Copper Balance. Keywords : Artificial neural network, modeling, Plasma coating, fatigue failure. Materials science ; Coating ; Molybdenum ; Magnesium ; Oxide ; Plasma ; Composite material ; Metallurgy Metal Alloys Wear and Properties ; Surface Treatment and Coatings ; Tribology and Wear Analysis
Authors: Jan H. Orkisz
Source: theses.fr (ABES) (preprint, 2018-10-22)
Field: Chemistry — Spectroscopy

Understanding the structure of molecular clouds : multi-line wide-field imaging of Orion B The new generation of wide-bandwidth high-resolution receivers turns almost any radio observation into a spectral survey. In the case of wide-field imaging of the interstellar medium, such a wealth of data provides new diagnostic tools, but also poses new challenges in terms of data processing and analysis. The ORION-B project aims at observing 5 square degrees of the OB molecular cloud, or about half of the cloud's surface, over the entire 3mm band. The emission of tens of molecular tracers has been mapped, including CO isotopologues, HCO, HCN, HNC, N2H+, methanol, SO, CN...Having access to spatially resolved maps from many molecular species enables us to identify the best tracers of the gas density and illumination. Machine learning techniques have also been applied to these maps, in order to segment the molecular cloud into typical regions based on their molecular emission, and to quantify the most meaningful correlations of different molecular tracers with each other and with physical quantities such as density or dust temperature.The wide-field coverage, together with the spatial and spectral resolution, also allows to characterize statistically the kinematics and dynamics of the gas. The amount of momentum in the compressive and solenoidal (rotational) modes of turbulence are retrieved, showing that the cloud is dominated by solenoidal motions, with the compressive modes being concentrated in two star-forming regions. This result is in line with the overall very low star formation efficiency of the cloud, and highlights the role of compressive forcing in the star formation process.The numerous filaments identified in the molecular cloud also prove to have rather low densities, and are very stable against gravitational collapse. Most filaments are starless, but they show signs of longitudinal and radial fragmentation, which indicates that star formation might occur later on. Physics ; Humanities ; Philosophy Spectroscopy and Laser Applications ; Astro and Planetary Science ; Atmospheric Ozone and Climate
Authors: Liam Wilbraham | Reiner Sebastian Sprick | Kim E. Jelfs | Martijn A. Zwijnenburg
Source: ChemRxiv (preprint, 2018-12-11)
Field: Materials Science — Materials Chemistry

Enabling the Exploration of Binary Copolymer Property Space with Neural Networks The extremely large number of unique polymer compositions that can be achieved through copolymerisation makes it an attractive strategy for tuning their optoelectronic properties. However, this same attribute also makes it challenging to explore the resulting property space and understand the range of properties that can be realised. In an effort to enable the rapid exploration of this space in the case of binary copolymers, we train a neural network using a tiered data generation strategy to accurately predict the optical and electronic properties of 350,000 binary copolymers that are, in principle, synthesizable from their dihalogen monomers via Yamamoto, or Suzuki-Miyaura and Stille coupling after one-step functionalisation. By extracting general features of this property space that would otherwise be obscured in smaller datasets, we identify simple models that effectively relate the properties of these copolymers to the homopolymers of their constituent monomers. We find that binary copolymerisation does not appear to allow access to regions of the optoelectronic property space that are not already sampled by the homopolymers, although conceptually allows for more fine-grained property control. Using the large volume of data available, we test the hypothesis that copolymerisation of ‘donor’ and ‘acceptor’ monomers can result in copolymers with a lower optical gap than their related homopolymers and propose a heuristic to predict promising combinations of monomers for which this behaviour is likely. Finally, through a ‘topographical’ analysis of the co-polymer property space, we show how this large volume of data can be used to identify dominant monomers in specific regions of property space that may be amenable to a variety of applications, such as organic photovoltaics, light emitting diodes, and thermoelectrics. Copolymer ; Monomer ; Binary number ; Property (philosophy) ; Materials science ; Computer science ; Polymer ; Space (punctuation) ; Stille reaction ; Mathematics Machine Learning in Materials Science ; Environmental Impact and Sustainability ; Green IT and Sustainability
Authors: Mona Lisa Moura de Oliveira | Nara Angélica Policarpo | Ana Luiza B. P. Barros | Carla A. Silva
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2018-03-01)
Field: Materials Science — Materials Chemistry

Evaluation Of Nh3-Slip From Diesel Vehicles Equipped With Selective Catalytic Reduction Systems By Neural Networks Approach Selective catalytic reduction systems for nitrogen oxides reduction by ammonia has been the chosen technology by most of diesel vehicle (i.e. bus and truck) manufacturers in Brazil, as also in Europe. Furthermore, at some conditions, over-stoichiometric ammonia availability is also needed that increases the NH<sub>3</sub> slips even more. Ammonia (NH<sub>3</sub>) by this vehicle exhaust aftertreatment system provides a maximum efficiency of NOx removal if a significant amount of NH<sub>3</sub> is stored on its catalyst surface. In the other words, the practice shows that slightly less than 100% of the NOx conversion is usually targeted, so that the aqueous urea solution hydrolyzes to NH<sub>3</sub> via other species formation, under relatively low temperatures. This paper presents a model based on neural networks integrated with a road vehicle simulator that allows to estimate NH<sub>3</sub>-slip emission factors for different driving conditions and patterns. The proposed model generates high NH<sub>3</sub>slips which are not also limited in Brazil, but more efforts needed to be made to elucidate the contribution of vehicle-emitted NH<sub>3</sub> to the urban atmosphere. Selective catalytic reduction ; Diesel fuel ; Catalysis ; Automotive engineering ; Reduction (mathematics) ; Artificial neural network ; Slip (aerodynamics) ; Engineering ; Computer science ; Environmental science ; Materials science ; Chemistry ; Artificial intelligence ; Aerospace engineering ; Mathematics ; Organic chemistry Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Air Quality Monitoring and Forecasting