jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amir Farzad Forughi
Source: cIRcle (University of British Columbia) (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Paper drying : experimental studies on the influence of dryer fabric This research is an experimental investigation on paper drying that primarily focuses on the effects of the dryer fabric on the drying process of paper. A novel method for moisture content measurement is presented. The working principle of this method is the strong correlation between the optical transparency of paper and its moisture content due to the refractive index matching role of water in wet paper. Spectrographic and microscopic measurement techniques were employed to characterize the relation of moisture content and relative transparency of paper. As optical access to the paper is restricted by the dryer fabric, the optical transparency of paper should be measured only with one-sided optical access. To achieve this goal, a novel technique of transmittance measurement is developed that is able to determine the transparency of thin film objects (i.e. paper) with only one-sided optical access. Employing a fluorescence imaging method, this optical configuration eliminates the spurious effect of reflection of the incident light by filtering the excitation wavelength before reaching the optical detector. To study the paper drying process in a multi-cylinder dryer, an experimental setup is designed to simulate realistic conditions of a typical paper dryer while providing optical access for the measurement system. Ten commercially available fabric types manufactured by weaving synthetic filaments are used in the investigations. It is shown that the fabric structure affects the drying progression and the drying time significantly. The contact area and three-dimensional arrangement of the filaments have the greatest impact on the drying process. To study through air drying (TAD), another experimental apparatus is designed to perform drying under controlled conditions of air temperature and mass-flowrate. Four commercially available TAD fabrics with different structural designs and characteristics are used in the investigations. It is shown that the geometry of the contact spots of the fabrics has a significant impact on the drying time at high drying intensities. Comparing the spatial maps of moisture content with the paper grammage distribution reveals that there is a correlation between the local grammage and the local moisture in a paper sheet during the drying process. Process engineering ; Environmental science ; Food science ; Pulp and paper industry ; Chemistry ; Engineering Textile materials and evaluations ; Material Properties and Processing
Authors: Effiong Edet Bassey
Source: Interventions in Pediatric Dentistry Open Access Journal (article, 2018-04-30)
Field: Chemistry — Inorganic Chemistry

Antimicrobial Susceptibility Pattern of Coconut Oil Extract on Selected Bacterial and Fungi The study is aimed at evaluating the phytochemical content of coconut oil and determines the susceptibility pattern of coconut oil against bacteria and fungi. Coconut oil is a high value coconut product and some positive health benefits have currently been attributed to coconut intake such as its antioxidant and anticancer properties. Coconut oil has also been shown to have antibacterial and antifungal properties and can be used in the treatment of infectious diseases. The findings revealed that the coconut oil sample contains phytochemicals such as alkaloid, glycosides, Terpenoids and considered to be responsible for the many benefits attributed to coconut consumption. This susceptibility testing was conducted by agar diffusion method and the zones inhibition indicated the antimicrobial properties of coconut oil. The assey of antibacterial activity of standard bacteria organisms showed that Staphyloccus aureus had the highest susceptibility to coconut oil while Pseudomonas aeroginosa had the least. this was concluded from their average zones of inhibition 14.55mm (32%) for Staphyloccocus Aureus, 12.1mm (27%) for Streptocccus pneumonia, 10.95 (24%) for Escherichia coli and 7.7mm (17%) for Pseudomonas aeroginosa. Candida albicans had a higher susceptibility to coconut oil more than aspergillus in antifungal testing. This was also concluded from their average zones of inhibition 18.5mm (55%) for Candida albicans and 15.1mm (45%) for Aspergillus fumigatus. The utilization of coconut oil should be promoted as a functional food in Nigeria and the use of coconut seed flesh in our diets should be encouraged for health supporting functions. Antimicrobial ; Coconut oil ; Microbiology ; Biology ; Food science Coconut Research and Applications ; Essential Oils and Antimicrobial Activity ; Advanced Chemical Sensor Technologies
Authors: Seongok Ryu | Yongchan Kwon | Woo Youn Kim
Source: arXiv (Cornell University) (preprint, 2018-11-19)
Field: Materials Science — Materials Chemistry

Uncertainty quantification of molecular property prediction using Bayesian neural network models In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning chemical reactions. Despite the rapid increase in the use of state-of-the-art models and algorithms, deep neural network models often produce poor predictions in real applications because model performance is highly dependent on the quality of training data. In the field of molecular analysis, data are mostly obtained from either complicated chemical experiments or approximate mathematical equations, and then quality of data may be questioned.In this paper, we quantify uncertainties of prediction using Bayesian neural networks in molecular property predictions. We estimate both model-driven and data-driven uncertainties, demonstrating the usefulness of uncertainty quantification as both a quality checker and a confidence indicator with the three experiments. Our results manifest that uncertainty quantification is necessary for more reliable molecular applications and Bayesian neural network models can be a practical approach. Artificial neural network ; Computer science ; Bayesian probability ; Bayesian network ; Machine learning ; Artificial intelligence ; Property (philosophy) ; Uncertainty quantification ; Variable-order Bayesian network ; Bayes' theorem ; Data mining ; Bayesian inference Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Kristof T. Schütt | Alexandre Tkatchenko | Klaus‐Robert Müller
Source: arXiv (Cornell University) (preprint, 2018-12-11)
Field: Materials Science — Materials Chemistry

Learning representations of molecules and materials with atomistic\n neural networks Deep Learning has been shown to learn efficient representations for\nstructured data such as image, text or audio. In this chapter, we present\nneural network architectures that are able to learn efficient representations\nof molecules and materials. In particular, the continuous-filter convolutional\nnetwork SchNet accurately predicts chemical properties across compositional and\nconfigurational space on a variety of datasets. Beyond that, we analyze the\nobtained representations to find evidence that their spatial and chemical\nproperties agree with chemical intuition.\n Intuition ; Computer science ; Chemical space ; Convolutional neural network ; Artificial neural network ; Variety (cybernetics) ; Artificial intelligence ; Space (punctuation) ; Theoretical computer science ; Cognitive science ; Chemistry ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Helge S. Stein | Dan Guevarra | Paul F. Newhouse | Edwin Soedarmadji | John M. Gregoire
Source: ChemRxiv (preprint, 2018-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning of Optical Properties of Materials - Predicting Spectra from Images and Images from Spectra As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 180,902 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80,000 unique quinary oxide and 67,000 unique quaternary oxide compositions, making it the largest and most diverse experimental materials set utilized in machine learning studies. The extensive dataset enabled training and validation of 3 distinct models for mapping between sample images and absorption spectra, including a conditional variational autoencoder that generates images of hypothetical materials with tailored absorption properties. The absorption patterns auto-generated from sample images capture the salient features of ground truth spectra, and direct band gap energies extracted from these auto-generated patterns are quite accurate with a mean absolute error of 240 meV, which is the approximate uncertainty from traditional extraction of the band gap energy from measurements of the full transmission and reflection spectra. Optical properties of materials are not only ubiquitous in materials applications but also emblematic of the confluence of underlying physical phenomena that yield the type of complex data relationships that merit and benefit from neural network-type modelling. Artificial intelligence ; Computer science ; Machine learning ; Autoencoder ; Spectral line ; Ground truth ; Sample (material) ; Materials informatics ; Set (abstract data type) ; Algorithm ; Materials science ; Artificial neural network ; Chemistry ; Physics Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping
Authors: Jatin Kumar | Qianxiao Li | Karen Yuanting Tang | Tonio Buonassisi | Anibal L. Gonzalez-Oyarce | Jun Ye
Source: ChemRxiv (preprint, 2018-12-28)
Field: Materials Science — Materials Chemistry

Machine Learning Enables Polymer Cloud-Point Engineering via Inverse Design Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4°C root mean squared error (RMSE) in a temperature range of 24– 90°C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80°C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers. Mean squared error ; Computer science ; Inverse ; Point cloud ; Particle swarm optimization ; Polymer ; Algorithm ; Mathematical optimization ; Artificial intelligence ; Machine learning ; Materials science ; Mathematics ; Statistics ; Geometry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Adversarial Robustness in Machine Learning
Authors: Ayfer Dönmez Çavdar | Ercüment Öztürk | Tuğrul Çavdar
Source:  (article, 2018-09-01)
Field: Materials Science — Polymers and Plastics

A Novel Approach to Determine the Amount of Natural Fiber and Polymer of Composite Materials via Artificial Neural Networks In this study, it is aimed to use Tea Mill Waste Fiber (TMWF) as a mixture raw material to produce a high quality and durable composite product via Artificial Neural Network (ANN) which is a new and developing method. It is aimed to be able to produce better quality products by mixing polypropylene (PP), high density polyethylene (HDPE) and TMWF as raw materials in the correct proportions. An ANN was generated in the Matlab environment and it was trained using data, which was obtained as a result of empirical studies to find the ideal mixture ratio to obtain a quality product. It has been shown that the ratio of the mixture in the simulation result is correct and the created ANN is successful as these results are compared to the results obtained from the experimental studies. High-density polyethylene ; Artificial neural network ; Raw material ; Polypropylene ; Composite number ; Fiber ; Process engineering ; MATLAB ; Materials science ; Product (mathematics) ; Computer science ; Composite material ; Pulp and paper industry ; Polyethylene ; Artificial intelligence ; Engineering ; Mathematics Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Transport Systems and Technology
Authors: Sherif Abdulkader Tawfik | Olexandr Isayev | Catherine Stampfl | Joseph G. Shapter | David W. Winkler | Michael J. Ford
Source: ChemRxiv (preprint, 2018-08-20)
Field: Materials Science — Materials Chemistry

Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. Here we demonstrate that a combination of DFT and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiment. As a proof of concept we applied this methodology to predict the interlayer distance and band gap of bilayer heterostructures. Our methods quickly and accurately predicted these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties. van der Waals force ; Computer science ; Density functional theory ; Key (lock) ; Heterojunction ; Space (punctuation) ; Parameter space ; Work (physics) ; Artificial intelligence ; Machine learning ; Statistical physics ; Computational science ; Mathematics ; Physics ; Quantum mechanics ; Molecule ; Geometry Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Steen Lysgaard | Paul C. Jennings | Jens S. Hummelshøj | Thomas Bligaard | Tejs Vegge
Source: ChemRxiv (preprint, 2018-12-03)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA. Genetic algorithm ; Computer science ; Algorithm ; Artificial intelligence ; Reduction (mathematics) ; Machine learning ; Surrogate model ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yan Li | Guohui Li | Hanyi Min | Zibing Dong | Yuan Tian | Xiaoqi Li | Peijun Xu
Source: Communications in Information and Systems (article, 2018-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Prediction of molecular energy using deep tensor neural networks Artificial neural network ; Deep neural networks ; Tensor (intrinsic definition) ; Statistical physics ; Artificial intelligence ; Computer science ; Physics ; Mathematics Various Chemistry Research Topics ; Machine Learning in Materials Science ; Advanced Data Processing Techniques
Authors: Vinícius Galhard Grassi
Source: Chem (article, 2018-05-01)
Field: Materials Science — Materials Chemistry

Reaction: Conventional Polymers—New Tricks Emerging Petrochemical ; Consolidation (business) ; Styrene ; Polymer science ; Thermoplastic polymer ; Engineering ; Polymer ; Chemistry ; Organic chemistry ; Copolymer ; Business ; Waste management Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: Li Gao | Sheliang Wang | Jiangle Li
Source: NeuroQuantology (article, 2018-06-21)
Field: Materials Science — Materials Chemistry

Constitutive Model of Shape Memory Alloy Wavelet Neural Network Based on Improved Bat Algorithm Based on the experiment of austenite SMA wire properties, the influence of strain amplitude on the dynamic properties of SMA wire is studied, and a constitutive model of SMA wire based on a chaotic Gaussian bat (GS-BA) optimized wavelet neural network (GS-BA-WNN) is proposed. In view of such shortcomings as easy early-maturing and low diversity in late period of the basic bat algorithm (BA), the Gaussian disturbance is used to enhance algorithm’s ability to escape local optimum and promote the bat population chaos optimization to improve the population diversity. GS-BA is combined with wavelet neural network to obtain the initial parameter configuration of WNN, and the model of GS-BA-WNN is used to simulate the constitutive relationship of SMA wire under different amplitudes of strain, and at the same time it is compared with the model of WNN and model of WNN (BA-WNN) optimized by BA. The results show that the GS-BA-WNN model established using experimental data as model training data has higher prediction precision and smaller error than other models, and has advantages in predicting the constitutive relationship of SMA wire. Artificial neural network ; Wavelet ; Computer science ; Shape-memory alloy ; Constitutive equation ; Artificial intelligence ; Algorithm ; Engineering ; Structural engineering ; Finite element method Shape Memory Alloy Transformations
Authors: Lukas Turcani | Rebecca L. Greenaway | Kim E. Jelfs
Source: ChemRxiv (preprint, 2018-10-26)
Field: Materials Science — Materials Chemistry

Machine Learning for Organic Cage Property Prediction We use machine learning to predict shape persistence and cavity size in porous organic cages. The majority of hypothetical organic cages suffer from a lack of shape persistence and as a result lack intrinsic porosity, rendering them unsuitable for many applications. We have created the largest computational database of these molecules to date, numbering 63,472 cages, formed through a range of reaction chemistries and in multiple topologies. We study our database and identify features which lead to the formation of shape persistent cages. We find that the imine condensation of trialdehydes and diamines in a [4+6] reaction is the most likely to result in shape persistent cages, whereas thiol reactions are most likely to give collapsed cages. Using this database, we develop machine learning models capable of predicting shape persistence with an accuracy of up to 93%, reducing the time taken to predict this property to milliseconds, and removing the need for specialist software. In addition, we develop machine learning models for two other key properties of these molecules, cavity size and symmetry. We provide open-source implementations of our models, together with the accompanying data sets, and an online tool giving users access to our models to easily obtain predictions for a hypothetical cage prior to a synthesis attempt. Computer science ; Machine learning ; Artificial intelligence ; Property (philosophy) ; Algorithm Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Supramolecular Chemistry and Complexes
Authors: Farag K. Abo‐Elyousr | L. S. Nassrat
Source: TELKOMNIKA (Telecommunication Computing Electronics and Control) (article, 2018-04-01)
Field: Materials Science — Materials Chemistry

Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy Artificial neural network ; Neuro-fuzzy ; Biological system ; Artificial intelligence ; Computer science ; Fuzzy logic ; Voltage ; Pattern recognition (psychology) ; Materials science ; Machine learning ; Biology ; Fuzzy control system ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Infrastructure Maintenance and Monitoring
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2018-08-27)
Field: Materials Science — Materials Chemistry

Machine learning offers fast, accurate calculations Existing computers can calculate the exact properties of only the smallest molecules thanks to the mathematical complexity of quantum mechanics. So chemists have invented methods, including force fields, density functional theory (DFT), and the coupled-cluster singles, doubles, and triples technique (CCSD(T)), to approximate values like molecular energies and forces. Users can get quick answers or accurate ones with these methods. For example, CCSD(T) is accurate but slow, compared with force fields. Some researchers think machine learning could offer a better way. At the ACS meeting last week, Adrian Roitberg of the University of Florida described a method that can achieve the accuracy of CCSD(T) in the computational time of force fields. He calls it Accurate NeurAl networK engINe for Molecular Energies (ANAKIN-ME). Roitberg said the third version of the method, which his team calls ANI-1ccx, can predict the forces and energy of a molecule with only the positions of its Computer science ; Coupled cluster ; Artificial neural network ; Density functional theory ; Statistical physics ; Energy (signal processing) ; Force field (fiction) ; Artificial intelligence ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science
Authors: Rishabh Sharma | Isaac Chelladurai | Andrew D. Orme | Michael Miles | Christophe Giraud-Carrier | David T. Fullwood
Source: Journal of Microscopy (article, 2018-08-08)
Field: Materials Science — Biomaterials

A step towards intelligent EBSD microscopy: machine‐learning prediction of twin activity in MgAZ31 One role of artificial intelligence is to predict future events after learning from many previous observations. In materials science, various phenomena (such as crack nucleation) are difficult to predict because they have been insufficiently observed. Furthermore, observation is difficult, precisely because their location cannot be predicted, leading to a chicken and egg conundrum. This paper applies machine learning to the search for twin nucleation sites in a magnesium alloy, in an attempt to guide the observation of twin nucleation events in a microscope based on previous observations. As more data is obtained, the accuracy of the location prediction will increase. In the current case, the machine-learning tool achieved 85% accuracy for predicting the location of twin interactions with grain boundaries after several thousand observations. The resultant framework provides the first step towards an intelligent microscopy for efficient observation of stochastic events during in situ microscopy campaigns. Artificial intelligence ; Machine learning ; Computer science ; Decision tree Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties