jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yumeng Li | Pingfeng Wang | Weirong Xiao
Source:  (article, 2018-01-07)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification of Atomistic Materials Simulation with Machine Learning Potentials Computer science ; Uncertainty quantification ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Lukas Turcani | Kim E. Jelfs
Source: ChemRxiv (preprint, 2018-08-23)
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 ; Property (philosophy) ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Supramolecular Chemistry and Complexes
Authors: Ryan‐Rhys Griffiths | Philippe Schwaller | Alpha A. Lee
Source: ChemRxiv (preprint, 2018-11-21)
Field: Materials Science — Materials Chemistry

Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning. In this paper, we identify three trends within the fields of chemical reaction prediction and synthesis design that require a change in direction. First, the manner in which reaction datasets are split into reactants and reagents encourages testing models in an unrealistically generous manner. Second, we highlight the prevalence of mislabelled data, and suggest that the focus should be on outlier removal rather than data fitting only. Lastly, we discuss the problem of reagent prediction, in addition to reactant prediction, in order to solve the full synthesis design problem, highlighting the mismatch between what machine learning solves and what a lab chemist would need. Our critiques are also relevant to the burgeoning field of using machine learning to accelerate progress in experimental Natural Sciences, where datasets are often split in a biased way, are highly noisy, and contextual variables that are not evident from the data strongly influence the outcome of experiments.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Outlier ; Artificial intelligence ; Reagent ; Machine learning ; Computer science ; Field (mathematics) ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Ameneh Ahangarpour | Mansoor Farbod | Afshin Ghanbarzadeh | Abbas Moradi | Amin MirzakhaniNafchi
Source: SHILAP Revista de lepidopterología (article, 2018-07-01)
Field: Materials Science — Materials Chemistry

Optimization of continual production of CNTs by CVD method using Radial Basic Function (RBF) neural network and the Bees Algorithm Optimization of continuous synthesis of high purity carbon nanotubes (CNTs) using chemical vapour deposition (CVD) method was studied experimentally and theoretically. Iron pentacarbonyl (Fe(CO)5), acetylene (C2H2) and Ar were used as the catalyst source, carbon source and carrier gas respectively. The synthesis temperature and flow rates of Ar and acetylene were optimized to produce CNTs at a large scale. A flow rate of 30-120 sccm of acetylene and 500-3000 sccm of Ar at temperatures between 650-950 °C were examined. Using the fundamental trial and error method it was found that the maximum yield of pure CNTs can be produced at 750 °C with flow rates of 40-45 sccm of acetylene and 1500 sccm of Ar. In theoretical part, an artificial neural network (ANN) and the Bees Algorithm (BA) were used to model and optimize the CNTs production, based on the experimental data. The Bees Algorithm used the ANN as the fitness function and the optimum variables found as 60 sccm for acetylene, 555 sccm for argon and 759 °C for temperature. The computational results have relatively good agreement with the experimental results. Acetylene ; Materials science ; Carbon nanotube ; Chemical vapor deposition ; Volumetric flow rate ; Artificial neural network ; Yield (engineering) ; Iron pentacarbonyl ; Carbon fibers ; Argon ; Chemical engineering ; Nanotechnology ; Analytical Chemistry (journal) ; Biological system ; Composite material ; Computer science ; Thermodynamics ; Chromatography ; Organic chemistry ; Composite number ; Artificial intelligence ; Chemistry Carbon Nanotubes in Composites ; Heat Transfer and Optimization
Authors: Ahmad Danial Azzahari | Vidhya Selvanathan | Muhammad Rizwan | Faridah Sonsudin | Rosiyah Yahya
Source: Ionics (article, 2018-03-12)
Field: Materials Science — Polymers and Plastics

Conductivity or rheology? Tradeoff for competing properties in the fabrication of a gel polymer electrolyte based on chitosan-barbiturate derivative Polymer ; Materials science ; Chemical engineering ; Dynamic mechanical analysis ; Chitosan ; Electrolyte ; Rheology ; Sol-gel ; Derivative (finance) ; Polymer chemistry ; Conductivity ; Electrochemistry ; Composite material ; Chemistry ; Physical chemistry ; Nanotechnology ; Electrode Conducting polymers and applications ; Advanced Battery Materials and Technologies ; Supercapacitor Materials and Fabrication
Authors: Jong-Tak Kim | Jae Sakong | Sung-Choong Woo | Jin‐Young Kim | Tae‐Won Kim
Source: Journal of Mechanical Science and Technology (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Determination of the damage mechanisms in armor structural materials via self-organizing map analysis Acoustic emission ; Materials science ; Split-Hopkinson pressure bar ; Composite material ; Strain rate ; Fracture (geology) ; SIGNAL (programming language) ; Deformation (meteorology) ; Cluster (spacecraft) ; Amplitude ; Compression (physics) ; Stress (linguistics) ; Structural engineering ; Computer science ; Engineering ; Physics High-Velocity Impact and Material Behavior ; Advanced Measurement and Detection Methods ; Surface Treatment and Residual Stress
Authors: Xavier Brumwell | Paul Sinz | Kwang J. Kim | Yue Qi | Matthew Hirn
Source: arXiv (Cornell University) (preprint, 2018-11-21)
Field: Materials Science — Materials Chemistry

Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction A general machine learning architecture is introduced that uses wavelet scattering coefficients of an inputted three dimensional signal as features. Solid harmonic wavelet scattering transforms of three dimensional signals were previously introduced in a machine learning framework for the regression of properties of small organic molecules. Here this approach is extended for general steerable wavelets which are equivariant to translations and rotations, resulting in a sparse model of the target function. The scattering coefficients inherit from the wavelets invariance to translations and rotations. As an illustration of this approach a linear regression model is learned for the formation energy of amorphous lithium-silicon material states trained over a database generated using plane-wave Density Functional Theory methods. State-of-the-art results are produced as compared to other machine learning approaches over similarly generated databases. Wavelet ; Scattering ; Energy (signal processing) ; Harmonic ; Computer science ; Plane (geometry) ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Algorithm ; Physics ; Mathematics ; Optics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: James D. Martin | Katherine A. Nock
Source: Journal of Chemical Education (article, 2018-10-18)
Field: Chemistry — Physical and Theoretical Chemistry

A Nonlinear, “Sticky” Web of Study for Chemistry: A Graphical Curricular Tool for Teaching and Learning Chemistry Built upon the Interconnection of Core Chemical Principles The National Research Council’s Framework for K–12 Science Education articulates the need to shift science curricula from being a collection of discrete facts to curricula that integrate core ideas and practices. To help teachers better integrate content and to respond to expressed frustration regarding extensive lists of standards often presented as collections of isolated topics in prescribed courses of study, we developed a web of study approach to general chemistry curricula. Instead of a traditional linear course of study for which lists of standards tend to shift teaching and learning toward coverage, the web of study approach emphasizes integration of concepts by which learners build a coherent and self-consistent body of knowledge. Using both spatial and color-coded relationships, a general chemistry course is mapped onto the triad of primary concepts, Matter–Energy–Bonding, each represented as a primary color. Subtopics, traditionally identified in bulleted lists of standards, are graphically placed and color coded with secondary colors to provide a visual representation of an entire general chemistry course. The nonlinear web of study approach offers a greater “sticking capacity”, with deep learning achieved by teacher and learner as they reflect on how any given topic, concept, or practice relates to the entire web. While providing important contexts to understand the relevance and relationships of new material being learned, this visual representation of content facilitates seeing and thinking about interrelationships, provides a framework with which to formulate logical explanations of observed phenomena, and stimulates the most fundamental process of science: asking new questions. Chemistry ; Chemistry education ; Core (optical fiber) ; Computer science ; Quality (philosophy) ; Physics Various Chemistry Research Topics ; Innovative Teaching Methods ; Science Education and Pedagogy
Authors: Giovanni Di Nicola | Gianluca Coccia | Mariano Pierantozzi | Sebastiano Tomassetti | Roberta Cocci Grifoni
Source: Chemical Engineering Communications (article, 2018-06-06)
Field: Chemistry — Analytical Chemistry

Artificial neural network for the second virial coefficient of organic and inorganic compounds: An ANN for <i>B</i> of organic and inorganic compounds An artificial neural network (ANN) to estimate the second virial coefficient, valid for organic and inorganic compounds, is presented. First, we statistically analyzed 6,531 experimental points, belonging to 234 fluids, collected from literature. The data were investigated with a factor analysis approach to identify the most significant parameters that influence the second virial coefficient. The factor analysis, combined with physical considerations, allowed to find four (Tr, Tc, Pc, ω) or five (μr) parameters as input variables for the ANN, according to the specific chemical family. The architecture of the proposed multi-layers perceptron (MLP) neural network consists of one input layer with five input variables (Tr, Tc, Pc, ω, μr), one output layer with one neuron (B) and two-hidden-layers with 19 neurons each. We trained, validated and tested several configurations of the neural network to obtain this network topology that minimizes the deviations between experimental and calculated points. Results show that the ANN is able to calculate the second virial coefficient with greater accuracy (RMSE = 29.38 cm3/mol) than that of correlations available in literature. To identify the outliers and applicability domain of the proposed MLP neural network, an outlier diagnosis based on the Leverage approach was performed. This analysis shows that the model is statistically valid. Artificial neural network ; Virial coefficient ; Perceptron ; Multilayer perceptron ; Leverage (statistics) ; Outlier ; Mean squared error ; Biological system ; Artificial intelligence ; Mathematics ; Pattern recognition (psychology) ; Chemistry ; Statistics ; Computer science ; Thermodynamics ; Physics Spectroscopy and Chemometric Analyses ; Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics
Authors: Rui Li | Yao Liu | Shulan Zuo | Tongyun Zhao | Fengxia Hu | Jirong Sun | Bao-gen Shen
Source: Chinese Physics B (article, 2018-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Composition design for (PrNd–La–Ce) <sub>2</sub> Fe <sub>14</sub> B melt-spun magnets by machine learning technique Data-driven technique is a powerful and efficient tool for guiding materials design, which could supply as an alternative to trial-and-error experiments. In order to accelerate composition design for low-cost rare-earth permanent magnets, an approach using composition to estimate coercivity (H cj) and maximum magnetic energy product ((BH)max) via machine learning has been applied to (PrNd–La–Ce)2Fe14B melt-spun magnets. A set of machine learning algorithms are employed to build property prediction models, in which the algorithm of Gradient Boosted Regression Trees is the best for predicting both H cj and (BH)max, with high accuracies of and 0.89, respectively. Using the best models, predicted datasets of or (BH)max in high-dimensional composition space can be constructed. Exploring these virtual datasets could provide efficient guidance for materials design, and facilitate the composition optimization of 2:14:1 structure melt-spun magnets. Combined with magnets' cost performance, the candidate cost-effective magnets with targeted properties can also be accurately and rapidly identified. Such data analytics, which involves property prediction and composition design, is of great time-saving and economical significance for the development and application of LaCe-containing melt-spun magnets. Magnet ; Materials science ; Coercivity ; Composition (language) ; Computer science ; Algorithm ; Machine learning ; Mechanical engineering ; Artificial intelligence ; Process engineering ; Physics ; Condensed matter physics Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Magnetic properties of thin films
Authors: Patryk Kot | Magomed Muradov | Samuel Ryecroft | Montserrat Ortoneda Pedrola | Andy Shaw | Janet Hemingway | Rinki Deb | M Nicole Coleman
Source:  (article, 2018-07-01)
Field: Chemistry — Analytical Chemistry

Identification of Optimal Frequencies to Determine Alpha-Cypermethrin Using Machine Learning Feature Selection Techniques Machine learning feature space reduction techniques allow for vast feature spaces to be reduced with little loss or even significant improvements in the reliability of predictions of models. Microwave spectroscopy with feature spaces of over 8000 are not uncommon when considering magnitude and phase. Applying Machine learning techniques to this type of feature space allows for a quicker reduction and helps to identify the most suitable predictive features. The control of insect vectors that transmit diseases including malaria, visceral leishmaniasis and zika rely on the use of insecticide. These diseases affect millions, malaria alone accounted for 214 million new cases resulting in 438, 000 deaths in 2015. One method used in controlling the vectors is through indoor residual spraying, applying insecticide to the wall surface inside houses. Alpha-cypermethrin is one of the insecticides that is currently sprayed in several countries for vector control. Quality assurance and monitoring of the control activities is challenging relying on the use of laboratory-reared insects. This was improved with a chemical based Insecticide Quantification Kit, but these assays have been challenging to operationalise. An electromagnetic sensor is being developed to investigate the potential to detect alpha-cypermethrin. Preliminary experiments were carried out to differentiate tiles sprayed with Technical Grade alpha-cypermethrin, wettable powder containing 5% alpha-cypermethrin and wettable powder with over dose of alpha-cypermethrin using a horn antenna at a frequency range between 1 GHz to 6 GHz. The experimental results indicated the potential use of electromagnetic waves to determine alpha-cypermethrin in a non-destructive manner. Indoor residual spraying ; Wettable powder ; Feature (linguistics) ; Artificial intelligence ; Feature vector ; Machine learning ; Feature selection ; Computer science ; Support vector machine ; Pattern recognition (psychology) ; Biology ; Pesticide ; Malaria ; Ecology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Nandakumar Pillai | R. Karthikeyan | J. Paulo Davim
Source: Tribology - Materials Surfaces & Interfaces (article, 2018-01-02)
Field: Materials Science — Materials Chemistry

Heat treatment effects on tribological characteristics for AISI A8 tool steel and development of wear mechanism maps using K means clustering and neural networks Viking steel classified under AISI A8 cold working tool steel is widely used for heavy duty blanking and forming operations. The excellent combination of wear resistance and toughness make this material superior when compared to other tool steels in such applications. This cold working tool steel is easy to machine and gives excellent mechanical properties upon proper heat treatment. The heat treatment carried out with air and oil as quenching media at various conditions of temperature and time are discussed. Optical microscopy and hardness tests are performed on the heat-treated specimens. The samples with best results from each group are selected for further analysis. The tribological properties of these specimens are analysed with the help of wear tests conducted on pin on disc tribometer under different load and velocity levels. The data obtained from these experiments are used to find out the coefficient of friction and wear rate. The experimental results are used to develop wear mechanism maps with the help of K means clustering analysis and probabilistic neural networks. A novel approach based on clustering analysis is proposed as the analysis of wear data and wear mechanism maps are developed using with neural networks for tool steel. The data analysis is correlated with the scanning electron microscopic observations and the results are discussed. Materials science ; Tribology ; Tribometer ; Tool wear ; Mechanism (biology) ; Toughness ; Quenching (fluorescence) ; Artificial neural network ; Metallurgy ; Machining ; Computer science ; Artificial intelligence Metal Alloys Wear and Properties ; Metal and Thin Film Mechanics ; Advanced machining processes and optimization
Authors: Najmeh Moazeni | Morteza Vadood | Dariush Semnani | Hossein Hasani
Source: Materials Research Express (article, 2018-01-15)
Field: Materials Science — Biomaterials

Modeling the compliance of polyurethane nanofiber tubes for artificial common bile duct The common bile duct is one of the body's most sensitive organs and a polyurethane nanofiber tube can be used as a prosthetic of the common bile duct. The compliance is one of the most important properties of prosthetic which should be adequately compliant as long as possible to keep the behavioral integrity of prosthetic. In the present paper, the prosthetic compliance was measured and modeled using regression method and artificial neural network (ANN) based on the electrospinning process parameters such as polymer concentration, voltage, tip-to-collector distance and flow rate. Whereas, the ANN model contains different parameters affecting on the prediction accuracy directly, the genetic algorithm (GA) was used to optimize the ANN parameters. Finally, it was observed that the optimized ANN model by GA can predict the compliance with high accuracy (mean absolute percentage error = 8.57%). Moreover, the contribution of variables on the compliance was investigated through relative importance analysis and the optimum values of parameters for ideal compliance were determined. Polyurethane ; Nanofiber ; Common bile duct ; Tube (container) ; Compliance (psychology) ; Materials science ; Biomedical engineering ; Engineering ; Composite material ; Surgery ; Medicine ; Psychology Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Advanced Sensor and Energy Harvesting Materials
Authors: Mengzhu Li | Wenqing Liu | Zhigang Zhao | Lixin Liu | Xing Liu
Source: Tenth International Conference on Information Optics and Photonics (article, 2018-11-15)
Field: Chemistry — Analytical Chemistry

Application of hyperspectral imaging technology in nondestructive testing of fruit quality Hyperspectral imaging (HSI) technology is a multidimensional information acquisition technology that combines imaging and spectroscopic techniques. It can not only visually display the external quality characteristics of the objects to be measured, but also reflect the differences in their internal chemical composition. HSI is playing an increasingly important role in rapid and non-destructive testing of fruit quality. In this paper, we discuss the application of HSI in the identification of small tomato varieties and the detection of pesticide residues. The back propagation neural network (BPNN) and support vector machine (SVM) algorithms were used to establish the variety identification and pesticide residues concentration analysis models. By using multiplicative scatter correction (MSC) pretreatment the accuracy of the two models reached up to 100%. The current study indicates that combining HSI technology with proper algorithm can provide an efficient method to identify small tomato varieties and detect pesticide residues. Hyperspectral imaging ; Support vector machine ; Computer science ; Artificial intelligence ; Identification (biology) ; Pattern recognition (psychology) ; Artificial neural network ; Quality (philosophy) ; Nondestructive testing ; Computer vision Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research