jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gabriele Scalia | Colin A. Grambow | Barbara Pernici | Yi‐Pei Li | William H. Green
Source: arXiv (Cornell University) (preprint, 2019-10-07)
Field: Materials Science — Materials Chemistry

Evaluating Scalable Uncertainty Estimation Methods for DNN-Based\n Molecular Property Prediction Advances in deep neural network (DNN) based molecular property prediction\nhave recently led to the development of models of remarkable accuracy and\ngeneralization ability, with graph convolution neural networks (GCNNs)\nreporting state-of-the-art performance for this task. However, some challenges\nremain and one of the most important that needs to be fully addressed concerns\nuncertainty quantification. DNN performance is affected by the volume and the\nquality of the training samples. Therefore, establishing when and to what\nextent a prediction can be considered reliable is just as important as\noutputting accurate predictions, especially when out-of-domain molecules are\ntargeted. Recently, several methods to account for uncertainty in DNNs have\nbeen proposed, most of which are based on approximate Bayesian inference. Among\nthese, only a few scale to the large datasets required in applications.\nEvaluating and comparing these methods has recently attracted great interest,\nbut results are generally fragmented and absent for molecular property\nprediction. In this paper, we aim to quantitatively compare scalable techniques\nfor uncertainty estimation in GCNNs. We introduce a set of quantitative\ncriteria to capture different uncertainty aspects, and then use these criteria\nto compare MC-Dropout, deep ensembles, and bootstrapping, both theoretically in\na unified framework that separates aleatoric/epistemic uncertainty and\nexperimentally on the QM9 dataset. Our experiments quantify the performance of\nthe different uncertainty estimation methods and their impact on\nuncertainty-related error reduction. Our findings indicate that ensembling and\nbootstrapping consistently outperform MC-Dropout, with different\ncontext-specific pros and cons. Our analysis also leads to a better\nunderstanding of the role of aleatoric/epistemic uncertainty and highlights the\nchallenge posed by out-of-domain uncertainty.\n Computer science ; Bootstrapping (finance) ; Scalability ; Uncertainty quantification ; Inference ; Machine learning ; Artificial intelligence ; Generalization ; Dropout (neural networks) ; Context (archaeology) ; Data mining ; Bayesian probability ; Econometrics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: C. Mahil loo Christopher | T. Sasikumar | Tom Page
Source: Materials Today Proceedings (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Crack Growth Detection on Al/Sicp Using Acoustic Monitoring and Artificial Neural Network Acoustic emission ; Materials science ; Ultimate tensile strength ; Artificial neural network ; Silicon carbide ; Composite material ; Cracking ; Fracture (geology) ; Structural engineering ; Computer science ; Engineering ; Artificial intelligence High-Velocity Impact and Material Behavior ; Non-Destructive Testing Techniques ; Ultrasonics and Acoustic Wave Propagation
Authors: Gustavo M. Dalpian | Osvaldo N. Oliveira
Source: ACS Applied Materials & Interfaces (article, 2019-07-17)
Field: Materials Science — Materials Chemistry

Preface to the Forum on Materials Discovery and Design ADVERTISEMENT RETURN TO ISSUEEditorialNEXTPreface to the Forum on Materials Discovery and DesignGustavo M. DalpianGustavo M. DalpianFederal University of ABC, BrazilMore by Gustavo M. Dalpianhttp://orcid.org/0000-0001-5561-354X and Osvaldo N. Oliveira Jr.Osvaldo N. Oliveira, Jr.University of São Paulo, BrazilMore by Osvaldo N. Oliveira, Jr.http://orcid.org/0000-0002-5399-5860Cite this: ACS Appl. Mater. Interfaces 2019, 11, 28, 24823–24824Publication Date (Web):July 17, 2019Publication History Published online17 July 2019Published inissue 17 July 2019https://pubs.acs.org/doi/10.1021/acsami.9b10631https://doi.org/10.1021/acsami.9b10631editorialACS PublicationsCopyright © 2019 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views3711Altmetric-Citations3LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (241 KB) Get e-AlertscloseSUBJECTS:Chemical calculations,Impurities,Machine learning,Materials,Two dimensional materials Get e-Alerts Materials science ; Nanotechnology ; Engineering ethics ; Engineering physics ; Engineering Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Xin Li | Guyu Hu | Zhisong Pan
Source: Journal of Ambient Intelligence and Humanized Computing (article, 2019-11-05)
Field: Chemistry — Analytical Chemistry

An eigendecomposition method based on deep learning and probabilistic graph model Computer science ; Computational intelligence ; Probabilistic logic ; Artificial intelligence ; Eigendecomposition of a matrix ; Graph ; Machine learning ; Eigenvalues and eigenvectors ; Pattern recognition (psychology) ; Theoretical computer science Spectroscopy and Chemometric Analyses ; Advanced Decision-Making Techniques ; Industrial Vision Systems and Defect Detection
Authors: Sun Zhichao | Zhikun Yin | Jing Cao | Long Huang
Source: JOM (article, 2019-10-11)
Field: Materials Science — Materials Chemistry

Allowable Ranges of Conventional Forging Parameters Determination for TA15 Ti-Alloy to Obtain Tri-modal Microstructure Under Given Subsequent Heat Treatment Forging ; Microstructure ; Materials science ; Quenching (fluorescence) ; Metallurgy ; Alloy ; Strain rate ; Modal ; Deformation (meteorology) ; Air cooling ; Composite material ; Structural engineering ; Mechanical engineering ; Engineering Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Metal and Thin Film Mechanics
Authors: Yi-Ting Tsai | Susan A. Baldwin | Lim C. Siang | R. Bhushan Gopaluni
Source: Processes (article, 2019-09-10)
Field: Chemistry — Analytical Chemistry

A Comparison of Clustering and Prediction Methods for Identifying Key Chemical–Biological Features Affecting Bioreactor Performance Chemical–biological systems, such as bioreactors, contain stochastic and non-linear interactions which are difficult to characterize. The highly complex interactions between microbial species and communities may not be sufficiently captured using first-principles, stationary, or low-dimensional models. This paper compares and contrasts multiple data analysis strategies, which include three predictive models (random forests, support vector machines, and neural networks), three clustering models (hierarchical, Gaussian mixtures, and Dirichlet mixtures), and two feature selection approaches (mean decrease in accuracy and its conditional variant). These methods not only predict the bioreactor outcome with sufficient accuracy, but the important features correlated with said outcome are also identified. The novelty of this work lies in the extensive exploration and critique of a wide arsenal of methods instead of single methods, as observed in many papers of similar nature. The results show that random forest models predict the test set outcomes with the highest accuracy. The identified contributory features include process features which agree with domain knowledge, as well as several different biomarker operational taxonomic units (OTUs). The results reinforce the notion that both chemical and biological features significantly affect bioreactor performance. However, they also indicate that the quality of the biological features can be improved by considering non-clustering methods, which may better represent the true behaviour within the OTU communities. Cluster analysis ; Hierarchical clustering ; Random forest ; Computer science ; Outcome (game theory) ; Data mining ; Artificial intelligence ; Machine learning ; Feature selection ; Biochemical engineering ; Bioreactor ; Set (abstract data type) ; Mathematics ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Viral Infectious Diseases and Gene Expression in Insects ; Gene expression and cancer classification
Authors: Liam Wilbraham | Reiner Sebastian Sprick | Kim E. Jelfs | Martijn A. Zwijnenburg
Source: ChemRxiv (preprint, 2019-03-12)
Field: Materials Science — Materials Chemistry

Mapping 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, and challenge common ideas behind copolymer design. 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. Overall, despite the prevalence of this concept in the literature, we observe that this phenomenon is relatively rare, and propose conditions that greatly enhance the likelihood of its experimental realisation. 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 ; Materials science ; Property (philosophy) ; Space (punctuation) ; Computer science ; Polymer ; Stille reaction ; Mathematics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Organic Electronics and Photovoltaics
Authors: Naser Hadi | Reza Alizadeh | Aligholi Niaei
Source: SHILAP Revista de lepidopterología (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Synthesis and characterization of MFI zeolite nanosheets for optimizing the dominant operational conditions of methanol to propylene process Nanosheets of MFI zeolite were successfully synthesized by hydrothermal method and were properly characterized by XRD, FE-SEM, EDX-dot mapping, TEM, FT-IR, N2 adsorption/desorption, NH3-TPD, ICP-AES and TGA techniques. The characterizations of MFI zeolite nanosheets suitably confirmed well synthesized MFI nanostructure, superior specific surface area (695.96m2.g-1) and great mesopores volume. Currently, the major concern in commercialized technologies of methanol to propylene (MTP) process is to discover the best operational conditions for maximizing propylene selectivity. Therefore, in present investigation the effectiveness of more significant operational conditions such as reaction temperature, methanol molar percent in feedstock and methanol WHSV on propylene selectivity was considered. For first time, conventional response surface methodology and an artificial neural network model coupled with genetic algorithm were appropriately employed to optimize MTP operational conditions. For providing the data bank of optimization, MTP reaction was carried out over the synthesized MFI zeolite nanosheets at various operational conditions. It was concluded that neuro-genetic method showed more reliable performance in optimizing MTP operational conditions with R2 value of 0.9998. Furthermore, time on stream examination was conducted over MFI zeolite nanosheets at optimal operational conditions which were suggested by neuro-genetic approach. At optimal operational conditions the catalytic life-time was sufficiently enhanced (101h). Materials science ; Zeolite ; Methanol ; Selectivity ; Catalysis ; Chemical engineering ; Adsorption ; Mesoporous material ; Desorption ; Specific surface area ; Nanotechnology ; Organic chemistry ; Chemistry Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Mohammad Atif Faiz Afzal | Mojtaba Haghighatlari | Sai Prasad Ganesh | Chong Cheng | Johannes Hachmann
Source: ChemRxiv (article, 2019-02-06)
Field: Materials Science — Polymers and Plastics

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining &lt;div&gt;We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.&lt;/div&gt; Virtual screening ; Throughput ; High-throughput screening ; Computer science ; Chemistry ; Software ; Code (set theory) ; Polarizability ; Feature (linguistics) ; Protocol (science) ; Data mining ; Computational science ; Drug discovery ; Molecule Synthesis and properties of polymers ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Martijn ten Bhömer | Hai‐Ning Liang | Difeng Yu | Yuanjin Liu | Yifan Zhang | Eva De Laat | Carola Leegwater
Source: Temes de disseny (article, 2019-07-15)
Field: Materials Science — Polymers and Plastics

Designing Predictive Tools for Personalized Functionalities in Knitted Performance Wear Developments of advanced textile manufacturing techniques—such as 3D body-forming knitwear machinery—allows the production of almost finalized garments, which require little to no further production steps to finalize the garment. Moreover, advanced knitting technology in combination with new materials enables the integration of localized functionalities within a garment on a “stitch by stitch level.” There is potential in enhancing the design tools for advanced knitting manufacturing through the use of technologies such as data gathering, machine learning, and simulation. This approach reflects the potential of Industry 4.0, as design, product development, and manufacturing are moving closer together. However, there is still limited knowledge at present about how these new technologies and tools can have an impact on the creative design process. The case study presented in this paper explores the potential of predictive software design tools for fashion designers who are developing personalized advanced functionalities in textile products. The main research question explored in this article is: “How can designers benefit from intelligent design software for the manufacturing of advanced personalized functionalities in textile products?”. Within this larger research question three sub-research questions are explored: (1) What kind of advanced functionalities can be considered for the personalization process of knitwear? (2) How to design interactions and interfaces that use intelligent predictive algorithms to stimulate creativity during the fashion design process? (3) How will predictive software impact the manufacturing process for other stakeholders and production steps? These questions are investigated through the analysis of a Research Through Design case study, in which several predictive algorithms were compared and implemented in a user interface that would aid knitwear designers during the development process of high-performance running tights. Personalization ; Manufacturing engineering ; Process (computing) ; Computer science ; Product design ; Software ; Textile ; Clothing ; Product (mathematics) ; New product development ; Systems engineering ; Engineering ; World Wide Web Textile materials and evaluations ; Fashion and Cultural Textiles ; Crafts, Textile, and Design
Authors: Lujun Zhang | Xiaodong Huang | Chunyan Wang | Chun Yang
Source: Energy & Fuels (article, 2019-07-08)
Field: Chemistry — Analytical Chemistry

Insight into Polycyclic Aromatic Hydrocarbons in Unconventional Oil via Concentration-Resolved Fluorescence Spectroscopy Coupled with Data Mining Techniques The exploration, production, and transportation of unconventional oils have attracted increasing attention for their economic value and environmental pressure. However, the previous analytical techniques of conventional oils encounter bottlenecks because of the separation difficulties of the unresolved complex mixtures. It is of great value to develop new methods to pursue a more detailed investigation of the chemical compositions of unconventional oil. Concentration-resolved fluorescence spectroscopy (CRFS) was developed to characterize the multi-dimensional fluorescence features of polycyclic aromatic hydrocarbons in unconventional oil samples. Laboratory simulation experiments of thermal evolution and biodegradation were designed to verify the effectiveness of CRFS compared to gas chromatography–flame ionization detector and gas chromatography–mass spectrometry. Dual-tree complex wavelet analysis and principal component analysis were used to remove redundant information and extract more detailed and effective information on CRFS spectra, and then a generalized regression neural network was used to classify and identify crude oil samples of different heavy oil species. With 100% accuracy, this computer data processing combined CRFS method is proven to be fast, accurate, and economical and is expected to be an effective method to solve the present problem of unconventional oil analysis. Asphaltene ; CRFS ; Flame ionization detector ; Chemistry ; Principal component analysis ; Biological system ; Mass spectrometry ; Gas chromatography ; Computer science ; Chromatography ; Artificial intelligence ; Conditional random field ; Organic chemistry Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Oil Spill Detection and Mitigation
Authors: Agus S. Soemantri | Miskiyah | Juniawati | Wahyu Diyono
Source: IOP Conference Series Earth and Environmental Science (article, 2019-09-01)
Field: Chemistry — Analytical Chemistry

Rapid Testing of Aflatoxin by Using Image Processing and Artificial Neural Network Abstract Aflatoxin can be recognized clearly by using UV-light. This information is very important to develop the device for detecting the aflatoxin inside the corn by using image processing. Current research related to identification of aflatoxin has been conducted manually by the experts. This method have some weakness including subjectivity factors, inconsistent result, and time required used. Based on the problems above, it needed to create the rapid testing device for identification of aflatoxin with consistence result, accurate and easy tooperate. The research aimed to develop the device for rapid testing of aflatoxin. The method used image processing and artificial neural network. The raw material used was the corn. The image of aflatoxin taken by using digital camera (Gopro 4) and processed by image processing program. ANN model was developed with 10 input parameters, 20 hidden layers and 4 targets. The fourth of targets above were the size of aflatoxin such as 1 - 2 ppb, 2 – 3 ppb, 3 – 4 ppb dan 4 - 5 ppb. The result showed that the characteristics of image were very specific among the input parameters and the most influential to recognize the object was the longest diameter of aflatoxin. The result of training showed that the size of aflatoxin can be recognized by the system was 100%, while the validation by using the different sample was 99%. Based on this research can be conclude that image processing can be used as the rapid testing of aflatoxin. Aflatoxin ; Artificial intelligence ; Artificial neural network ; Image processing ; Identification (biology) ; Pattern recognition (psychology) ; Digital image processing ; Digital image ; Computer science ; Computer vision ; Image (mathematics) ; Biotechnology ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: Mojtaba Mohammadpoor | Roya Mohammadzadeh Kakhki | Hakimeh Assadi
Source: Current Pharmaceutical Analysis (article, 2019-06-27)
Field: Chemistry — Analytical Chemistry

A Bayesian Regularized Artificial Neural Network for Simultaneous Determination of Loratadine, Naproxen and Diclofenac in Wastewaters Naproxen ; Artificial neural network ; Loratadine ; Mean squared error ; Chemistry ; Backpropagation ; Artificial intelligence ; Pattern recognition (psychology) ; Chromatography ; Biological system ; Statistics ; Mathematics ; Computer science Analytical Methods in Pharmaceuticals ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Sensors