jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Erhan Çalışkan | Yusuf Sevim
Source: Applied Ecology and Environmental Research (article, 2019-01-01)
Field: Materials Science — Polymers and Plastics

A COMPARATIVE STUDY OF ARTIFICIAL NEURAL NETWORKS AND MULTIPLE REGRESSION ANALYSIS FOR MODELING SKIDDING TIME One of the most important functions of forests is providing raw material for wood. Timber extraction is among the most technically demanding, expensive and time consuming activities for wood raw material production. The analysis of timber extraction activities is complex and it is quite difficult to model them. Therefore, Artificial Neural Networks (ANN) frequently used as a modelling tool in the analysis of complex problems have been used to solve this issue. The aim of this study is to investigate the feasibility of ANN's including Multilayer Perceptron (MLP), Cascade Forward Back Propagation (CFBP) and compare the predictions for total time during log skidding operations stations in Eastern Black sea region (Giresun Forest District Directorates) of Turkey with those of the Multiple Regression Analysis (MRA). Moreover, standard times are calculated, and affective factors are determined, after which the effectiveness levels are evaluated at each working stage by way of timing determinations when skidder is used for timber extraction. The comparison of models were carried out by using the correlation coefficient, mean squared error, root mean square errors and mean absolute error. The comparison results indicate that MLP and CFBP models are better at predicting the total time during log skidding operations in comparison with the MRA model. These results have put forth that artificial neural networks have a greater prediction in comparison with multiple regression analysis for predicting the skidding time in timber extraction operations and that less erroneous results are obtained. It is observed that artificial neural networks can be preferred in cases for which the multiple regression analysis predictions have not been met and the analysis cannot be performed. Artificial neural network ; Computer science ; Regression analysis ; Artificial intelligence ; Machine learning Textile materials and evaluations ; Color perception and design
Authors: Martin Mortensen | Jacob Krall | Kenneth T. Kongstad | Benjamin M. Brygger | Ombretta Lenzi | Pierre Francotte | Troels E. Sørensen | Birgitte Nielsen | Anders A. Jensen | Trevor G. Smart | Bente Frølund
Source: ACS Chemical Neuroscience (article, 2019-10-07)
Field: Materials Science — Materials Chemistry

Developing New 4-PIOL and 4-PHP Analogues for Photoinactivation of γ-Aminobutyric Acid Type A Receptors The critical roles played by GABA<sub>A</sub> receptors as inhibitory regulators of excitation in the central nervous system has been known for many years. Aberrant GABA<sub>A</sub> receptor function and trafficking deficits have also been associated with several diseases including anxiety, depression, epilepsy, and insomnia. As a consequence, important drug groups such as the benzodiazepines, barbiturates, and many general anesthetics have become established as modulators of GABA<sub>A</sub> receptor activity. Nevertheless, there is much we do not understand about the roles and mechanisms of GABA<sub>A</sub> receptors at neural network and systems levels. It is therefore crucial to develop novel technologies and especially chemical entities that can interrogate GABA<sub>A</sub> receptor function in the nervous system. Here, we describe the chemistry and characterization of a novel set of 4-PIOL and 4-PHP analogues synthesized with the aim of developing a toolkit of drugs that can photoinactivate GABA<sub>A</sub> receptors. Most of these new analogues show higher affinities/potencies compared with the respective lead compounds. This is indicative of cavernous areas being present near their binding sites that can be potentially associated with novel receptor interactions. The 4-PHP azide-analogue, <b>2d</b>, possesses particularly impressive nanomolar affinity/potency and is an effective UV-inducible photoinhibitor of GABA<sub>A</sub> receptors with considerable potential for photocontrol of GABA<sub>A</sub> receptor function <i>in situ</i>. Receptor ; Chemistry ; Aminobutyric acid ; gamma-Aminobutyric acid ; Pharmacology ; Stereochemistry ; Biochemistry ; Biology Photochromic and Fluorescence Chemistry ; Receptor Mechanisms and Signaling ; Cancer, Stress, Anesthesia, and Immune Response
Authors: Yehia Mansour | Yann Battie | Aotmane En Naciri | N. Chaoui
Source: Optics Letters (article, 2019-06-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial neural network for the classification of nanoparticles shape distributions A new methodology, to the best of our knowledge, is developed to determine the shape distribution profile of gold nanoparticles (NPs) from optical spectroscopic measurements. Indeed, an artificial neural network (ANN) approach was introduced to classify Au NP shape distributions from their normalized absorption spectra. This ANN quantitatively analyzes the absorption spectra and provides the posterior probability to have a bimodal or unimodal shape distribution. Several colloidal suspensions were considered to investigate the robustness of the ANN approach. The comparison between ANN classification and TEM analysis was also given and discussed. We demonstrate that ANN classification is a suitable tool to inspect rapidly Au colloidal suspensions after their synthesis. Artificial neural network ; Robustness (evolution) ; Nanoparticle ; Materials science ; Colloidal gold ; Optics ; Biological system ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Nanotechnology ; Physics ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification
Authors: Sarah N. Hankins | Lars Kotthoff | Ray S. Fertig
Source:  (article, 2019-10-15)
Field: Materials Science — Biomaterials

Bio-like Composite Microstructure Designs for Enhanced Damage Tolerance via Machine Learning Variations of unique and tailored composite microstructures have been observed in nature and have served as templates for the development of new synthetic materials. Microstructures are studied in fish scales for their penetration resistance, in spider webs for energy absorption, and in seashells and bone for their strength and toughness. However, it has proven difficult to reproduce the properties found in natural materials due to the interaction between the intricate structures at different length scales. Rather than attempting to replicate these materials (biomimetics), the focus of this work is to use a bio-inspired pattern generation algorithm to search for new topologies that outperform traditional composite structures due to their naturelike design. The bio-inspired pattern generation algorithm employed in this research is known as the Gray-Scott model. This model was selected due to its unique ability to manufacture patterns that propagate with time, allowing the reinforcement volume fraction of the composite structure to be controlled. The model is capable of producing Turing patterns, propagating wave fronts, homogeneous oscillations, and chaos. Traditionally, Turing models have been primarily studied for their applications in morphogenesis and pattern development. However, this research extends the application of the Gray-Scott model by investigating the patterns as physical load bearing structures. A methodology was developed by which the patterns can be converted to structures, analyzed for a desired mechanical property, and optimized via Bayesian machine learning algorithms that yield an improvement of the average quality of structures produced by nearly a factor of 10. Biomimetics ; Computer science ; Turing ; Toughness ; Artificial intelligence ; Composite number ; Template ; Materials science ; Biological system ; Algorithm ; Nanotechnology ; Composite material Calcium Carbonate Crystallization and Inhibition
Authors: Farhad Ajorkaran | Alireza Cheshmeh Sefidi
Source: Petroleum Science and Technology (article, 2019-07-04)
Field: Chemistry — Analytical Chemistry

Application of RBF-ANN in prediction of natural gas density in different operational conditions The degree of accuracy in prediction of different processes of gas engineering is extensively dependent on gas properties. One of the dominant properties which has straight effects on calculation and performance of different parts of gas industries is natural gas density. Due to this fact, in this paper, radial basis function artificial neural network (RBF-ANN) was used as novel approach to estimate gas density in terms of molecular weight, critical pressure and critical temperature of gas, pressure and temperature. To prepare and validate RBF-ANN model, a large and reliable experimental data bank was gathered from literature. A comprehensive analysis which include statistical and different graphical analysis were done to evaluate the performance. The coefficients of determination (R2) were determined as 0.99995 and 0.99993 for training and testing phases respectively. The comparisons illustrate that the RBF-ANN has great potential in prediction of natural gas density at different operational conditions. Artificial neural network ; Natural gas ; Radial basis function ; Computer science ; Machine learning ; Chemistry Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Fault Detection and Control Systems
Authors: Graham Roberts | Rajat Sainju | Brian Hutchinson | Mychailo B. Toloczko | Danny J. Edwards | Yuanyuan Zhu
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

DefectNet – A Deep Convolutional Neural Network for Semantic Segmentation of Crystallographic Defects in Advanced Microscopy Images Journal Article DefectNet – A Deep Convolutional Neural Network for Semantic Segmentation of Crystallographic Defects in Advanced Microscopy Images Get access Graham Roberts, Graham Roberts Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Rajat Sainju, Rajat Sainju Department of Materials Science and Engineering, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA Search for other works by this author on: Oxford Academic Google Scholar Brian Hutchinson, Brian Hutchinson Computer Science Department, Western Washington University, Bellingham, WA 98225, USAComputing & Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Mychailo B Toloczko, Mychailo B Toloczko Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Danny J Edwards, Danny J Edwards Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Yuanyuan Zhu Yuanyuan Zhu Nuclear Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USADepartment of Materials Science and Engineering, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA Corresponding author: yuanyuan.2.zhu@uconn.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 164–165, https://doi.org/10.1017/S1431927619001557 Published: 01 August 2019 Library science ; National laboratory ; Art history ; History ; Computer science ; Engineering ; Engineering physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Elizaveta M. Solovyeva | Vladimir Kopysov | Aleksandr Y. Pereverzev | Anna A. Lobas | Sergei A. Moshkovskii | Mikhail V. Gorshkov | Oleg V. Boyarkin
Source: Analytical Chemistry (article, 2019-05-01)
Field: Chemistry — Spectroscopy

Method for Identification of Threonine Isoforms in Peptides by Ultraviolet Photofragmentation of Cold Ions Identification of isomeric amino acid residues in peptides and proteins is challenging but often highly desired in proteomics. One of the practically important cases that require isomeric assignments is that associated with single-nucleotide polymorphism substitutions of Met residues by Thr in cancer-related proteins. These genetically encoded substitutions can yet be confused with the chemical modifications, arising from protein alkylation by iodoacetamide, which is commonly used in the standard procedure of sample preparation for proteomic analysis. Similar to the genetically encoded mutations, the alkylation also induces a conversion of methionine residues, but to the iso-threonine form. Recognition of the mutations therefore requires isoform-sensitive detection techniques. Herein, we demonstrate an analytical method for reliable identification of isoforms of threonine residues in tryptic peptides. It is based on ultraviolet photodissociation mass spectrometry of cryogenically cooled ions and a machine-learning algorithm. The measured photodissociation mass spectra exhibit isoform-specific patterns, which are independent of the residues adjacent to threonine or iso-threonine in a peptide sequence. A comprehensive metric-based evaluation demonstrates that, being calibrated with a set of model peptides, the method allows for isomeric identification of threonine residues in peptides of arbitrary sequence. Chemistry ; Threonine ; Peptide ; Mass spectrometry ; Gene isoform ; Biochemistry ; Amino acid ; Combinatorial chemistry ; Serine ; Chromatography ; Phosphorylation Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Enzyme Structure and Function
Authors: Mohammad Farajvand | Vahid Kiarostami | Mehran Davallo | A.M. Ghaedi
Source: Environmental Monitoring and Assessment (article, 2019-04-17)
Field: Chemistry — Analytical Chemistry

Simultaneous extraction of Cu2+ and Cd2+ ions in water, wastewater, and food samples using solvent-terminated dispersive liquid–liquid microextraction: optimization by multiobjective evolutionary algorithm based on decomposition Disperser ; Extraction (chemistry) ; Detection limit ; Solvent ; Aqueous solution ; Response surface methodology ; Evolutionary algorithm ; Central composite design ; Chemistry ; Metal ions in aqueous solution ; Algorithm ; Chromatography ; Materials science ; Ion ; Analytical Chemistry (journal) ; Mathematics ; Mathematical optimization ; Organic chemistry Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Peter C. Meltzer | Marcelo Daniel Gutierrez Mallea | Peter J. Bentley
Source: arXiv (Cornell University) (preprint, 2019-05-08)
Field: Materials Science — Materials Chemistry

PiNet: A Permutation Invariant Graph Neural Network for Graph\n Classification We propose an end-to-end deep learning learning model for graph\nclassification and representation learning that is invariant to permutation of\nthe nodes of the input graphs. We address the challenge of learning a fixed\nsize graph representation for graphs of varying dimensions through a\ndifferentiable node attention pooling mechanism. In addition to a theoretical\nproof of its invariance to permutation, we provide empirical evidence\ndemonstrating the statistically significant gain in accuracy when faced with an\nisomorphic graph classification task given only a small number of training\nexamples. We analyse the effect of four different matrices to facilitate the\nlocal message passing mechanism by which graph convolutions are performed vs. a\nmatrix parametrised by a learned parameter pair able to transition smoothly\nbetween the former. Finally, we show that our model achieves competitive\nclassification performance with existing techniques on a set of molecule\ndatasets.\n Pooling ; Invariant (physics) ; Theoretical computer science ; Computer science ; Graph ; Graph property ; Line graph ; Combinatorics ; Artificial intelligence ; Discrete mathematics ; Mathematics ; Voltage graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Aliaksei Mazheika | Yang‐Gang Wang | Rosendo Valero | Luca M. Ghiringhelli | Francesc Viñes | Francesc Illas | Sergey V. Levchenko | Matthias Scheffler
Source: MPG.PuRe (Max Planck Society) (preprint, 2019-12-13)
Field: Materials Science — Materials Chemistry

Ab initio data-analytics study of carbon-dioxide activation on semiconductor oxide surfaces The excessive emissions of carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) into the atmosphere threaten to shift the CO&lt;sub&gt;2&lt;/sub&gt; cycle planet-wide and induce unpredictable climate changes. Using artificial intelligence (AI) trained on high-throughput first principles based data for a broad family of oxides, we develop a strategy for a rational design of catalytic materials for converting CO&lt;sub&gt;2&lt;/sub&gt; to fuels and other useful chemicals. We demonstrate that an electron transfer to the π-antibonding orbital of the adsorbed molecule and the associated bending of the initially linear molecule, previously proposed as the indicator of activation, are insufficient to account for the good catalytic performance of experimentally characterized oxide surfaces. Instead, our AI model identifies the common feature of these surfaces in the binding of a molecular O atom to a surface cation, which results in a strong elongation and therefore weakening of one molecular C-O bond. This finding suggests using the C-O bond elongation as an indicator of CO&lt;sub&gt;2&lt;/sub&gt; activation. Based on these findings, we propose a set of new promising oxide-based catalysts for CO&lt;sub&gt;2&lt;/sub&gt; conversion, and a recipe to find more. Antibonding molecular orbital ; Oxide ; Catalysis ; Materials science ; Molecule ; Ab initio ; Carbon dioxide ; Adsorption ; Computational chemistry ; Chemical physics ; Chemistry ; Physical chemistry ; Electron ; Physics ; Atomic orbital ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Fumiaki Tanaka | Hiroshi Sato | Naoki Yoshii | H. Matsui
Source: IEEE Transactions on Semiconductor Manufacturing (article, 2019-09-24)
Field: Materials Science — Materials Chemistry

Materials Informatics for Process and Material Co-Optimization In semiconductor manufacturing, fabrication processes and their materials should be properly co-optimized to achieve required processing results within reasonable development duration and acceptable cost. Unfortunately, it is a very time-consuming procedure, because the number of possible combinations of process/material candidates is very large. Here, we develop a methodology for co-optimization of processes and their materials. We successfully constructed a prediction model for dry-etching of high-k materials (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.65). Also, it was proven that considering both the materials and processes is needed for accurate prediction of etching rates. By trying only <; 0.00001% of all possible process/material candidates with this model and Bayesian optimization, we can find new combinations of gasses and their processes for more than 100 times higher etching rates than that with a traditional gas/process condition. Furthermore, we discussed that accurate prediction can be made by using a combination of the Bayesian optimization with LASSO and materials knowledge from related scientific papers. Future work will focus on validating the versatility of our methodology by applying it to other development items. Process (computing) ; Process optimization ; Process engineering ; Informatics ; Computer science ; Manufacturing engineering ; Engineering ; Materials science ; Chemical engineering ; Electrical engineering Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Manufacturing Process and Optimization
Authors: Pavel Karpov | Guillaume Godin | Igor V. Tetko
Source: ChemRxiv (preprint, 2019-10-21)
Field: Materials Science — Materials Chemistry

Transformer-CNN: Fast and Reliable Tool for QSAR We present SMILES-embeddings derived from internal encoder state of a Transformer model trained to canonize SMILES as a Seq2Seq problem. Using CharNN architecture upon the embeddings results in a higher quality QSAR/QSPR models on diverse benchmark datasets including regression and classification tasks. The proposed Transformer-CNN method uses SMILES augmentation for training and inference, and thus the prognosis grounds on an internal consensus. Both the augmentation and transfer learning based on embedding allows the method to provide good results for small datasets. We discuss the reasons for such effectiveness and draft future directions for the development of the method. The source code and the embeddings are available on https://github.com/bigchem/transformer-cnn, whereas the OCHEM environment (https://ochem.eu) hosts its on-line implementation. Transformer ; Computer science ; Inference ; Embedding ; Encoder ; Artificial intelligence ; Transfer of learning ; Quantitative structure–activity relationship ; Benchmark (surveying) ; Machine learning ; Engineering Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Domain Adaptation and Few-Shot Learning