jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yoojin Chung | Hyunwoo Kim
Source: International Journal of Internet Technology and Secured Transactions (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Automated analysis of blood vessel permeability using deep learning This paper presented a deep learning-based approach using CNN that automatically detects changes of permeability in the blood vessel network after exposing the vessels to chemicals. Because chemicals can affect the permeability of the vessels, it is important to characterise and quantify the changes to blood vessels accurately from time-lapse microscope images. We increased the number of data by applying diverse augmentation methods to 2,800 images. In this paper, we also devised a method of automatically labelling images as to whether there is any change in the blood vessel to obtain learning data for CNN by pre-treating images using diverse image processing methods and separately establishing thresholds for different images. To verify the performance of the developed CNN, we used 4-fold cross validation and blood vessel changes were automatically detected with an accuracy of 92.07% in the experiment. Computer science ; Blood vessel ; Permeability (electromagnetism) ; Artificial intelligence ; Chemistry Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Brain Tumor Detection and Classification
Authors: Ravindra K. Garmodea | Vivek R. Gavala | Sachin G. Solankea | Sanjay D. Nikhade
Source:  (article, 2021-10-07)
Field: Materials Science — Polymers and Plastics

Composite Material Selection Analytics using Statistical and MCDM Techniques for Cotton-Glass Fibre This article pertains to composite materials integrated with different materials like glass fiber/cotton/fillers characteristics for prediction of selection analytics. These composite materials are integrated by different layup process for various mechanical properties to find the perfect composite combination. There has to be a proper selection procedure technique to find out the proportion or probability of better composite material characterization. This article related to different types of multi-criteria decision making (MCDM) techniques and recommends use of these techniques for better decision-making process where the composite materials selections are limited and attributes of mechanical characteristics are in large numbers. Material selection prediction models with application prospects for final product characteristics are also discussed here for product designing and development. This paper also gives the glimpse on machine learning technique analysis for composite selection criteria as compared with MCDM techniques for future references and study. We have also studied some in-depth research work which has proved the material selection for its robust applications using MCDM Techniques. The study was tested with small demonstration that the composite dataset and material properties could be used in any MCDM approach, such as the COPRAS method. The co-relation and dependable matrix are built with high precision using the supplied independent variables. Multiple-criteria decision analysis ; Material selection ; Selection (genetic algorithm) ; Computer science ; Process (computing) ; Analytics ; Composite number ; Process engineering ; Machine learning ; Materials science ; Engineering ; Operations research ; Data mining ; Algorithm ; Composite material Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: M.H. El-Axir | Y.A.-H. Mashal | Nizar Taha | Asmaa A. Rashed
Source: Open MIND (article, 2021-11-08)
Field: Materials Science — General Materials Science

Single and Multi-Optimization of Roller Burnishing Process Parameters for AL-Alloy 7075 In the present experimental investigation, Single and multi-optimization of three methodologies (Artificial Neural Network (ANN), Response surface methodology (RSM) and Taguchi method) for prediction of the results of roller burnishing process parameters on surface roughness, surface micro hardness, surface out of roundness, and change in diameter for AL-alloy 7075 have been studied. It's known that the part's performance is strongly influenced by the surface quality and burnishing process is one of the most significant surface finishing processes. It has been increasingly applied in manufacturing due to its several advantages that are lacked in other finishing processes. Roller Burnishing is a cold working process which produces a fine surface finish by the planetary rotation of hardened rolls over a bored or turned metal surface. This work studies the effect of burnishing speed, number of passes, depth of penetration and burnishing feed on surface roughness, surface microhardness, change in diameter, and surface out of roundness of AL-alloy 7075. The experiments are designed based on Taguchi experimental design technique. The results obtained have shown that the best network is 4- 50-1 for both average surface roughness (Ra) and microhardness (HV), 4-30-1 for surface out of roundness (OR), and 4-40-1 for change in diameter (ΔD). In the case of multi-response optimization, the ANN architectures 4-40-4 was found as the best networks in this investigation. Burnishing (metal) ; Alloy ; Materials science ; Metallurgy ; Process (computing) ; Process optimization ; Computer science ; Engineering ; Chemical engineering Material Properties and Applications ; Aluminum Alloy Microstructure Properties ; Metal Alloys Wear and Properties
Authors: Dávid Péter Kovács | Cas van der Oord | Jiří Kučera | Alice E. A. Allen | Daniel J. Cole | Christoph Ortner | Gábor Csányi
Source: ChemRxiv (preprint, 2021-10-12)
Field: Materials Science — Materials Chemistry

Linear Atomic Cluster Expansion Force Fields forOrganic Molecules: beyond RMSE We demonstrate that fast and accurate linear force fields can be built for molecules using the Atomic Cluster Expansion (ACE) framework. The ACE models parametrize the Potential Energy Surface in terms of body ordered symmetric polynomials making the functional form reminiscent of traditional molecular mechanics force fields. We show that the 4 or 5-body ACE force fields improve on the accuracy of the empirical force fields by up to a factor of 10, reaching the accuracy typical of recently proposed machine learning based approaches. We not only show state of the art accuracy and speed on the widely used MD17 and ISO17 benchmark datasets, but also go beyond RMSE by comparing a number of ML and empirical force fields to ACE on more important tasks such as normal mode prediction, high temperature molecular dynamics, dihedral torsional profile prediction and even bond breaking. We also demonstrate the smoothness, transferability and extrapolation capabilities of ACE on a new challenging benchmark dataset comprising a potential energy surface of a flexible drug-like molecule. Extrapolation ; Force field (fiction) ; Benchmark (surveying) ; Smoothness ; Cluster (spacecraft) ; Statistical physics ; Potential energy surface ; Potential energy ; Molecular dynamics ; Cluster expansion ; Computer science ; Physics ; Mathematics ; Molecule ; Classical mechanics ; Artificial intelligence ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: M.L. Lynch | Sarah Bowman
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Modeling and machine learning tools to improve image processing in crystallization screening Imaging methods are vital to the process of screening for successful crystallization hits. When screening is carried out in a high-throughput manner, the sheer volume of images needing processing is enormous, requiring a large commitment of time and effort to locate successful crystallization conditions. In addition, different imaging modes are subject to different complications, including optical aberrations, resolution limitations, and behavior with respect to obstruction, etc. We discuss our recent work on computational and modeling approaches to improve image processing and analysis for crystallization screening. These include wavelet transforms for image reconstruction, denoising, and registration, and machine learning approaches to process and classify image content, enabling easier identification of crystal hits. We discuss applications of our methods to standard brightfield imaging, as well as explore their capabilities in multimodal image integration, combining information from UV-TPEF (two-photon excited ultraviolet fluorescence) and SHG (second harmonic generation) nonlinear imaging modes. These computational tools are developed to facilitate steps towards automated imaging to streamline crystal detection and acquisition. Crystallization ; Computer science ; Image processing ; Image (mathematics) ; Artificial intelligence ; Materials science ; Process engineering ; Chemical engineering ; Engineering Crystallization and Solubility Studies ; Minerals Flotation and Separation Techniques
Authors: Ji Woong Yu | Min Young Ha | Bumjoon Seo | Won Bo Lee
Source: arXiv (Cornell University) (preprint, 2021-10-27)
Field: Materials Science — Materials Chemistry

A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization The combination of neural network potential (NNP) with molecular simulations plays an important role in an efficient and thorough understanding of a molecular system's potential energy surface (PES). However, grasping the interplay between input features and their local contribution to NNP is growingly evasive due to heavy featurization. In this work, we suggest an end-to-end model which directly predicts per-atom energy from the coordinates of particles, avoiding expert-guided featurization of the network input. Employing self-attention as the main workhorse, our model is intrinsically equivariant under the permutation operation, resulting in the invariance of the total potential energy. We tested our model against several challenges in molecular simulation problems, including periodic boundary condition (PBC), $n$-body interaction, and binary composition. Our model yielded stable predictions in all tested systems with errors significantly smaller than the potential energy fluctuation acquired from molecular dynamics simulations. Thus, our work provides a minimal baseline model that encodes complex interactions in a condensed phase system to facilitate the data-driven analysis of physicochemical systems. Equivariant map ; Statistical physics ; Binary number ; Molecular dynamics ; Computer science ; Biological system ; Pairwise comparison ; Cyclic permutation ; Potential energy surface ; Potential energy ; Work (physics) ; Physics ; Topology (electrical circuits) ; Theoretical computer science ; Mathematics ; Artificial intelligence ; Classical mechanics ; Molecule ; Quantum mechanics ; Pure mathematics ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Lorenzo V. Mugnai | A. F. Al-Refaie | Andrea Bocchieri | Quentin Changeat | E. Pascale | G. Tinetti
Source:  (preprint, 2021-07-21)
Field: Chemistry — Analytical Chemistry

Alfnoor: a population study on Ariel's low resolution transmission spectra. In the next decade, the Ariel Space Telescope will provide the first statistical data set of exoplanet spectra, performing spectroscopic observations of about 1000 exoplanets in the wavelength range 0.5 - 7.8 micron during its Reconnaissance Survey. The Ariel Reconnaissance Survey has been designed specifically to identify planets without molecular features in their atmosphere, and select targets (about 500) for accurate chemical characterisation with higher SNR spectroscopic observations.In this work, we investigate the information content of Ariel's Reconnaissance Survey low resolution transmission spectra. We produce different planetary populations using the Ariel candidate target list, randomizing the planetary atmospheres, and simulating the Ariel observations using the Alfnoor software. Then we analyse the dataset, getting three different results:(1) We present a solid strategy that will allow selecting candidate planets to be reobserved in an Ariel's higher resolution, using a chi-squared based metric to identify the flat spectra.(2) Because the reconnaissance survey is not optimised for spectral retrieval, we propose a novel model-independent metric to preliminary classify exoplanets by their atmospheric composition. Without any other planetary information than the spectrum, our metric proves capable of indicating the presence of a molecule when its abundance in the atmosphere is in excess of 10-4 in mixing ratio.(3) We introduce the possibility of finding other methods to better exploit the data scientific content. We report as an example of possible strategies, a preliminary study involving Deep and Machine Learning algorithms. We show that their performance in identifying the presence of a certain molecule in the spectra is marginally better than our metric for some of these algorithms, while others outperform the metric.&amp;#160;We conclude that the the Ariel reconnaissance survey is effective in detecting exoplanets manifesting featureless spectra, and we further show that the data collected in this observing mode have a rich scientific content, allowing for a first chemical classification of the observed targets. Exoplanet ; Planet ; Physics ; Population ; Spectral line ; Spectral resolution ; Metric (unit) ; Atmosphere (unit) ; Astrophysics ; Astronomy ; Meteorology Spectroscopy and Chemometric Analyses
Authors: Evgeny Zhvansky | Anatoly Sorokin | Denis S. Zavorotnyuk | Vsevolod Shurkhay | Vasiliy A. Eliferov | Denis S. Bormotov | Daniil G. Ivanov | Potapov Aa
Source: F1000Research (preprint, 2021-01-21)
Field: Chemistry — Spectroscopy

Comparison of different machine learning methods and dimensionality reduction for classification astrocytoma and glioblastoma tissues by mass spectra <ns3:p> <ns3:bold>Background:</ns3:bold> Recently developed methods of ambient ionization allow rapid obtaining of large mass spectrometric datasets, which have a great application in biological and medical analysis. One of the areas that could employ such analysis is neurosurgery. The fast <ns3:italic>in situ</ns3:italic> identification of dissected tissues could assist the neurosurgery procedure. The additional information about tumor could help the tumor border monitoring. In this paper, tumor tissues of astrocytoma and glioblastoma are compared, as their identifications during surgery could influence the extent of resection and, hence, the median and overall survival. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> Mass spectrometric profiles of brain tumor tissues contain molecular information, which is rather hard to interpret in terms of identifications of individual molecules. The machine learning algorithms are employed for the fast automated mass spectra classification. Different algorithms of dimensionality reduction are considered to process the mass spectra before the classification task, as the initial dimensionality of mass spectra is too high compared with the number of mass spectra. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> Different classifiers are compared for both just preprocessed data and after dimensionality reduction. The Non-Negative Matrix Factorization appears to be the most effective dimensionality reduction algorithm. The random forest algorithm demonstrated the most robust appearance on the tested data. Also, the comparison of the accuracy of the trained classifier on the mass spectra of tissues measured with different instruments and different resolution is provided in the paper. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> Machine learning classifiers overfit the raw mass spectrometric data. The dimensionality reduction allows the classification of both train and test data with 88% accuracy. Positive mode data provides better accuracy. A combination of principal component analysis and AdaBoost algorithms appears to be most robust to changing the instrument and conditions. </ns3:p> Dimensionality reduction ; Artificial intelligence ; Curse of dimensionality ; Mass spectrum ; Glioblastoma ; Machine learning ; Pattern recognition (psychology) ; Computer science ; Mass spectrometry ; Mathematics ; Chemistry ; Medicine ; Cancer research ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Neuroscience and Neuropharmacology Research
Authors: Rasmus Tranås | Ole Martin Løvvik | Oliver Tomić | Kristian Berland
Source: Computational Materials Science (preprint, 2021-10-20)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis Thermal conductivity ; Lattice (music) ; Principal component analysis ; Machine learning ; Artificial intelligence ; Feature selection ; Computer science ; Density functional theory ; Materials science ; Random forest ; Statistical physics ; Algorithm ; Mathematics ; Condensed matter physics ; Chemistry ; Physics ; Computational chemistry ; Composite material Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Rasmus Tranås | Ole Martin Løvvik | Oliver Tomić | Kristian Berland
Source: arXiv (Cornell University) (preprint, 2021-07-08)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity of half-Heuslers with density functional\n theory and machine learning: Enhancing predictivity by active sampling with\n principal component analysis Low lattice thermal conductivity is essential for high thermoelectric\nperformance of a material. Lattice thermal conductivity is often computed using\ndensity functional theory (DFT), typically at a high computational cost.\nTraining machine learning models to predict lattice thermal conductivity could\noffer an effective procedure to identify low lattice thermal conductivity\ncompounds. However, in doing so, we must face the fact that such compounds can\nbe quite rare and distinct from those in a typical training set. This\ndistinctness can be problematic as standard machine learning methods are\ninaccurate when predicting properties of compounds with features differing\nsignificantly from those in the training set. By computing the lattice thermal\nconductivity of 122 half-Heusler compounds, using the temperature-dependent\neffective potential method, we generate a data set to explore this issue. We\nfirst show how random forest regression can fail to identify low lattice\nthermal conductivity compounds with random selection of training data. Next, we\nshow how active selection of training data using feature and principal\ncomponent analysis can be used to improve model performance and the ability to\nidentify low lattice thermal conductivity compounds. Lastly, we find that\nactive learning without the use of DFT-based features can be viable as a\nquicker way of selecting samples.\n Thermal conductivity ; Principal component analysis ; Lattice (music) ; Feature selection ; Artificial intelligence ; Machine learning ; Random forest ; Computer science ; Density functional theory ; Materials science ; Statistical physics ; Algorithm ; Condensed matter physics ; Thermodynamics ; Chemistry ; Physics ; Computational chemistry Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Anna Himmelhuber | Stephan Grimm | Sonja Zillner | Mitchell Joblin | Martin Ringsquandl | Thomas A. Runkler
Source: arXiv (Cornell University) (preprint, 2021-12-03)
Field: Materials Science — Materials Chemistry

Combining Sub-Symbolic and Symbolic Methods for Explainability Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed to provide insights into the GNN decision making process. These are first important steps on the way to explainability, but the generated explanations are often hard to understand for users that are not AI experts. To overcome this problem, we introduce a conceptual approach combining sub-symbolic and symbolic methods for human-centric explanations, that incorporate domain knowledge and causality. We furthermore introduce the notion of fidelity as a metric for evaluating how close the explanation is to the GNN's internal decision making process. The evaluation with a chemical dataset and ontology shows the explanatory value and reliability of our method. Computer science ; Connectionism ; Fidelity ; Artificial intelligence ; The Symbolic ; Metric (unit) ; Process (computing) ; Machine learning ; Theoretical computer science ; Artificial neural network ; Programming language Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Boyuan Ma | Xiang Yin | Xiaojuan Ban | Haiyou Huang | Neng Zhang | Hao Wang | Weihua Xue
Source: arXiv (Cornell University) (preprint, 2021-11-09)
Field: Materials Science — Materials Chemistry

Data privacy protection in microscopic image analysis for material data mining Recent progress in material data mining has been driven by high-capacity models trained on large datasets. However, collecting experimental data has been extremely costly owing to the amount of human effort and expertise required. Therefore, material researchers are often reluctant to easily disclose their private data, which leads to the problem of data island, and it is difficult to collect a large amount of data to train high-quality models. In this study, a material microstructure image feature extraction algorithm FedTransfer based on data privacy protection is proposed. The core contributions are as follows: 1) the federated learning algorithm is introduced into the polycrystalline microstructure image segmentation task to make full use of different user data to carry out machine learning, break the data island and improve the model generalization ability under the condition of ensuring the privacy and security of user data; 2) A data sharing strategy based on style transfer is proposed. By sharing style information of images that is not urgent for user confidentiality, it can reduce the performance penalty caused by the distribution difference of data among different users. Computer science ; Task (project management) ; Data mining ; Generalization ; Data sharing ; Data security ; Segmentation ; Confidentiality ; Information privacy ; Feature (linguistics) ; Image (mathematics) ; Artificial intelligence ; Computer security ; Encryption ; Engineering Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Cell Image Analysis Techniques