jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhengwei Wang | Yuxiao Wang | Xuan Zhang | Zhaoxu Meng | Zhenghe Yang | Wei Zhao | Xuefeng Cui
Source: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (article, 2022-12-06)
Field: Materials Science — Materials Chemistry

Graph-based Reaction Classification by Contrasting between Precursors and Products The classification of organic reactions is a complex and tedious process, and it often requires certain domain knowledge to understand the classification rules. To reduce such requirements on domain knowledge, BERT-based deep learning methods have been applied to classify reactions based on SMILES strings. However, the same reaction can be represented by different but equivalent SMILES strings, and it can be observed that BERT-based methods are highly sensitive to the choice of SMILES strings. Logically, GNN-based methods are robust to equivalent SMILES strings. Here, we propose a graph isomorphism network (GIN) with contrastive learning, called ContraGIN, to encourage feature fusion learning between precursors and products of the reaction. Indeed, experiments have shown that the new method focuses on learning the atoms near the bond changes before and after the reaction, and consequently achieves a classification accuracy of 99.30% on the USPTO 1k TPL dataset. Additional experiments have also shown that ContraGIN is more robust and much faster for lager reactions (with more atoms) and complicated reactions (with rings). Computer science ; Isomorphism (crystallography) ; Artificial intelligence ; Domain (mathematical analysis) ; Graph ; Feature (linguistics) ; Machine learning ; Theoretical computer science ; Pattern recognition (psychology) ; Chemistry ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Zayna Juracka | Yue An | Amir Sada Khan
Source:  (article, 2022-03-30)
Field: Chemistry — Spectroscopy

Machine learning approach to assess sensitivity of wavelength modulation spectroscopy signals in trace gas sensing Mid-infrared laser-based sensors are commonly used to detect and quantify many chemical species for environmental, industrial, defense, and security applications. Data-driven approaches, including machine learning and information theory, can be applied to photonics-based sensors to quantify drifts and improve precision. These methods are used to classify signals from rotational-vibrational absorption spectra of Nitrous oxide (N2O) in the 4.3 m region of the spectrum. The detection method utilizes the structural complexity of wavelength modulation spectroscopy signals and information encoded in the spectra. We create our basic training models by simulating temperature, pressure, density fluctuations effects, and molecular transition line broadening of a Voigt lineshape profile. Instrument (laser and detector) noise optical fringing effects can be incorporated in the models. The paper shows that signal variations due to Trace gas density fluctuations and molecular collision dynamics can be discriminated from instrument drifts. The proposed methodology can be used to accurately predict, detect, and evaluate short-term and long-term drifts in sensing systems which can be integrated with the conventional Allan variance methods. We demonstrate this methodology by high-precision sensing of rotational-vibrational transitions of Nitrous oxide and carbon monoxide using an interband cascade laser operating at a 4.3 m spectral region. Detector ; Laser ; Spectroscopy ; Sensitivity (control systems) ; Trace gas ; Noise (video) ; SIGNAL (programming language) ; Modulation (music) ; Allan variance ; Photonics ; Computer science ; Optics ; Materials science ; Optoelectronics ; Chemistry ; Physics ; Acoustics ; Electronic engineering ; Artificial intelligence Spectroscopy and Laser Applications ; Analytical Chemistry and Sensors ; Laser Design and Applications
Authors: Ayşenur Gürgen | Başak Atilgan | Sibel Yıldız | Oktay Gönültaş | Sami İmamoğlu
Source: Maderas Ciencia y tecnología (article, 2022-04-01)
Field: Chemistry — Analytical Chemistry

Combining artificial neural network and moth-flame optimization algorithm for optimization of ultrasound-assisted and microwave-assisted extraction parameters: Bark of Pinus brutia In this study, the extraction parameters of Pinus brutia bark were optimized using a hybrid artificial intelligence technique. Firstly, the bark samples were extracted by ultrasound-assisted extraction and microwave-assisted extraction which are defined as 'green' extraction methods at different conditions. The selected extraction parameters for ultrasound-assisted extraction were 0:100; 20:80; 40:60; 80:20 (%) ethanol: water ratios; 40 C, 60 C extraction temperatures and 5 min, 10 min, 15 min, 20 min extraction times and for microwave-assisted extraction were 90, 180, 360, 600, 900 (W) microwave power, 0:100; 20:80; 40:60; 60:40; 80:20 (%) ethanol: water ratios. Then Stiasny number, condensed tannin content and reducing sugar content of all extracts were determined. Next, the prediction models were developed for each studied parameter using Artificial Neural Network. Finally, the extraction parameters were optimized using Moth-Flame Optimization Algorithm. After that optimization process, while the extraction time was the same (5 min), the ethanol: water ratio and extraction temperature values differed for the optimization of all studied assays of ultrasound-assisted extraction. Also, microwave power and ethanol: water ratio variables were found in different values for each assay of microwave-assisted extraction. The results showed that the Artificial Neural Network and Moth-Flame Optimization could be a novel and powerful hybrid approach to optimize the extraction parameters of Pinus brutia barks with saving time, cost, chemical and effort. Extraction (chemistry) ; Bark (sound) ; Artificial neural network ; Materials science ; Chromatography ; Chemistry ; Machine learning ; Computer science ; Acoustics Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis
Authors: Tina Olfatbakhsh | Jeffrey L. Andrews | Abbas S. Milani
Source: Materials Today Communications (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Materials informatics of woven fabric composites: Effect of different dimensionality reduction and learning methods Dimensionality reduction ; Materials science ; Random forest ; Artificial intelligence ; Curse of dimensionality ; Principal component analysis ; Lasso (programming language) ; Computer science Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Industrial Vision Systems and Defect Detection
Authors: Guoyin Wang | Xingmou Liu | You Ma | Yongming Yang | ammad jadoo
Source: Journal of Magnetism and Magnetic Materials (article, 2022-05-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bias magnetic characteristic analysis and condition identification of transformers under DC bias magnetism conditions based on electromagnetic vibration and convolutional neural network Magnetostriction ; Magnetism ; Transformer ; Vibration ; Convolutional neural network ; Computer science ; Wavelet ; Magnetic core ; Acoustics ; Pattern recognition (psychology) ; Artificial intelligence ; Physics ; Magnetic field ; Electromagnetic coil ; Condensed matter physics ; Voltage Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Power Transformer Diagnostics and Insulation
Authors: Heitor Luiz Ornaghi | Roberta Motta Neves | Francisco Maciel Monticeli | Lucas Dall Agnol
Source: Polymer Bulletin (article, 2022-05-12)
Field: Materials Science — Polymers and Plastics

Dynamic mechanical and thermogravimetric properties of synthetized polyurethanes Soft robotics ; Electronics ; Artificial neural network ; Thermogravimetric analysis ; Robotics ; Shape-memory alloy ; Shape-memory polymer ; Computer science ; Dynamic mechanical analysis ; Materials science ; Mechanical engineering ; Biological system ; Artificial intelligence ; Polymer ; Robot ; Composite material ; Engineering ; Chemical engineering ; Electrical engineering Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials
Authors: Jin Young Oh | Dongwon Shin | Woo Seok Choi
Source: Journal of the American Ceramic Society (article, 2022-09-17)
Field: Materials Science — Materials Chemistry

Application of machine learning to sporadic experimental data for understanding epitaxial strain relaxation Abstract Understanding epitaxial strain relaxation is one of the key challenges in functional thin films with strong structure–property relations. Herein, we employ an emerging data analytics approach to quantitatively evaluate the underlying relationships between critical thickness ( h c ) of strain relaxation and various physical and chemical features, despite the sporadic experimental data points available. First, we have collected and refined the reported h c of the perovskite oxide thin film/substrate system to construct a consistent sub‐dataset which captures a common trend among the varying experimental details. Then, we employ correlation analyses and feature engineering to find the most relevant feature set which includes Poisson's ratio and lattice mismatch. With the insight offered by correlation analyses and feature engineering, machine learning (ML) models have been trained to deduce a decent accuracy, which has been further validated experimentally. The demonstrated framework is expected to be efficiently extended to the other classes of thin films in understanding h c . Relaxation (psychology) ; Computer science ; Feature (linguistics) ; Epitaxy ; Thin film ; Artificial intelligence ; Materials science ; Perovskite (structure) ; Correlation ; Analytics ; Feature engineering ; Strain engineering ; Machine learning ; Data mining ; Chemistry ; Mathematics ; Nanotechnology ; Deep learning ; Optoelectronics ; Crystallography ; Psychology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Aruna Prakash | Stefan Sandfeld
Source: Advanced Engineering Materials (article, 2022-09-06)
Field: Materials Science — Materials Chemistry

Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning Atomistic simulations of the molecular dynamics/statics kind are regularly used to study small‐scale plasticity. Contemporary simulations are performed with tens to hundreds of millions of atoms, with snapshots of these configurations written out at regular intervals for further analysis. Continuum scale constitutive models for material behavior can benefit from information on the atomic scale, in particular in terms of the deformation mechanisms, the accommodation of the total strain, and partitioning of stress and strain fields in individual grains. Herein, a methodology is developed using statistical data mining and machine learning algorithms to automate the analysis of continuum field variables in atomistic simulations. Three important field variables are focused on: total strain, elastic strain, and microrotation. The results show that the elastic strain in individual grains exhibits a unimodal lognormal distribution, while the total strain and microrotation fields evidence a multimodal distribution. The peaks in the distribution of total strain are identified with a Gaussian mixture model and methods to circumvent overfitting problems are presented. Subsequently, the identified peaks are evaluated in terms of deformation mechanisms in a grain, which, e.g., helps to quantify the strain for which individual deformation mechanisms are responsible. The overall statistics of the distributions over all grains are an important input for higher scale models, which ultimately also helps to be able to quantitatively discuss the implications for information transfer to phenomenological models. Statistical physics ; Log-normal distribution ; Overfitting ; Deformation (meteorology) ; Materials science ; Scale (ratio) ; Molecular dynamics ; Field (mathematics) ; Plasticity ; Gaussian ; Computer science ; Mechanics ; Artificial intelligence ; Mathematics ; Physics ; Statistics ; Artificial neural network Machine Learning in Materials Science ; Microstructure and mechanical properties ; Force Microscopy Techniques and Applications
Authors: Zhe Zhang | Xi Yang | Xiaobiao Huang | Timur Shaftan | Victor Smaluk | Minghao Song | Weishi Wan | Lijun Wu | Yimei Zhu
Source: Scientific Reports (article, 2022-03-10)
Field: Materials Science — Surfaces, Coatings and Films

Toward fully automated UED operation using two-stage machine learning model To demonstrate the feasibility of automating UED operation and diagnosing the machine performance in real time, a two-stage machine learning (ML) model based on self-consistent start-to-end simulations has been implemented. This model will not only provide the machine parameters with adequate precision, toward the full automation of the UED instrument, but also make real-time electron beam information available as single-shot nondestructive diagnostics. Furthermore, based on a deep understanding of the root connection between the electron beam properties and the features of Bragg-diffraction patterns, we have applied the hidden symmetry as model constraints, successfully improving the accuracy of energy spread prediction by a factor of five and making the beam divergence prediction two times faster. The capability enabled by the global optimization via ML provides us with better opportunities for discoveries using near-parallel, bright, and ultrafast electron beams for single-shot imaging. It also enables directly visualizing the dynamics of defects and nanostructured materials, which is impossible using present electron-beam technologies. Computer science ; Automation ; Artificial intelligence ; Metric (unit) ; Beam (structure) ; Simulation ; Optics ; Mechanical engineering ; Physics ; Engineering Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; X-ray Spectroscopy and Fluorescence Analysis
Authors: B.M. Tripathi | Amit Kumar Sinha | T. Mahata
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Ceramics and Composites

Machine learning guided study of composition-coefficient of thermal expansion relationship in oxide glasses using a sparse dataset Thermal expansion ; Mean absolute percentage error ; Regression ; Coefficient of determination ; Machine learning ; Silicate glass ; Mean squared error ; Silicate ; Linear regression ; Oxide ; Generalization ; Materials science ; Artificial intelligence ; Computer science ; Mathematics ; Artificial neural network ; Statistics ; Chemistry ; Composite material ; Metallurgy ; Organic chemistry Glass properties and applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Mohit Gupta | Nitesh Garg | J. K. Garg | Vansh Gupta | Devraj Gautam
Source: 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) (article, 2022-05-20)
Field: Chemistry — Analytical Chemistry

Designing an Intelligent Parcel Management System using IoT &amp; Machine Learning Parcels delivery is a critical activity in railways. More importantly, each parcel must be thoroughly checked and sorted according to its destination address. We require an efficient and robust IoT system capable of doing all of these tasks with great precision and minimal human interaction. This paper discusses, We created a fully-fledged solution using IoT and machine learning to assist trains in performing this operation efficiently. In this study, we covered the product, which consists mostly of two phases. Scanning is the first step, followed by sorting. During the scanning process, the parcel will be passed through three scanners that will look for explosives, drugs, and any dangerous materials in the parcel and will trash it if any of the tests fail. When the scanning step is over, the parcel moves on to the sorting phase, where we use Qr codes to retrieve the details of the parcels and sort them properly. The simulation of the system is done using the blender software. Our research shows that our procedure significantly improves accuracy as well as the assessment of cutting-edge technology and existing techniques. Computer science ; sort ; Sorting ; Process (computing) ; Enhanced Data Rates for GSM Evolution ; Train ; Software ; Internet of Things ; Edge computing ; Artificial intelligence ; Real-time computing ; Embedded system ; Database ; Operating system Spectroscopy and Chemometric Analyses ; Thermography and Photoacoustic Techniques ; Industrial Vision Systems and Defect Detection
Authors: Weixing Zhang | Chunxia He
Source: JOURNAL OF RENEWABLE MATERIALS (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Modeling and Optimization of the Shear Strength of Cassava Starch-Based Adhesives Using Artificial Intelligence Methods With the exponential growth of the computing power, machine learning techniques have been successfully used in various applications. This paper intended to predict and optimize the shear strength of single lap cassava starchbased adhesive joints for comparison with the application of artificial intelligence (AI) methods. The shear strength was firstly determined by the experiment with three independent experimental variables (starch content, NaOH concentration and reaction temperature). The analysis of range (ANORA) and analysis of variance (ANOVA) were applied to investigate the optimal combination and the significance of each factor for the shear strength based on the orthogonal experiment. The performance of all AI models was characterized by mean absolute error (MAE), root mean square error (RMSE) and regression coefficient (R2 ) compared with the experimental ones. The GA-optimized ANN model was combined with the genetic algorithm (GA) to find the optimal combination of factors for the finalized optimized cassava starch adhesives (CSA-OP). The physicochemical properties of the cassava starch and CSA-OP were determined by the FTIR, TGA and SEM-EDS, respectively. The results showed that the numerical optimized condition of the GA-optimized ANN model was superior to the orthogonal experimental optimized condition. The sensitivity analysis revealed that the relative importance of variables was consistent with the results from ANOVA. FTIR results showed that there were high hydroxyl groups in cassava starch. TGA results showed that the residue of CSA-OP was higher than the cassava starch. SEM-EDS results showed that both the cassava starch and CSA-OP had abundant carbon and oxygen functional groups. Consequently, the obtained results revealed that the use of AI methods was an adequate approach to model and optimize the experimental variables of the shear strength of single lap cassava starch-based adhesive joints. Starch ; Coefficient of determination ; Mean squared error ; Adhesive ; Correlation coefficient ; Materials science ; Mathematics ; Composite material ; Chemistry ; Statistics ; Food science Natural Fiber Reinforced Composites ; Asphalt Pavement Performance Evaluation
Authors: Dario Antweiler | Marc Harmening | Nicole Marheineke | Andre Schmeißer | Raimund Wegener | Pascal Welke
Source: Machine Learning with Applications (article, 2022-03-16)
Field: Materials Science — Polymers and Plastics

Graph-based tensile strength approximation of random nonwoven materials by interpretable regression Nonwoven materials consist of random fiber structures. They are essential to diverse application areas such as clothing, insulation and filtering. A long term goal in industry is the simulation-based optimization of material properties in dependence of the manufacturing parameters. Recent works developed a framework to predict tensile strength properties representing the fiber structure as a stochastic graph. In this paper we present an efficient machine learning approach using a regression model trained on features extracted from the graph, for which we develop a novel graph stretching algorithm. We demonstrate that applying our method to a practically relevant dataset yields similar prediction results as the original ODE approach (R2=0.98), while achieving a significant speedup by up to three orders of magnitude. This opens the field to optimization, as Monte Carlo simulations accounting for the stochastic nature of nonwovens become easily accessible. Our model generalizes well to unseen parameter combinations. Additionally, our approach produces interpretable results by using a simple linear model for the regression task. Computer science ; Monte Carlo method ; Speedup ; Graph ; Algorithm ; Regression ; Ultimate tensile strength ; Linear regression ; Random forest ; Artificial intelligence ; Machine learning ; Mathematics ; Theoretical computer science ; Materials science ; Statistics ; Parallel computing ; Composite material Textile materials and evaluations ; Mechanical Behavior of Composites ; 3D Shape Modeling and Analysis
Authors: XinCi Liu | Chang Zhao
Source: Computational Intelligence and Neuroscience (article, 2022-03-26)
Field: Chemistry — Analytical Chemistry

Research on Image Feature Extraction Algorithm of the Egg and Egg White Protein Thermal Gelation Based on PCA/ICA With the rapid development of the computer field in recent years, a series of major breakthroughs have been made in the field of computer vision. The key technologies in image feature recognition, face recognition, image understanding, pattern recognition, and machine learning have been rapidly applied and developed. The research and application of this field provide efficient and convenient means. However, for traditional physical and chemical experimental research, parameter adjustment is time-consuming and costly. In response to the phenomenon, this article starts with the study of the characteristics of the egg white protein thermal gelation image and explores the extraction of external features presented by the optimal parameters of the coagulation image under the thermal coagulation state of the egg white protein, based on the classic PCA and ICA-image feature extraction algorithm and its improved algorithm, respectively. Experiment and simulation research on several image feature extraction algorithms under different egg white solidification states are carried out, and the efficient recognition method and accuracy of the image under the optimal egg white protein thermal gelation state are discussed. It has important reference significance for the research of optimal image feature extraction in the future high-efficiency experimental research. Artificial intelligence ; Egg white ; Computer science ; Feature extraction ; Field (mathematics) ; Feature (linguistics) ; Pattern recognition (psychology) ; Image (mathematics) ; Algorithm ; Computer vision ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: Aditya Nandy | Shuwen Yue | Changhwan Oh | Chenru Duan | Gianmarco Terrones | Y. Barry Chung | Heather J. Kulik
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

A Database of Ultrastable MOFs Reassembled From Stable Fragments With Machine Learning Models Database ; Computer science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Mohammed Jomaa | Vidyanshu Mishra | Madhusudan Chaudhary | Dundappa Mumbaraddi | Vladimir K. Michaelis | Arthur Mar
Source: Journal of Solid State Chemistry (article, 2022-06-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

True colours shining through: Determining site distributions in coloured Li-containing quaternary Heusler compounds Chromaticity ; Quaternary ; Annealing (glass) ; Crystallography ; Materials science ; Metal ; Spectral line ; Spectroscopy ; Chemistry ; Physics ; Metallurgy ; Optics ; Geology Heusler alloys: electronic and magnetic properties ; Intermetallics and Advanced Alloy Properties ; Quasicrystal Structures and Properties