jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yogesh Khatri | Vaidehi Atpadkar | Aashi Agarwal | Arti Kashyap
Source: IEEE Transactions on Magnetics (article, 2023-06-20)
Field: Materials Science — Materials Chemistry

Magnetic MXene: A Machine-Learning Model With Small Data MXenes, comprising atomically thin layers of transition metal nitrides, carbides, and carbonitrides, exhibit properties that are not found in their corresponding bulk materials. Interestingly, because of the presence of transition metal, MXenes may also provide the candidate materials for observing low-dimensional magnetism. This can be of interest to various applications such as data storage, electromagnetic interference shielding, and spintronic devices. Here, we focus on the magnetic MXenes, which are only a few in number out of known MXenes. We propose machine-learning models to predict the magnetic moments of the MXenes and to classify the MXenes based on their chemical stability. Using these models, we propose four new chemically stable MXene materials having a potentially high magnetic moment. MXenes ; Spintronics ; Materials science ; Magnetism ; Magnetic moment ; Carbide ; Condensed matter physics ; Ferromagnetism ; Nanotechnology ; Physics ; Metallurgy MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Graphene research and applications
Authors: Kumar Ghosh | Sumit Ghosh
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Classical and quantum machine learning applications in spintronics Prediction of physical observables with machine learning for spintronic and molecular devices. Spintronics ; Observable ; Quantum ; Computer science ; Artificial intelligence ; Nanotechnology ; Physics ; Quantum mechanics ; Materials science ; Ferromagnetism Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Surface and Thin Film Phenomena
Authors: Leovergildo Rodrigues Farias | João dos Santos Panero | Jordana Souza Paula RISS | Ana Paula Folmer Corrêa | Marcos José Salgado Vital | Francisco dos Santos Panero
Source: Sensors (article, 2023-08-23)
Field: Chemistry — Analytical Chemistry

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as <i>Escherichia coli</i>, <i>Salmonella enteritidis</i>, <i>Enterococcus faecalis</i> and <i>Listeria monocytogenes</i>. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry. Artificial intelligence ; Principal component analysis ; Enterococcus faecalis ; Near-infrared spectroscopy ; Spectroscopy ; Bacteria ; Identification (biology) ; Computer science ; Pattern recognition (psychology) ; Chemistry ; Escherichia coli ; Biology ; Physics ; Botany ; Biochemistry ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Vishu Gupta | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source:  (article, 2023-06-18)
Field: Materials Science — Materials Chemistry

Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data Artificial intelligence based predictive modeling has become increasingly sought-after in the field of materials science for training property prediction models due to their promising ability to extract and utilize data-driven information from materials data. However, current methods typically use limited hand-engineered fixed-length representations obtained from available composition-based information only, making model inputs a stumbling block when handling small and specialized training datasets. In this paper, we study and propose a method to perform representation learning (RL) that is both applicable and adaptive for generalized use across various domains. We introduce a RL technique that utilizes pre-activation based representations extracted from a model pre-trained using a deep neural network to maximize the accuracy. We perform model training for inorganic material properties using composition-based numerical vectors representing the elemental fractions (EF) of the materials by leveraging source models trained on large datasets to build target models on small datasets and then compare its performance against traditional machine learning (ML), deep neural network and RL-based graph neural network (GNN) models trained from scratch (SC) with EF as input, more informative physical attributes (PA) as input, as well as conventional TL/RL techniques. Using large <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\sim 345K)$</tex> datasets for source model training and small computational <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\sim 28K)$</tex> and experimental <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\sim 2K)$</tex> datasets for target model training and testing, we show that the proposed RL methods help significantly improve the accuracy of the model as compared to the SC models and conventional TL/RL techniques for all data sizes and properties by using only EF as input. We also perform a statistical significance analysis by calculating the p-value to find that the observed improvement in the accuracy of proposed RL model over SC, RL-based GNN, and conventional TL/RL models is indeed significant. Computer science ; Artificial intelligence ; Artificial neural network ; Machine learning ; Representation (politics) ; Analytics ; Graph ; Field (mathematics) ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ziyong Chen | Vivian Wing‐Wah Yam
Source: The Journal of Physical Chemistry Letters (article, 2023-02-14)
Field: Materials Science — Materials Chemistry

Machine-Learned Electronically Excited States with the MolOrbImage Generated from the Molecular Ground State We present a general machine learning framework for probing the electronic state properties using the novel quantum descriptor MolOrbImage. Each pixel of the MolOrbImage records the quantum information generated by the integration of the physical operator with a pair of bra and ket molecular orbital (MO) states. Inspired by the success of deep convolutional neural networks (NNs) in computer vision, we have implemented the convolutional-layer-dominated MO-NN model. Using the orbital energy and electron repulsion integral MolOrbImages, the MO-NN model achieves promising prediction accuracies against the ADC(2)/cc-pVTZ reference for transition energies to both low-lying singlet [mean absolute error (MAE) < 0.16 eV] and triplet (MAE < 0.14 eV) states. An apparent improvement in the prediction of oscillator strength, which has been shown to be challenging previously, has been demonstrated in this study. Moreover, the transferability test indicates the remarkable extrapolation capacity of the MO-NN model to describe the out of data set systems. Extrapolation ; Excited state ; Singlet state ; Ground state ; Convolutional neural network ; Computer science ; Physics ; Test set ; Artificial intelligence ; Atomic physics ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Advanced Chemical Physics Studies
Authors: Zhilong Yuan | Ye Wang | Lingfeng Zhu | Congcong Zhang | Yifei Sun
Source: Fuel (article, 2023-12-18)
Field: Materials Science — Materials Chemistry

Advancing C5+ hydrocarbons fuels production: An interpretable machine learning framework for Co-catalyzed syngas conversion Syngas ; Catalysis ; Production (economics) ; Process engineering ; Environmental science ; Chemistry ; Chemical engineering ; Biochemical engineering ; Computer science ; Pulp and paper industry ; Organic chemistry ; Engineering ; Economics ; Microeconomics Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Jiao Peng | Li Wang | Pu Wang | Yong Pei
Source: ACS Omega (article, 2023-06-12)
Field: Materials Science — Materials Chemistry

Density Functional Theory Computation and Machine Learning Studies of Interaction between Au<sub>3</sub> Clusters and 20 Natural Amino Acid Molecules The optimal adsorption sites and the binding energies of neutral Au<sub>3</sub> clusters with 20 natural amino acids under the gas phase and water solvation were systematically investigated based on density functional theory (DFT) calculations. The calculation results showed that in the gas phase Au<sub>3</sub> tends to bind with N atoms of amino groups in amino acids, except methionine, which tends to bind with Au<sub>3</sub> through S atoms. Under water solvation, Au<sub>3</sub> clusters tended to bind to N atoms of amino groups and N atoms of side chain amino groups in amino acids. However, methionine and cysteine bind more strongly to the gold atom through the S atom. Based on the binding energy data of Au<sub>3</sub> clusters and 20 natural amino acids under water solvation calculated by DFT, a machine learning model (gradient boosted decision tree) was proposed to predict the optimal binding Gibbs free energy (Δ<i>G</i>) of the interaction between Au<sub>3</sub> clusters and amino acids. The main factors affecting the strength of the interaction between Au<sub>3</sub> and amino acids were uncovered by the feature importance analysis. Solvation ; Amino acid ; Chemistry ; Binding energy ; Density functional theory ; Methionine ; Atom (system on chip) ; Molecule ; Natural bond orbital ; Gibbs free energy ; Crystallography ; Computational chemistry ; Thermodynamics ; Organic chemistry ; Atomic physics ; Physics ; Biochemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Kanna Omae | Takahiro Yamazaki | Chiemi Oka | Junpei Sakurai | Seiichi Hata
Source: Microelectronic Engineering (article, 2023-06-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Stress measurement based on magnetic Barkhausen noise for thin films Barkhausen effect ; Stress (linguistics) ; Materials science ; Thin film ; Noise (video) ; Computer science ; Machine learning ; Artificial intelligence ; Nanotechnology ; Magnetic field ; Physics ; Magnetization Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Michael Oberaigner | Alexander Clausen | Dieter Weber | Gerald Kothleitner | Rafal E. Dunin–Borkowski | Daniel Knez
Source: Microscopy and Microanalysis (article, 2023-01-12)
Field: Materials Science — Materials Chemistry

Online Thickness Determination with Position Averaged Convergent Beam Electron Diffraction using Convolutional Neural Networks Abstract Position averaged convergent beam electron diffraction (PACBED) is one of the most convenient and precise thickness determination techniques available in a scanning transmission electron microscope. The thickness is determined by finding the best match of the recorded PACBED pattern with a series of simulated diffraction patterns by visual inspection. The automatization of this process can be enhanced by convolutional neural networks (CNNs), making the method fast and easy to apply. However, the simulation of a synthetic dataset and the training of the CNNs carry a high computational cost. With the aim to simplify this process, we propose to build a server-based database of pretrained CNN models that is accessed by the user via a web service directly from the data acquisition and analysis software. We demonstrate a working prototype comprised of a shared CNN database containing three material systems. By this, the microscope operator can determine the specimen thickness by PACBED within a few seconds in a reproducible way during a microscope session, without any prior knowledge about machine learning or multislice modeling. Furthermore, the service is integrated into other software and workflows through the API. Convolutional neural network ; Computer science ; Workflow ; Software ; Position (finance) ; Process (computing) ; Diffraction ; Artificial intelligence ; Computational science ; Optics ; Database ; Physics ; Operating system Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Tibor Nagy | Gergő Róth | Máté Benedek | Ákos Kuki | István Timári | Miklós Zsuga | Sándor Kéki
Source: Analytical Chemistry (article, 2023-06-22)
Field: Chemistry — Spectroscopy

Enhanced Copolymer Characterization for Polyethers Using Gel Permeation Chromatography Combined with Artificial Neural Networks Gel permeation chromatography (GPC) is a generally applied method for the mass analysis of various polymers and copolymers, but it inherently fails to provide additional important information such as the composition of copolymers. However, we will show that GPC measurements using different solvents can yield not just the correct molecular weight but the composition of the copolymer. Accordingly, artificial neural networks (ANNs) have been developed to process the data of GPC measurements and determine the molecular weight and the chemical composition of the copolymers. The target values of the ANNs were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and nuclear magnetic resonance (NMR) spectroscopy. Our GPC-ANN method is demonstrated by the analysis of various poloxamers, i.e., poly(ethylene oxide) (PEO)-poly(propylene oxide) (PPO) block copolymers. Two ANNs were constructed. The first one (<i>ANN</i>_1) works in a wider mass range (from 900 to 12,500 dalton), while the second one (<i>ANN</i>_2) produces more output values. <i>ANN</i>_2 can thus predict seven characteristic copolymer parameters, namely, two average molecular weights, the average weight fraction of the EO unit, and four average numbers of the repeat units. The correlation between the experimentally obtained outputs and the predicted ones is high (<i>r</i> > 0.98). The accuracy of the ANNs is very convincing, and both ANNs predict the number-average molecular weight (<i>M</i><sub>n</sub>) with an accuracy below 5%. Furthermore, this work is the first step for creating an open database and applications extending the use of the GPC-ANN method for the analysis of copolymers. Copolymer ; Gel permeation chromatography ; Chemistry ; Propylene oxide ; Molar mass distribution ; Ethylene oxide ; Size-exclusion chromatography ; Desorption ; Polymer ; Mass spectrometry ; Mass fraction ; Matrix-assisted laser desorption/ionization ; Analytical Chemistry (journal) ; Chromatography ; Organic chemistry Analytical Chemistry and Chromatography ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jamie Milne | Qian Chen | David Hargreaves | Yinhai Wang | Julie Wilson
Source: PLoS ONE (article, 2023-03-09)
Field: Materials Science — Materials Chemistry

Not getting in too deep: A practical deep learning approach to routine crystallisation image classification Using a relatively small training set of ~16 thousand images from macromolecular crystallisation experiments, we compare classification results obtained with four of the most widely-used convolutional deep-learning network architectures that can be implemented without the need for extensive computational resources. We show that the classifiers have different strengths that can be combined to provide an ensemble classifier achieving a classification accuracy comparable to that obtained by a large consortium initiative. We use eight classes to effectively rank the experimental outcomes, thereby providing detailed information that can be used with routine crystallography experiments to automatically identify crystal formation for drug discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions. Classifier (UML) ; Computer science ; Deep learning ; Artificial intelligence ; Pattern recognition (psychology) ; Crystallization ; Convolutional neural network ; Contextual image classification ; Machine learning ; Data mining ; Image (mathematics) ; Chemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Peter Minch | Romakanta Bhattarai | Trevor David Rhone
Source: Solid State Communications (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Data-driven study of magnetic anisotropy in transition metal dichalcogenide monolayers Transition metal ; Chalcogen ; Magnetocrystalline anisotropy ; Monolayer ; Magnetic anisotropy ; Condensed matter physics ; Anisotropy ; Magnetization ; Materials science ; Chemistry ; Chemical physics ; Nanotechnology ; Crystallography ; Physics ; Magnetic field 2D Materials and Applications ; Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties
Authors: Anita Rácz | Dóra Tátraaljai | Szilvia Klébert
Source: Materials Today Chemistry (article, 2023-08-24)
Field: Chemistry — Analytical Chemistry

Determination of melt flow index and polymer additives in polyethylene based on IR spectra and multivariate modeling Protection against degradation is essential for preserving the favorable physical and chemical properties of polymers. There are several types of stabilizers employed as antioxidants, but the latest studies showed that artificial stabilizers spark environmental and healthcare concern. Here, we have used multivariate modeling based on ATR and transmission infrared spectra to provide information about the degradation and the applied additive stabilizer for polyethylene samples. The classification of samples based on the used additives was successful in single and combined additive groups as well, with accuracies over 0.9 in each case. ATR-IR combined with principal component analysis (PCA) and correlation and regression tree (CART) methods yielded the best classification model. Melt flow index values (MFI), which can correlate with the degradation and stability of the polymers, were predicted with partial least squares (PLS) regression and artificial neural network (ANN) models. The best combination for regression modeling was the transmission IR spectra with the ANN algorithm. The ANN model could predict the MFI values remarkably well, with an R2 value of 0.95, even for the test validation. Infrared spectroscopy coupled with chemometrics is suitable to be integrated into the quality control of polymers. Melt flow index ; Partial least squares regression ; Chemometrics ; Multivariate statistics ; Principal component analysis ; Polymer ; Biological system ; Materials science ; Polyethylene ; Infrared spectroscopy ; Chemistry ; Artificial intelligence ; Computer science ; Machine learning ; Chromatography ; Organic chemistry ; Composite material Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Yiming Zhao | Yongjia Zhao | Qihan Gong | Zhuo Wang
Source: ACS Applied Materials & Interfaces (article, 2023-10-13)
Field: Chemistry — Inorganic Chemistry

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal–Organic Frameworks Metal-organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO<sub>2</sub>/CH<sub>4</sub> separations and CH<sub>4</sub> uptake, both of which showed good predictive performance. Materials science ; Binary number ; Component (thermodynamics) ; Adsorption ; Metal-organic framework ; Transformer ; Graph ; Biological system ; Theoretical computer science ; Computer science ; Mathematics ; Thermodynamics ; Voltage ; Organic chemistry ; Arithmetic ; Electrical engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Enhanced Oil Recovery Techniques
Authors: Bora Timurkutluk | Yelda Ciflik | Güray Sonugür | Tolga Altan | Ömer Genç
Source: Fuel (article, 2023-09-04)
Field: Materials Science — Materials Chemistry

Quantitative estimation of triple phase boundaries in solid oxide fuel cell electrodes via artificial neural network Triple phase boundary ; Materials science ; Solid oxide fuel cell ; Electrode ; Artificial neural network ; Biological system ; Backpropagation ; Phase (matter) ; Volume fraction ; Electrolyte ; Microstructure ; Particle (ecology) ; Particle size ; Range (aeronautics) ; Computer science ; Artificial intelligence ; Chemical engineering ; Composite material ; Chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion