jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shenjun Zha | Dmitry Sharapa | Sihang Liu | Zhi‐Jian Zhao | Felix Studt
Source: The Journal of Physical Chemistry A (article, 2022-12-13)
Field: Materials Science — Materials Chemistry

Modeling CoCu Nanoparticles Using Neural Network-Accelerated Monte Carlo Simulations The correct description of catalytic reactions happening on bimetallic particles is not feasible without proper accounting of the segregation process. In this study, we tried to shed light on the structure of large CoCu particles, for which quite controversial results were published before. However, density functional theory (DFT) is challenging to be directly used for the systematic study of nanometer-sized particles. Therefore, we constructed a neural network-based potential and further applied it to the Monte Carlo simulations for the description of the segregation phenomenon. The resulting approach shows high efficiency and can be used in systems with thousands of atoms. The accuracy and transferability of the model to other sizes and compositions make this methodology useful for solving segregation problems. Monte Carlo method ; Transferability ; Artificial neural network ; Statistical physics ; Kinetic Monte Carlo ; Process (computing) ; Bimetallic strip ; Computer science ; Monte Carlo molecular modeling ; Materials science ; Nanoparticle ; Nanotechnology ; Physics ; Artificial intelligence ; Markov chain Monte Carlo ; Machine learning ; Mathematics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Catalytic Processes in Materials Science
Authors: Sterling G. Baird | Marianne Liu | Taylor D. Sparks
Source: arXiv (Cornell University) (preprint, 2022-03-19)
Field: Materials Science — Materials Chemistry

High-dimensional Bayesian Optimization of Hyperparameters for an Attention-based Network to Predict Materials Property: a Case Study on CrabNet using Ax and SAASBO Expensive-to-train deep learning models can benefit from an optimization of the hyperparameters that determine the model architecture. We optimize 23 hyperparameters of a materials informatics model, Compositionally-Restricted Attention-Based Network (CrabNet), over 100 adaptive design iterations using two models within the Adaptive Experimentation (Ax) Platform. This includes a recently developed Bayesian optimization (BO) algorithm, sparse axis-aligned subspaces Bayesian optimization (SAASBO), which has shown exciting performance on high-dimensional optimization tasks. Using SAASBO to optimize CrabNet hyperparameters, we demonstrate a new state-of-the-art on the experimental band gap regression task within the materials informatics benchmarking platform, Matbench (~4.5% decrease in mean absolute error (MAE) relative to incumbent). Characteristics of the adaptive design scheme as well as feature importances are described for each of the Ax models. SAASBO has great potential to both improve existing surrogate models, as shown in this work, and in future work, to efficiently discover new, high-performing materials in high-dimensional materials science search spaces. Hyperparameter ; Bayesian optimization ; Computer science ; Hyperparameter optimization ; Artificial intelligence ; Machine learning ; Benchmarking ; Bayesian network ; Feature (linguistics) ; Bayesian probability ; Data mining Machine Learning in Materials Science ; Advanced Neural Network Applications ; Advanced Multi-Objective Optimization Algorithms
Authors: Stephan Thaler | Julija Zavadlav
Source:  (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Uncertainty Quantification for Molecular Models via Stochastic Gradient MCMC The quality of molecular dynamics (MD) simulations critically depends on the employed potential energy model. Accurate uncertainty quantification (UQ) of these models could increase trust in MD simulation predictions and promote progress in the field of active learning of neural network (NN) potentials. Bayesian methods promise reliable uncertainty estimates, but the high computational cost of training via classical Markov Chain Monte Carlo (MCMC) schemes has prevented their application to deep NN potentials. In this work, we propose stochastic gradient MCMC methods as a computationally efficient option for Bayesian UQ of MD potentials. The stochastic gradient Langevin dynamics method yields promising results for a tabulated coarse-grained water model and could thus be a feasible approach for NN potentials. Additionally, we illustrate the inherent limit of Bayesian UQ imposed by the functional form of the employed model. Markov chain Monte Carlo ; Computer science ; Uncertainty quantification ; Biological system ; Statistical physics ; Bayesian probability ; Artificial intelligence ; Machine learning ; Physics ; Biology Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes
Authors: De Souza Vieira GL | Moutinho Da Ponte MJ | Pereira Moutinho VH | Ricardo Jardim‐Gonçalves | Pantoja Lima C | Marco Valério de Albuquerque Vinagre
Source: iForest - Biogeosciences and Forestry (article, 2022-07-14)
Field: Chemistry — Organic Chemistry

Identification of wood from the Amazon by characteristics of Haralick and Neural Network: image segmentation and polishing of the surface The identification of Amazonian timber species is a complex problem due to their great diversity and the lack of leaf material in the post-harvest inspection often hampers a correct recognition of the wood species. In this context, we developed a pattern recognition system of wood images to identify commonly traded species, with the aim of increasing the accuracy and efficiency of current identification methods. We used ten different species with three polishing treatments and twenty images for each wood species. As for the image recognition system, the textural segmentation associated with Haralick characteristics and classified by Artificial Neural Networks was used. We verified that the improvement of sandpaper granulometry increased the accuracy of species recognition. The developed model based on linear regression achieved a recognition rate of 94% in the training phase, and a post-training recognition rate of 65% for wood treated with 120-grit sandpaper mesh. We concluded that the wood pattern recognition model presented has the potential to correctly identify the wood species studied. Sandpaper ; Artificial intelligence ; Pattern recognition (psychology) ; Identification (biology) ; Context (archaeology) ; Segmentation ; Artificial neural network ; Computer science ; Polishing ; Convolutional neural network ; Computer vision ; Materials science ; Botany ; Composite material ; Biology Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Advanced Image and Video Retrieval Techniques
Authors: Kewei Pan | Qing Liu | Ying Lü | Wei Wei | Zhao Ding | Shixing Wang | Yang Zhou
Source: Ceramics International (article, 2022-09-09)
Field: Materials Science — Polymers and Plastics

High-throughput data mined prediction of design and preparation of flexible carbon-based conductive materials in energy storage Materials science ; Electrical conductor ; Boosting (machine learning) ; Conductivity ; Energy storage ; Carbon fibers ; Fabrication ; Computer science ; Composite number ; Filler (materials) ; Process engineering ; Composite material ; Artificial intelligence ; Power (physics) Conducting polymers and applications ; Electromagnetic wave absorption materials ; Advanced Sensor and Energy Harvesting Materials
Authors: R. Ganesh Babu | C. Chellaswamy | T. Geetha | Rohan Michael Ramesh
Source: Journal of Modern Optics (article, 2022-03-04)
Field: Chemistry — Spectroscopy

Fibre optic sensor based multi-gas detection using optimized convolutional neural network This study relates to an Optimized Convolutional Neural Network-based Multi-gas Optical Sensor (OCNN-MOS) developed using photonic crystals. A square-shaped dielectric rod with cubic lattice index was used for the detection of different gases, namely, N2, CH4 and CO. Three nanocavities were present in the proposed OCNN-MOS, with one nanocavity located at the centre (refractive index 2.6) and the other two were the sub-nanocavities located along the path of the output and input respectively. A grey wolf optimization technique was used for optimizing its hyperparameters and for the improvement of its performance. The proposed OCNN-MOS was connected with a clean air generator for diluting the flow of hydrocarbon, a flow controller, and three standard sample gas cylinders for the conduction of the experiment. A concentrated gas mixture of 60–200 ppm was used in the experiment. The proposed optimization achieved an improvement of 7.06% in the performance of CNN. Materials science ; Hyperparameter ; Convolutional neural network ; Computer science ; Artificial neural network ; Dielectric ; Optoelectronics ; Algorithm ; Artificial intelligence Spectroscopy and Laser Applications ; Advanced Fiber Optic Sensors ; Photonic Crystal and Fiber Optics
Authors: Alberta Longhini | Marco Moletta | Alfredo Reichlin | Michael C. Welle | Alexander Kravberg | Yufei Wang | David Held | Zackory Erickson | Danica Kragić
Source: arXiv (Cornell University) (preprint, 2022-09-12)
Field: Materials Science — Polymers and Plastics

Elastic Context: Encoding Elasticity for Data-driven Models of Textiles Physical interaction with textiles, such as assistive dressing, relies on advanced dextreous capabilities. The underlying complexity in textile behavior when being pulled and stretched, is due to both the yarn material properties and the textile construction technique. Today, there are no commonly adopted and annotated datasets on which the various interaction or property identification methods are assessed. One important property that affects the interaction is material elasticity that results from both the yarn material and construction technique: these two are intertwined and, if not known a-priori, almost impossible to identify through sensing commonly available on robotic platforms. We introduce Elastic Context (EC), a concept that integrates various properties that affect elastic behavior, to enable a more effective physical interaction with textiles. The definition of EC relies on stress/strain curves commonly used in textile engineering, which we reformulated for robotic applications. We employ EC using Graph Neural Network (GNN) to learn generalized elastic behaviors of textiles. Furthermore, we explore the effect the dimension of the EC has on accurate force modeling of non-linear real-world elastic behaviors, highlighting the challenges of current robotic setups to sense textile properties. Yarn ; Elasticity (physics) ; Textile ; Computer science ; Context (archaeology) ; Property (philosophy) ; Linear elasticity ; Graph ; Theoretical computer science ; Mechanical engineering ; Structural engineering ; Engineering ; Materials science ; Composite material ; Finite element method Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics
Authors: J. Zhao | Jesper Byggmästar | Huan He | K. Nordlund | Flyura Djurabekova | Mengyuan Hua
Source: Research Square (preprint, 2022-12-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Complex Ga2O3 Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials Interatomic potential ; Computer science ; Artificial intelligence ; Materials science ; Machine learning ; Statistical physics ; Chemistry ; Physics ; Computational chemistry ; Molecular dynamics Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Daniel García‐Lojo | David Méndez‐Merino | Ignacio Pérez‐Juste | Ángel Acuña | Luis García‐Río | Alfonso Rodríguez‐Patón | Isabel Pastoriza‐Santos | Jorge Pérez‐Juste
Source: Advanced Materials Technologies (article, 2022-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bolaform Surfactant‐Induced Au Nanoparticle Assemblies for Reliable Solution‐Based Surface‐Enhanced Raman Scattering Detection Abstract Solution‐based surface‐enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate‐stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting‐out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B 20 , to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B 20 , are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B 20 by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on‐site detection and quantification of beta‐blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state‐of‐the‐art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity. Raman scattering ; Materials science ; Nanotechnology ; Nanoparticle ; Dynamic light scattering ; Plasmonic nanoparticles ; Raman spectroscopy ; Bridging (networking) ; Reusability ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Biosensors and Analytical Detection
Authors: Yang Liu | Haiyou Huang | Jie Yuan | Yan Zhang | Hongyuan Feng | Ning Chen | Yang Li | Jiao Teng | Kui Jin | Dezhen Xue | Yanjing Su
Source: Patterns (article, 2022-10-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Upper limit of the transition temperature of superconducting materials Why are the transition temperatures (<i>T</i> <sub>c</sub>) of superconducting materials so different? The answer to this question is not only of great significance in revealing the mechanism of high-<i>T</i> <sub>c</sub> superconductivity but also can be used as a guide for the design of new superconductors. However, so far, it is still challenging to identify the governing factors affecting the <i>T</i> <sub>c</sub>. In this work, with the aid of machine learning and first-principles calculations, we found a close relevance between the upper limit of the <i>T</i> <sub>c</sub> and the energy-level distribution of valence electrons. It implies that some additional inter-orbital electron-electron interaction should be considered in the interpretation of high-<i>T</i> <sub>c</sub> superconductivity. Superconductivity ; Condensed matter physics ; Limit (mathematics) ; Electron ; Physics ; Valence (chemistry) ; Work (physics) ; Transition temperature ; Quantum mechanics ; Mathematics Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Rare-earth and actinide compounds
Authors: Tuan Hoang Nguyen | Khang M. Le | Lam Nguyen | Thanh N. Truong
Source: ACS Omega (article, 2022-12-17)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Quantitative Structure–Property Relationships for the Electronic Properties of Cyano Polycyclic Aromatic Hydrocarbons In this study, quantitative structure-property relationships (QSPR) based on a machine learning (ML) methodology and the truncated degree of π-orbital overlap (DPO) to predict the electronic properties, namely, the bandgaps, electron affinities, and ionization potentials of the cyano polycyclic aromatic hydrocarbon (CN-PAH) chemical class were developed. The level of theory B3LYP/6-31+G(d) of density functional theory (DFT) was used to calculate a total of 926 data points for the development of the QSPR model. To include the substituents effects, a new descriptor was added to the DPO model. Consequently, the new ML-DPO model yields excellent linear correlations to predict the desired electronic properties with high accuracy to within 0.2 eV for all multi-CN-substituted PAHs and 0.1 eV for the mono-CN-substituted PAH subclass. Quantitative structure–activity relationship ; Density functional theory ; Chemistry ; Ionization energy ; Electron affinity (data page) ; Computational chemistry ; Electronic structure ; Aromaticity ; Molecular descriptor ; Hydrocarbon ; Polycyclic aromatic hydrocarbon ; Ionization ; Molecule ; Organic chemistry ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Mir Saeid Hesarian | Jafar Tavoosi
Source: Complexity (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Evaluating the Smoothness of the Washed Fabric after Laundry with the Washing Machine Based on a New Type‐2 Fuzzy Neural Network Clothes laundering are necessary during their cycle life, and the mechanical forces exposed to fabrics during laundering were caused to wrinkle. Therefore, in this paper, the wrinkle of the cotton fabric after home laundering was evaluated based on their characteristic. The washing process was done without any softener as toxic material. For this purpose, experimental and theoretical evaluations were conducted. In experiments, the cotton fabrics in various characteristics were washed by washing machine without any softener in special adjustments. The wrinkle of the samples was rated based on the light line method. Theoretical evaluations were studied by the development of a new type‐2 fuzzy neural network. In this model the thickness, weight, warp and weft density per inch, warp and weft Tex as linear density, and cover factor of the fabric in warp and weft directions were considered as input parameters and the wrinkle grade of the washed fabric was output. Analysis of the modeling and experimental results illustrates that when eight mentioned parameters were selected as inputs, the mean square error, root mean square error, and mean absolute error of the model were decreased in comparison of the models with two, four, and six inputs. According to this fact, all of the input parameters have an effect on the wrinkle of the cotton fabric after the washing process. Wrinkle ; Laundry ; Smoothness ; Mean squared error ; Artificial neural network ; Computer science ; Mathematics ; Materials science ; Pulp and paper industry ; Composite material ; Biological system ; Structural engineering ; Engineering ; Artificial intelligence ; Statistics ; Waste management Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Color perception and design
Authors: William Basener
Source:  (article, 2022-09-13)
Field: Chemistry — Analytical Chemistry

Classifying Crop Types Using Gaussian Bayesian Models and Neural Networks on Ghisaconus USGS Data from NASA Hyperspectral Satellite Imagery Hyperspectral Imagining is a type of digital imaging in which each pixel contains typically hundreds of wavelengths of light providing spectroscopic information about the materials present in the pixel [1]. In this paper we provide classification methods for determining crop type in the USGS GHISACONUS data, which contains around 7,000 pixel spectra from the five major U.S. agricultural crops (winter wheat, rice, corn, soybeans, and cotton) collected by the NASA Hyperion satellite, and includes the spectrum, geolocation, crop type, and stage of growth for each pixel. We apply standard LDA and QDA as well as Bayesian custom versions that compute the joint probability of crop type and stage, and then the marginal probability for crop type, outperforming the non-Bayesian methods. We also test a single layer neural network with dropout on the data, which performs comparable to LDA and QDA but not as well as the Bayesian methods. Hyperspectral imaging ; Pixel ; Computer science ; Remote sensing ; Artificial intelligence ; Bayesian probability ; Artificial neural network ; Satellite ; Dropout (neural networks) ; Pattern recognition (psychology) ; Machine learning ; Geography ; Engineering Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Advanced Statistical Methods and Models
Authors: Xiangyu Li | Fugang Li | Minzu Liang | Kefan Zhang
Source: Journal of Physics Conference Series (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Design Methods for the Lethality of Fragmentation Warhead based on a Multi-Objective Optimization Algorithm Abstract Fragmentation warhead is the main weapon used to damage personnel and light armored objectives, with the lethal parameters primarily consisting of the number and velocity of fragments. Increasing the lethality of fragmentation warhead is currently the most important development direction for fragmentation warhead. The formation process of natural fragment is complicated and there are several factors that affect the properties of fragment, so there are still many techniques of developing the lethality of fragmentation warhead. In this paper, our object is to focus on the optimization design for the lethality of fragmentation warhead, based on the optimal Latin hypercube test design method and the radial basis function neural network surrogate modelling, taking the number of effective fragments per unit mass and the average kinetic energy of the effective fragments as the optimization objectives, and the explosive charge, the fragment material, the overall structure and initiation method are considered, respectively. The Pareto optimal solution sets under different design variables are obtained, and the optimal matching of multiple design parameters is realized, establishing the integration design method for “explosive - material-structure” of the fragmentation warhead. The research results can provide technical support for the structural optimization design of the fragmentation warhead. Warhead ; Fragmentation (computing) ; Lethality ; Latin hypercube sampling ; Explosive material ; Computer science ; Pareto principle ; Mathematical optimization ; Engineering ; Mathematics ; Chemistry ; Aerospace engineering ; Statistics ; Monte Carlo method ; Biology High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Advanced Measurement and Detection Methods