jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Waqas Ahmad | Guoxin Wang | Yan Yan
Source: Applied Sciences (article, 2022-03-28)
Field: Materials Science — Materials Chemistry

ANN-Based Inverse Goal-Oriented Design Method for Targeted Final Properties of Materials Designing materials for targeted materials properties is the key to tackle the demands for personalized consumer products. The deficiency in the existing linear and nonlinear correlation methods attributed to simplifying assumptions and idealizations, nondeterministic simulations, and limited experimental data due to heavy computational time and cost, necessitates a design method that provides sufficient confidence to designers in decision making. To address this requirement, we propose, in this paper, an inverse goal-oriented materials design method supported by the design space exploration framework (DSEF). Keeping in view the accuracy and precision in the prediction confidence of machine learning-based methods, we developed an Artificial Neural Network based prediction model that supports DSEF. The proposed method for materials design can help designers to (1) explore PSPP spaces starting from end property requirements, (2) adjust the errors being propagated in the PSPP chain as well as in the predictions made by the model, and (3) timely adjust model parameters of the prediction model for accurate predictions. The efficacy of the method is illustrated for the hot stamping process to produce structural components from ultrahigh-strength steels (UHSS). The proposed method and prediction model are generic and applicable to any sequential manufacturing process to realize an end product. Computer science ; Nondeterministic algorithm ; Process (computing) ; Artificial neural network ; Product design ; Inverse ; Key (lock) ; Nonlinear system ; Industrial engineering ; Machine learning ; Product (mathematics) ; Engineering ; Algorithm ; Mathematics Machine Learning in Materials Science ; Material Selection and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Timo Gahlmann | Philippe Tassin
Source: arXiv (Cornell University) (preprint, 2022-01-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep neural networks for the prediction of the optical properties and the free-form inverse design of metamaterials Many phenomena in physics, including light, water waves, and sound, are described by wave equations. Given their coefficients, wave equations can be solved to high accuracy, but the presence of the wavelength scale often leads to large computer simulations for anything beyond the simplest geometries. The inverse problem, determining the coefficients from a field on a boundary, is even more demanding, since traditional optimization requires a large number of forward problems be solved sequentially. Here we show that the free-form inverse problem of wave equations can be solved with machine learning. First we show that deep neural networks can be used to predict the optical properties of nanostructured materials such as metasurfaces. Then we demonstrate the free-form inverse design of such nanostructures and show that constraints imposed by experimental feasibility can be taken into account. Our neural networks promise automated design in several technologies based on the wave equation. Inverse problem ; Artificial neural network ; Inverse ; Metamaterial ; Wave equation ; Computer science ; Boundary value problem ; Wavelength ; Field (mathematics) ; Applied mathematics ; Physics ; Mathematics ; Mathematical analysis ; Artificial intelligence ; Optics ; Geometry Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Acoustic Wave Phenomena Research
Authors: Claudius Zelenka | Marius Kamp | Kolja Strohm | Akram Kadoura | Jacob Johny | Reinhard Koch | Lorenz Kienle
Source: arXiv (Cornell University) (preprint, 2022-07-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Automated Classification of Nanoparticles with Various Ultrastructures and Sizes Accurately measuring the size, morphology, and structure of nanoparticles is very important, because they are strongly dependent on their properties for many applications. In this paper, we present a deep-learning based method for nanoparticle measurement and classification trained from a small data set of scanning transmission electron microscopy images. Our approach is comprised of two stages: localization, i.e., detection of nanoparticles, and classification, i.e., categorization of their ultrastructure. For each stage, we optimize the segmentation and classification by analysis of the different state-of-the-art neural networks. We show how the generation of synthetic images, either using image processing or using various image generation neural networks, can be used to improve the results in both stages. Finally, the application of the algorithm to bimetallic nanoparticles demonstrates the automated data collection of size distributions including classification of complex ultrastructures. The developed method can be easily transferred to other material systems and nanoparticle structures. Artificial intelligence ; Nanoparticle ; Artificial neural network ; Pattern recognition (psychology) ; Segmentation ; Bimetallic strip ; Computer science ; Categorization ; Contextual image classification ; Image segmentation ; Set (abstract data type) ; Data set ; Materials science ; Image (mathematics) ; Biological system ; Nanotechnology Gold and Silver Nanoparticles Synthesis and Applications
Authors: Soroosh Hakimian | Shamim Pourrahimi | Lucas A. Hof
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Application of Machine Learning Algorithms to Classify and Predict Corrosion Behavior of Stainless Steels in Lactic Acid Corrosion of metals is a critical issue, which causes a considerable amount of loss in industries. There are numerous factors that can influence corrosion, including chemical composition and multiple environmental conditions. Hence, it is difficult to determine the relation between individual environmental factors and corrosion processes based on physics-based corrosion laws or to predict the corrosion life of materials. Predicting corrosion behavior of materials in any type of environment is important since testing materials in each different environment is time-consuming and expensive. Hence, analyzing corrosion data and, subsequently, predicting corrosion behavior needs advanced data mining methods. Recently, machine learning (ML) methods have been extensively used in materials research thanks to their powerful data mining capabilities. By learning from sample data and experience, it automates the searching for knowledge without reliance on predetermined equations. In corrosion research, machine learning algorithms such as random forest, support vector regression, and artificial neural networks have been used to study the corrosion behavior. Stainless steels are widely used in corrosive environments. This research aims to develop classification methods for predicting stainless steel corrosion behavior in different concentrations of lactic acid and different temperatures. The Handbook of corrosion data was used to gather data on stainless steel corrosion in lactic acid. Outlier, repeated, and missing data were treated during a pre-processing step (Figure 1). Based on the ML results, we can properly predict the corrosion behavior of various grades of stainless steels, in different lactic acid concentration and test temperatures. The best training and testing accuracies are 98.73% and 90.00%, respectively, which are obtained by fitting the decision tree classifier. It is also concluded that the percentage of four essential elements (C, Cr, Ni, Mo) in stainless steel alloys, alongside acid concentration and temperature, can be used as the input data to predict the corrosion behavior. Receiver operating characteristic (ROC) curves indicate that stainless steels with poor corrosion behavior are correctly classified by support vector machine (SVM) multiclassification modeling. Therefore, the SVM algorithm is reliable for detecting stainless steels with poor corrosion behavior, which are highly risky for critical applications if chosen incorrectly. Figure 1 Corrosion ; Materials science ; Artificial neural network ; Random forest ; Metallurgy ; Support vector machine ; Computer science ; Artificial intelligence ; Machine learning ; Process engineering ; Engineering Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: José A. Villajos | Martin Bienert | Nikita Y. Gugin | Franziska Emmerling | Michael Maiwald
Source: ChemRxiv (preprint, 2022-08-30)
Field: Chemistry — Inorganic Chemistry

A database to compare possible MOFs for volumetric hydrogen storage, taking into account the cost of their building blocks Physical adsorption at cryogenic temperature can increase the density of the stored hydrogen at a lower pressure than conventional compressed gas systems. This mechanism is also reversible and involves faster kinetics than chemical storage. Materials with certain structural and porous properties are necessary for volumetrically efficient hydrogen storage, including large specific surface areas, pore volumes, and appropriated bulk densities. Metal-organic frameworks (MOF) materials are remarkable candidates as adsorbents due to their porous properties and high crystallinity. Large databases like the MOF subset from the CSD or the CoRE-MOF can be used to find the best materials for this application, providing crystallographic information, composition, and porous properties. Herein, we created a database which includes crystallographic and porous properties, metallic and organic composition, and the minimum available cost for their linkers and corresponding suppliers for those for which it was publicly available. The database is also helpful for selecting structures with potential for industrial production and starting material for computational tools like machine learning or artificial intelligence approaches that relate the composition of MOFs with their performance in different applications. A user interface allows for creating customized selections of suitable MOF structures, looking for their porous and crystalline properties, gravimetric and volumetric total uptakes, and metallic and organic composition, as well as properties for the organic linkers like name, molecular mass, price, or presence of specific functional groups. This information was used to select potential structures from up to two metals and two linkers for the volumetric cryostorage of hydrogen. Hydrogen storage ; Gravimetric analysis ; Metal-organic framework ; Porosity ; Materials science ; Adsorption ; Crystallinity ; Hydrogen ; Porous medium ; Specific surface area ; Database ; Computer science ; Chemical engineering ; Chemistry ; Composite material ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Boron and Carbon Nanomaterials Research ; Catalytic Processes in Materials Science
Authors: Sunao Shimizu | Tomoya Kishi | Goki Ogane | K. Tokiwa | Shimpei Ono
Source: Scientific Reports (article, 2022-05-03)
Field: Materials Science — Materials Chemistry

Electrical mapping of thermoelectric power factor in WO3 thin film With growing environmental awareness and considerable research investment in energy saving, the concept of energy harvesting has become a central topic in the field of materials science. The thermoelectric energy conversion, which is a classic physical phenomenon, has emerged as an indispensable thermal management technology. In addition to conventional experimental investigations of thermoelectric materials, seeking promising materials or structures using computer-based approaches such as machine learning has been considered to accelerate research in recent years. However, the tremendous experimental efforts required to evaluate materials may hinder us from reaping the benefits of the fast-developing computer technology. In this study, an electrical mapping of the thermoelectric power factor is performed in a wide temperature-carrier density regime. An ionic gating technique is applied to an oxide semiconductor WO<sub>3</sub>, systematically controlling the carrier density to induce a transition from an insulating to a metallic state. Upon electrically scanning the thermoelectric properties, it is demonstrated that the thermoelectric performance of WO<sub>3</sub> is optimized at a highly degenerate metallic state. This approach is convenient and applicable to a variety of materials, thus prompting the development of novel functional materials with desirable thermoelectric properties. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Engineering physics ; Seebeck coefficient ; Thermoelectric generator ; Energy harvesting ; Nanotechnology ; Computer science ; Characterization (materials science) ; Energy transformation ; Electric potential energy ; Optoelectronics ; Energy (signal processing) ; Engineering Advanced Thermoelectric Materials and Devices ; Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors
Authors: Mostafa A. Ebied | Mohamed Mostafa A. Azim | Ahmed Emad-Eldeen
Source: 2022 4th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE) (article, 2022-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial Intelligence-based Cyclic Voltammetry Behavior Model for Supercapacitance of Zinc oxide (ZnO) Nanocomposite This paper presents the fabrication of binder-free nanostructured Zinc oxide (ZnO) electrode for supercapacitor by the chemical bath deposition method. Further, cyclic voltammetry was modeled using an artificial neural network. The prepared material resulted in increasing the capacitance value; acting as a binder-free electrode. Results of the electrochemical study revealed that the medium current is 1.335mA. Using artificial neural network modeling primarily based totally on the idea of multilayer Perception. The cyclic behavior of the organized fabric is examined. From the evaluation of the artificial neural network, the outcomes have been obtained to be much like the experimental values with an error rate of 0.1. Cyclic voltammetry ; Supercapacitor ; Materials science ; Zinc ; Capacitance ; Nanocomposite ; Electrode ; Artificial neural network ; Electrochemistry ; Oxide ; Chemical engineering ; Fabrication ; Nanotechnology ; Computer science ; Metallurgy ; Artificial intelligence ; Chemistry ; Engineering Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: J. Pety | Maryvonne Gérin | Émeric Bron | P. Gratier | Jan H. Orkisz | Pierre Palud | Antoine Roueff | Lucas Einig | Miriam G. Santa-Maria | Victor de Souza Magalhães | Sébastien Bardeau | Jocelyn Chanussot | Pierre Chainais | J. R. Goicoechea | Viviana V. Guzmán | Annie Hughes | J. Kainulainen | David Languignon | François Levrier | D. C. Lis | H. S. Liszt | J. Le Bourlot | Franck Le Petit | Karin I. Öberg | N. Peretto | E. Roueff | A. Sievers | Pierre-Antoine Thouvenin | Pascal Tremblin
Source: EPJ Web of Conferences (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Revealing which Combinations of Molecular Lines are Sensitive to the Gas Physical Parameters of Molecular Clouds Atoms and molecules have long been thought to be versatile tracers of the cold neutral gas in the universe, from high-redshift galaxies to star forming regions and proto-planetary disks, because their internal degrees of freedom bear the signature of the physical conditions where these species reside. However, the promise that molecular emission has a strong diagnostic power of the underlying physical and chemical state is still hampered by the difficulty to combine sophisticated chemical codes with gas dynamics. It is therefore important 1) to acquire self-consistent data sets that can be used as templates for this theoretical work, and 2) to reveal the diagnostic capabilities of molecular lines accurately. The advent of sensitive wideband spectrometers in the (sub)- millimeter domain (e.g., IRAM-30m/EMIR, NOEMA, …) during the 2010s has allowed us to image a significant fraction of a Giant Molecular Cloud with enough sensitivity to detect tens of molecular lines in the 70 – 116 GHz frequency range. Machine learning techniques applied to these data start to deliver the next generation of molecular line diagnostics of mass, density, temperature, and radiation field. Molecular cloud ; Physics ; Astrophysics ; Galaxy ; Redshift ; Stars Spectroscopy and Laser Applications ; Molecular Spectroscopy and Structure ; Atmospheric Ozone and Climate
Authors: Xinrui Kang | Liang Dong | Qinfeng Li
Source:  (article, 2022-11-17)
Field: Materials Science — Materials Chemistry

GANExplainer: Explainability Method for Graph Neural Network with Generative Adversarial Nets In recent years, graph neural networks (GNNs) have achieved encouraging performance in the processing of graph data generated in non-Euclidean space. GNNs learn node features by aggregating and combining neighbor information, which is applied to many graphics tasks. However, the complex deep learning structure is still regarded as a black box, which is difficult to obtain the full trust of human beings. Due to the lack of interpretability, the application of graph neural network is greatly limited. Therefore, we propose an interpretable method, called GANExplainer, to explain GNNs at the model level. Our method can implicitly generate the characteristic subgraph of the graph without relying on specific input examples as the interpretation of the model to the data. GANExplainer relies on the framework of generative-adversarial method to train the generator and discriminator at the same time. More importantly, when constructing the discriminator, the corresponding graph rules are added to ensure the effectiveness of the generated characteristic subgraph. We carried out experiments on synthetic dataset and chemical molecules dataset and verified the effect of our method on model level interpreter from three aspects: accuracy, fidelity and sparsity. Computer science ; Interpretability ; Graph ; Discriminator ; Artificial intelligence ; Generator (circuit theory) ; Artificial neural network ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Guofeng Qian | Karnpiwat Tantratian | Lei Chen | Zhen Hu | Michael D. Todd
Source: Research Square (preprint, 2022-06-09)
Field: Materials Science — Materials Chemistry

A Probabilistic Computational Framework for the Prediction of Corrosion-Induced Cracking in Large Structures Abstract Corrosion can initiate cracking that leads to structural integrity reduction. Quantitative corrosion assessment is challenging, and the modeling of corrosion-induced crack initiation is essential for model-based corrosion reliability analysis of various structures. This paper proposes a probabilistic computational analysis framework for corrosion-to-crack transitions by integrating a phase-field model with machine learning and uncertainty quantification. An electro-chemo-mechanical phase-field model is developed to predict pitting corrosion evolution, in which stress is properly coupled into the electrode chemical potential. A crack initiation criterion based on morphology is proposed to quantify the pit-to-cracking transition. A spatio-temporal surrogate modeling method is developed to facilitate this, consisting of a Convolution Neural Network (CNN) to map corrosion morphology to latent spaces, and a Gaussian Process regression model with a nonlinear autoregressive exogenous model (NARX) architecture for prediction of corrosion dynamics in the latent space over time. It enables the real-time prediction of corrosion morphology and crack initiation behaviors (whether, when, and where the corrosion damage triggers the crack initiation), and thus makes it possible for probabilistic analysis, with uncertainty quantified. Examples at various stress and corrosion conditions are presented to demonstrate the proposed computational framework. Corrosion ; Probabilistic logic ; Materials science ; Stress corrosion cracking ; Pitting corrosion ; Structural engineering ; Nonlinear autoregressive exogenous model ; Computer science ; Engineering ; Artificial intelligence ; Metallurgy ; Artificial neural network Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis
Authors: Akshay Krishna Ammothum Kandy | Alexis Raulin-Foissac | Ga ́etan Laurens | Julien Lam
Source: ChemRxiv (preprint, 2022-09-22)
Field: Materials Science — Materials Chemistry

Comparing transferability in neural network approaches and linear models formachine-learning interaction potentials Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training-set poses a significant challenge. Here, we compare the transferability of two MLIP approaches: i) Neural Network Potentials (NNP) and ii) Physical LassoLars Interactions Potential (PLIP), trained over a small but diverse configuration of zinc oxide polymorphs. We obtained good agreement between both MLIPs and density functional theory reference results for physical properties including bulk lattice parameters, surface energies, and vibrational density of states. However, the NNP model performed poorly when compared to the linear PLIP model for the structural optimization of nanoparticles and molecular dynamics simulation of liquid phases, which are systems outside the training-set. While generally providing less accurate results, the studied linear model appears more reliable than NNP when it comes to transferability in this specific example Transferability ; Artificial neural network ; Interatomic potential ; Computer science ; Set (abstract data type) ; Density functional theory ; Machine learning ; Artificial intelligence ; Molecular dynamics ; Statistical physics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions