jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Heesu Hwang | Dohyoung Kim | Yoonmi Nam | Jong‐Ho Lee | Jin‐Ha Hwang
Source: ECS Transactions (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Synergistic Application of Machine Learning to Microstructural Characterization on Electrode Composites of Solid Oxide Fuel Cells Solid oxide fuel cells (SOFCs) have been recognized as one of the powerful next-generation energy conversion systems in association of the demanding green carbon technology. The electrochemical performance is crucially dependent on the intricate microstructures of porous electrodes, either cathodes or anodes. The composite electrodes should be analyzed in the sophisticated manner by characterizing the microstructural parameters. The current work combines electron microscopy with machine learning, more specifically semantic segmentation. The semantic segmentation was synergistically combined with high volume of electron micrographs enabled by FIB-SEM which has been used for microstructural analyses in SOFCs. The Ni/YSZ anode composites were selected as a model system, by incorporating the third components, i.e., pores featured by porous electrodes. The semantic segmentation-predicted image separation was connected with the conventional linear intercept approach, leading to the automated extraction of microstructural parameters. The work reports an exemplary analysis results based on the machine learning-assisted microstructure characterization in SOFC composite materials, implying that the machine learning-assisted approach becomes an essential tool in coping with high volume of electron microscopy-generated image data. Materials science ; Anode ; Microstructure ; Electrode ; Composite number ; Cathode ; Porosity ; Solid oxide fuel cell ; Scanning electron microscope ; Composite material ; Oxide ; Nanotechnology ; Metallurgy ; Electrical engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Anodic Oxide Films and Nanostructures
Authors: Churna Bhandari | Gavin Nop | Jonathan D. H. Smith | Durga Paudyal
Source: arXiv (Cornell University) (preprint, 2023-12-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accurate Machine Learning Predictions of Coercivity in High-Performance Permanent Magnets Increased demand for high-performance permanent magnets in the electric vehicle and wind turbine industries has prompted the search for cost-effective alternatives.Discovering new magnetic materials with the desired intrinsic and extrinsic permanent magnet properties presents a significant challenge to researchers because of issues with the global supply of rare-earth elements, material stability, and a low maximum magnetic energy product BH$_{max}$.While first-principle density functional theory (DFT) predicts materials' magnetic moments, magneto-crystalline anisotropy constants, and exchange interactions, it cannot compute coercivity ($H_c$).Although it is possible to calculate $H_c$ theoretically with micromagnetic simulations, the predicted value is larger than the experiment by almost an order of magnitude, due to the Brown paradox.To circumvent these, we employ machine learning (ML) methods on an extensive database obtained from experiments, DFT calculations, and micromagnetic modeling.The use of a large dataset enables realistic $H_c$ predictions for materials such as Ce-doped Nd$_2$Fe$_{14}$B, comparing favorably against micromagnetically simulated coercivities.Remarkably, our ML model accurately identifies uniaxial magneto-crystalline anisotropy as the primary contributor to $H_c$. With DFT calculations, we predict the Nd-site dependent magnetic anisotropy behavior in Nd$_2$Fe$_{14}$B, confirming that Nd $4g$-sites mainly contribute to uniaxial magneto-crystalline anisotropy, and also calculate Curie temperature (T$_{C}$).Both calculated results are in good agreement with experiment.The coupled experimental dataset and ML modeling with DFT input predict $H_c$ with far greater accuracy and speed than was previously possible using micromagnetic modeling.Further, we reverse-engineer the inter-grain exchange coupling with micromagnetic simulations by employing the ML predictions. Coercivity ; Magnet ; Anisotropy ; Condensed matter physics ; Materials science ; Density functional theory ; Magnetic anisotropy ; Curie temperature ; Physics ; Magnetization ; Ferromagnetism ; Magnetic field ; Optics ; Quantum mechanics Magnetic Properties of Alloys ; Magnetic properties of thin films ; Magnetic Properties and Applications
Authors: Long Chen | Jibao Lu | Shu Liu | Rong Sun
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Polymers and Plastics

A Data-Driven Method for Design of Foam Structures with High Effective Thermal Conductivity Thermal conductivity ; Materials science ; Thermal ; Metal foam ; Composite material ; Physics ; Thermodynamics ; Porosity Polymer Foaming and Composites
Authors: А. K. Ivanov-Schitz
Source: Russian Journal of Electrochemistry (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Solid State Ionics 2011–2021: Trends and Prospects Based on the analysis of publication activity, trends in the development of the main sections of solid state ionics have been formulated by using expertly curated abstract & citation database of peer-reviewed scientific literature Scopus. Promising areas of research related to in situ and operando experiments, artificial intelligence (machine learning), and the design of new devices using superionic materials are indicated. Scopus ; Citation ; Data science ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Library science ; MEDLINE Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Semiconductor materials and devices
Authors: Santosh Adhikari | Jacob M. Clary | Ravishankar Sundararaman | Charles B. Musgrave | Derek Vigil‐Fowler | Christopher Sutton
Source: ChemRxiv (preprint, 2023-05-28)
Field: Materials Science — Materials Chemistry

Accurate Prediction of HSE06 Band Structures for a Diverse Set of Materials Using ∆-learning We used machine learning (ML) to accurately predict eigenvalues of the hybrid HSE06 functional using eigenvalues computed by the less computationally expensive PBE and associated electronic features based on the k-point resolved atomic band char- acter. The ML model was trained using eigenvalues from only one k-point for each of the 168 compounds in the training set. The HSE06 eigenvalues across all k-points were then predicted for a separate set of 169 compounds with a mean absolute error (MAE) of 0.13 eV, representing a significant improvement over the error of PBE-computed eigenvalues relative to HSE06 (0.96 eV). These accurately predicted eigenvalues result in remarkably accurate predictions for the band structures, projected density of states and band gaps, even though the model was not explicitly trained on these other prop- erties. Finally, we demonstrate that our ML model has a similar accuracy for both ternary and quaternary compounds well outside the initial training set and on systems with 112 and 160 atoms, demonstrating its potential to rapidly predict HSE06-quality electronic structures of complex materials that are practically unfeasible for HSE06. Eigenvalues and eigenvectors ; Ternary operation ; Hybrid functional ; Set (abstract data type) ; Point (geometry) ; Training set ; Dimension (graph theory) ; Artificial intelligence ; Computer science ; Algorithm ; Machine learning ; Mathematics ; Density functional theory ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Lukas Müllender | Rizzi, Andrea | Michele Parrinello | Paolo Carloni | Davide Mandelli
Source: arXiv (Cornell University) (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

Effective Data-Driven Collective Variables for Free Energy Calculations from Metadynamics of Paths A variety of enhanced sampling methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected collective variables (CVs). The accuracy of these methods is crucially dependent on the ability of the chosen CVs to capture the relevant slow degrees of freedom of the system. For complex processes, finding such CVs is the real challenge. Machine learning (ML) CVs offer, in principle, a solution to handle this problem. However, these methods rely on the availability of high-quality datasets -- ideally incorporating information about physical pathways and transition states -- which are difficult to access, therefore greatly limiting their domain of application. Here, we demonstrate how these datasets can be generated by means of enhanced sampling simulations in trajectory space via the metadynamics of paths [arXiv:2002.09281] algorithm. The approach is expected to provide a general and efficient way to generate efficient ML-based CVs for the fast prediction of free energy landscapes in enhanced sampling simulations. We demonstrate our approach with two numerical examples, a two-dimensional model potential and the isomerization of alanine dipeptide, using deep targeted discriminant analysis as our ML-based CV of choice. Metadynamics ; Computer science ; Sampling (signal processing) ; Energy landscape ; Complex system ; Energy (signal processing) ; Trajectory ; Degrees of freedom (physics and chemistry) ; Function (biology) ; Configuration space ; Algorithm ; Artificial intelligence ; Data mining ; Molecular dynamics ; Mathematics ; Chemistry ; Physics ; Computational chemistry ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Complex Network Analysis Techniques
Authors: Feicheng Huan | Chenglong Qiu | Yin Sun | Gaoyang Luo | Shengwei Deng | Jianguo Wang
Source: Industrial & Engineering Chemistry Research (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Exploration of a Two-Dimensional Nanoslit for Blast Furnace Gas Separation Diffusion-induced gas separation is crucial for industrial applications, while the determination of specific conditions is still challenging. Here, molecular dynamics simulation data were used to train machine learning models to identify the effective separation conditions for blast furnace gas confined in nanosilts with different absorption strengths (graphene and graphene oxide). The diffusion coefficients and exponents of the blast furnace gas were obtained as a database by molecular dynamics (MD) simulations. Several environmental and structural controlling factors (such as temperature, layer distance, atomic number, ionization potential, etc.) were extracted through importance analysis. And the relationships between these factors and diffusion properties were further established by the Kernel Ridge Regression algorithm. Based on the differences in diffusion coefficients, specific binary gas mixtures in the blast furnace gas with competitively high separation potential have been screened out by a trained machine learning model and verified by MD simulations. The simulation strategy can provide theoretical guidance for the structural design of membranes for diffusion-controlled gas separation. Diffusion ; Gas separation ; Molecular dynamics ; Graphene ; Chemistry ; Kernel (algebra) ; Membrane ; Separation (statistics) ; Materials science ; Computer science ; Thermodynamics ; Nanotechnology ; Machine learning ; Physics ; Computational chemistry ; Mathematics Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Zeolite Catalysis and Synthesis
Authors: Mingyue Wu | Luis Ruiz Pestana
Source: Frontiers in Materials (article, 2023-09-12)
Field: Materials Science — Materials Chemistry

Identifying a machine-learning structural descriptor linked to the creep behavior of Kob-Andersen glasses A wide variety of materials, ranging from metals to concrete, experience, typically at high-temperatures or over long time scales, permanent deformations when subjected to sustained loads below their yield stress—a phenomenon known as creep. While theories grounded on defects such as vacancies, dislocations, or grain boundaries can explain creep in crystalline materials, our understanding of creep in disordered solids remains incomplete due to the lack of analogous structural descriptors. In this study, we use molecular dynamics to simulate the creep response of a Kob-Andersen glass model system under constant, uniaxial, compressive stress at finite temperature. We leverage that data to derive, using a machine-learning classification model, a structural descriptor termed looseness , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi mathvariant="double-struck">L</mml:mi></mml:mrow></mml:math> , which is based on simple and interpretable local structural features and can predict imminent plastic rearrangements within the glass. We show that the average looseness of the system evolves logarithmically with time, mirroring the time dependence of the creep strain and demonstrating the ability of our model to bridge local, short-term particle dynamics with the long-term macroscopic creep response. A detailed feature importance analysis reveals the particular significance of short-range structural heterogeneity in the prediction. We also scrutinize the spatial and temporal correlations of looseness , which mirror the lack of long-range order in glasses and their dynamic heterogeneity. Our research underscores the substantial predictive potential of machine-learning-derived structural indicators in systems experiencing concurrent stress and thermal excitations, paving the way for future work to elucidate the interplay between thermal and mechanical activation of structural defects in disordered solids. Creep ; Materials science ; Leverage (statistics) ; Stress (linguistics) ; Molecular dynamics ; Artificial intelligence ; Statistical physics ; Computer science ; Composite material ; Physics Material Dynamics and Properties ; Theoretical and Computational Physics ; Glass properties and applications
Authors: Chang Liu | Hiromasa Tamaki | Tomoyasu Yokoyama | Kensuke Wakasugi | Satoshi Yotsuhashi | Minoru KUSABA | Ryo Yoshida | Yoshida, Ryo
Source: arXiv (Cornell University) (preprint, 2023-05-03)
Field: Materials Science — Materials Chemistry

Shotgun crystal structure prediction using machine-learned formation energies Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward solving the crystal structure prediction problem: we performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor. This shotgun method (ShotgunCSP) has two key technical components: transfer learning for accurate energy prediction of pre-relaxed crystalline states, and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures. First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures. The ShotunCSP method is computationally less intensive than conventional methods and exhibits exceptional prediction accuracy, reaching 93.3% in benchmark tests with 90 different crystal structures. Crystal structure prediction ; Maxima and minima ; Benchmark (surveying) ; Computer science ; Crystal (programming language) ; Metastability ; Workflow ; Algorithm ; Artificial intelligence ; Machine learning ; Crystal structure ; Computational science ; Physics ; Mathematics ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Cheng Li | Jiayin Wu
Source:  (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

Bandgap Prediction of Two-Dimensional Transition Metal Layered Binary Compounds Based on Regression Forest Machine Learning Method Two-dimensional (2D) materials, renowned for their exceptional electronic properties, have found widespread applications in various domains. Accurate prediction of the bandgap width, a critical characteristic of 2D materials, is a challenging task due to bandgap underestimating associated with the PBE calculation method and the prohibitively high computational costs of the HSE approach. In this study, we propose the application of machine learning methodologies—Regression Forest, Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Regression (SVR), Ridge Regression (RIDGE), Elastic networks (Elastic Net), Adaptive Boosting Regression (AdaBoost Regression), Regression tree, and Gradient Boosting Regression (GBR)—to train a regression model. This regression model is designed to capture the 16 essential characteristics of 2D transition metal layered binary compounds and the corresponding HSE bandgap theory values. To assess the model’s performance, we employ the 10-fold cross-validation technique. The experimental findings demonstrate that machine learning-based bandgap prediction for 2D transition metal layered binary compounds not only achieves remarkable accuracy but also significantly reduces the computational requirements. Consequently, this research offers a promising and efficient approach for the precise characterization of various 2D material properties. Code available: https://github.com/lyc209059/BandReF (Password: BandReF3358code) Binary number ; Band gap ; Regression ; Materials science ; Computer science ; Regression analysis ; Artificial intelligence ; Machine learning ; Optoelectronics ; Mathematics ; Statistics Machine Learning in Materials Science ; Advanced Computing and Algorithms ; Gas Sensing Nanomaterials and Sensors
Authors: Shanping Liu | Romain Dupuis | Fan Dong | Salma Benzaria | Michael Bonneau | Prashant M. Bhatt | Mohamed Eddaoudi | Guillaume Maurin
Source: arXiv (Cornell University) (preprint, 2023-07-28)
Field: Chemistry — Inorganic Chemistry

Machine Learning Potential for Modelling H$_2$ Adsorption/Diffusion in MOF with Open Metal Sites Metal-organic frameworks (MOFs) incorporating open metal sites (OMS) have been identified as promising sorbents for many societally relevant-adsorption applications including CO$_2$ capture, natural gas purification and H$_2$ storage. It is critical to derive generic interatomic potential to achieve accurate and effective evaluation of MOFs for H$_2$ adsorption. On this path, as a proof-of-concept, the Al-soc-MOF containing Al-OMS, previously envisaged as a potential candidate for H$_2$ adsorption, was selected and a machine learning potential (MLP) was derived from a dataset initially generated by ab-initio molecular dynamics (AIMD) simulations. This MLP was further implemented in MD simulations to explore the binding modes of H$_2$ as well as its temperature dependence distribution in the MOFs pores from 10K to 90K. MLP-Grand Canonical Monte Carlo (GCMC) simulations were further performed to predict the H$_2$ sorption isotherm of Al-soc-MOF at 77K that was further confirmed by gravimetric sorption measurements. As a further step, MLP-based MD simulations were conducted to anticipate the kinetics of H$_2$ in this MOF. This work delivers the first MLP able to describe accurately the interactions between the challenging H$_2$ guest molecule and MOFs containing OMS. This innovative strategy applied to one of the most complex molecules owing to its highly polarizable nature alongside its quantum-mechanical effects that are only accurately described by quantum calculations, paves the way towards a more systematic accurate and efficient in silico assessment of the MOFs containing OMS for H$_2$ adsorption and beyond to the low-pressure capture/sensing of diverse molecules. Adsorption ; Metal-organic framework ; Sorption ; Molecule ; Molecular dynamics ; Ab initio ; Gravimetric analysis ; Chemistry ; Monte Carlo method ; Diffusion ; Materials science ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Physics ; Organic chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: O. Narivs’kyi | Геннадій Валентинович Сніжной | С. А. Субботин | Serhii Leoshchenko | Tеtiana Pulina | Valentyn Snizhnoi
Source: New Materials and Technologies in Metallurgy and Mechanical Engineering (article, 2023-04-20)
Field: Materials Science — Materials Chemistry

MATHEMATICAL MODELING OF THE CORROSION BEHAVIOR OF AUSTENITIC STEELS IN CHLORIDE-CONTAINING ENVIRONMENTS DURING THE OPERATION OF PLATE HEAT EXCHANGERS Purpose. It consists in the development of mathematical models that describe the dependence of the critical pitting temperatures of AISI 304, 08X18N10, AISI 321, 12X18N10T steels in model circulating waters with pH 4...8 and chloride concentration from 350 to 600 mg/l.&#x0D; Research methods. The developed mathematical models are based on linear square regression and a neural network of direct signal propagation for a reduced set of features.&#x0D; Results. It was established that the critical pitting temperatures of the studied austenitic chrome-nickel steels increase with an increase in the pH of the circulating water, the number of oxides up to 3.95 μm in size, the average distance between titanium nitrides, the Cr content and a decrease in the concentration of chlorides in the circulating water, the average distance between oxides, and the austenite average grain diameter.&#x0D; Scientific novelty. Based on the established relationships between the critical pitting temperatures of corrosion-resistant steels AISI 304, 08Х18Н10, AISI 321, 12Х18Н10Т, their chemical composition within the standard and structural heterogeneity, the mechanisms of their influence on the pitting resistance of these structural materials in circulating chloride-containing waters have been developed. It was established that metastable pitting is formed in a solid solution of austenite of steels around oxides with a size of 1,98...3,95 microns and repassivates before reaching critical dimensions of about 5 microns, which contributes to the growth of their pitting resistance in reversible chloride-containing environments.&#x0D; Practical value. The developed mathematical models are proposed to be used for the selection of optimal melts of austenitic chrome-nickel steels for the production of heat exchangers and prediction of their pitting resistance during their operation in circulating waters. The processes contributing to the perforation of heat transfer elements of heat exchangers during their operation have been identified. Austenite ; Heat exchanger ; Corrosion ; Metallurgy ; Chloride ; Materials science ; Mechanical engineering ; Engineering ; Microstructure Metal Alloys Wear and Properties ; Metallurgical Processes and Thermodynamics ; Hydrogen embrittlement and corrosion behaviors in metals