jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bastien Casier | S. Carniato | Tsveta Miteva | Nathalie Capron | Nicolas Sisourat
Source: The Journal of Chemical Physics (article, 2020-06-15)
Field: Materials Science — Materials Chemistry

Using principal component analysis for neural network high-dimensional potential energy surface Potential energy surfaces (PESs) play a central role in our understanding of chemical reactions. Despite the impressive development of efficient electronic structure methods and codes, such computations still remain a difficult task for the majority of relevant systems. In this context, artificial neural networks (NNs) are promising candidates to construct the PES for a wide range of systems. However, the choice of suitable molecular descriptors remains a bottleneck for these algorithms. In this work, we show that a principal component analysis (PCA) is a powerful tool to prepare an optimal set of descriptors and to build an efficient NN: this protocol leads to a substantial improvement of the NNs in learning and predicting a PES. Furthermore, the PCA provides a means to reduce the size of the input space (i.e., number of descriptors) without losing accuracy. As an example, we applied this novel approach to the computation of the high-dimensional PES describing the keto-enol tautomerism reaction occurring in the acetone molecule. Principal component analysis ; Computer science ; Bottleneck ; Artificial neural network ; Context (archaeology) ; Computation ; Artificial intelligence ; Potential energy surface ; Set (abstract data type) ; Component (thermodynamics) ; Construct (python library) ; Machine learning ; Algorithm ; Chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Masanori Kohyama | Shingo Tanaka | Yoshinori Shiihara
Source: MATERIALS TRANSACTIONS (article, 2020-11-29)
Field: Materials Science — Materials Chemistry

&lt;i&gt;Ab Initio&lt;/i&gt; Local-Energy and Local-Stress Calculations for Materials Science and Engineering Revealing atomic-scale distributions of energy and stress in defective or complex systems, based on the behavior of electrons, should contribute much to materials science and engineering, while only few practical ab initio methods were developed for this purpose. Thus, we developed computational techniques of local-energy and local-stress calculations within the plane-wave PAW (projector augmented wave)-GGA (generalized gradient approximation) framework. This is natural extension of ab initio energy-density and stress-density schemes, while the inherent gauge dependency is removed by integrating these densities in each local region where the contained gauge-dependent terms are integrated to be zero. In this overview, we explain our scheme with some details and discuss the concepts or physical meanings of local energy and local stress via the comparison with related schemes using similar densities, LCAO (linear combination of atomic orbitals) methods, Green’s function formulation implemented by multiple scattering or TB (tight-binding) methods, or EAM (embedded-atom method) potentials. We present recent applications to metallic surfaces, grain boundaries (GBs) with and without solute segregation, tensile tests of metallic GBs, local elastic constants of microstructures in alloys, and machine-learning based GB-energy prediction, where the local-energy and local-stress analyses provide novel aspects of phenomena, deep insights into the mechanism, and effective data for novel machine-learning based modelling. We discuss unsettled issues and future applications, especially for large-scale metallic systems. Ab initio ; Materials science ; Local-density approximation ; Atom (system on chip) ; Atomic units ; Atomic orbital ; Embedded atom model ; Stress (linguistics) ; Statistical physics ; Density functional theory ; Electron ; Physics ; Computer science ; Quantum mechanics ; Molecular dynamics Microstructure and mechanical properties ; Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics
Authors: Lee A. Griffin | Iaroslav Gaponenko | Nazanin Bassiri‐Gharb
Source: Advanced Materials (article, 2020-08-14)
Field: Materials Science — Materials Chemistry

Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films Machine-learning techniques are more and more often applied to the analysis of complex behaviors in materials research. Frequently used to identify fundamental behaviors within large and multidimensional datasets, these techniques are strictly based on mathematical models. Thus, without inherent physical or chemical meaning or constraints, they are prone to biased interpretation. The interpretability of machine-learning results in materials science, specifically materials' functionalities, can be vastly improved through physical insights and careful data handling. The use of techniques such as dimensional stacking can provide the much needed physical and chemical constraints, while proper understanding of the assumptions imposed by model parameters can help avoid overinterpretation. These concepts are illustrated by application to recently reported ferroelectric switching experiments in PbZr<sub>0.2</sub> Ti<sub>0.8</sub> O<sub>3</sub> thin films. Through systematic analysis and introduction of physical constraints, it is argued that the behaviors present are not necessarily due to exotic mechanisms previously suggested, but rather well described by classical ferroelectric switching superimposed by non-ferroelectric phenomena, such as electrochemical deformation, electrostatic interactions, and/or charge injection. Interpretability ; Ferroelectricity ; Materials science ; Stacking ; Physical system ; Artificial intelligence ; Thin film ; Interpretation (philosophy) ; Machine learning ; Computer science ; Nanotechnology ; Physics ; Optoelectronics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Pavel Potocek | Patrick Trampert | Maurice Peemen | Remco Schoenmakers | Tim Dahmen
Source: Microscopy and Microanalysis (article, 2020-04-07)
Field: Materials Science — Surfaces, Coatings and Films

Sparse Scanning Electron Microscopy Data Acquisition and Deep Neural Networks for Automated Segmentation in Connectomics With the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to electron radiation. Random sparse scanning can be used in the combination with image reconstruction techniques to reduce the acquisition time or electron dose in scanning electron microscopy. In this study, we demonstrate a workflow that includes data acquisition on a scanning electron microscope, followed by a sparse image reconstruction based on compressive sensing or alternatively using neural networks. Neuron structures are automatically segmented from the reconstructed images using deep learning techniques. We show that the average dwell time per pixel can be reduced by a factor of 2-3, thereby providing a real-life confirmation of previous results on simulated data in one of the key segmentation applications in connectomics and thus demonstrating the feasibility and benefit of random sparse scanning techniques for a specific real-world scenario. Connectomics ; Computer science ; Artificial intelligence ; Compressed sensing ; Artificial neural network ; Segmentation ; Data acquisition ; Scanning electron microscope ; Pattern recognition (psychology) ; Computer vision ; Optics ; Connectome ; Physics ; Neuroscience Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: Troy D. Loeffler | Tarak K. Patra | Henry Chan | Subramanian K. R. S. Sankaranarayanan
Source: Molecular Systems Design & Engineering (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Active learning a coarse-grained neural network model for bulk water from sparse training data Active learning scheme to train neural network potentials for molecular simulations. Artificial neural network ; Computer science ; Training (meteorology) ; Artificial intelligence ; Scheme (mathematics) ; Training set ; Active learning (machine learning) ; Machine learning ; Physics ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Marco Fronzi | Sherif Abdulkader Tawfik | Mutaz Abu Ghazaleh | Olexandr Isayev | David A. Winkler | Joseph G. Shapter | Michael J. Ford
Source: Advanced Theory and Simulations (article, 2020-09-09)
Field: Materials Science — Materials Chemistry

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications Abstract The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first‐principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state‐of‐the‐art computational resources. Additionally, they can often be extremely time consuming. A time and resource efficient machine learning approach to create a dataset of structural properties of 18 million van der Waals layered structures is described. In particular, the authors focus on the interlayer energy and the elastic constant of layered materials composed of two different 2D structures that are important for novel solid lubricant and super‐lubricant materials. It is shown that machine learning models can predict results of computationally expansive approaches (i.e., density functional theory) with high accuracy. Expansive ; van der Waals force ; Computer science ; Lubricant ; Resource (disambiguation) ; Focus (optics) ; Field (mathematics) ; Density functional theory ; Nanotechnology ; Computational science ; Artificial intelligence ; Machine learning ; Materials science ; Physics ; Mathematics Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Sajitha Biju | Sigfredo Fuentes | Claudia Gonzalez Viejo | Damir D. Torrico | Sumayya Inayat | Sunanda Gupta
Source: Journal of the Science of Food and Agriculture (article, 2020-09-02)
Field: Chemistry — Analytical Chemistry

Silicon supplementation improves the nutritional and sensory characteristics of lentil seeds obtained from drought‐stressed plants The results demonstrated that Si supplementation can improve the nutritional and sensory properties of seeds. This study offers an innovative approach in sensory analysis coupled with biometrics to accurately assess a consumer's preference towards tested samples. In the future, the results of this study will help in making a predictive model for sensory traits and nutritional components in seeds using machine-learning modelling techniques. © 2020 Society of Chemical Industry. Phytic acid ; Nutrient ; Tannin ; Food science ; Biology ; Sensory system ; Crop ; Micronutrient ; Sensory analysis ; Biotechnology ; Agronomy ; Horticulture ; Medicine Heavy Metals in Plants ; Seed and Plant Biochemistry ; Food composition and properties
Authors: Kang Guan | Yong Gao | Qingfeng Zeng | Xingang Luan | Yi Zhang | Laifei Cheng | Jianqing Wu | Zhenya Lu
Source: Chinese Journal of Chemical Engineering (article, 2020-04-14)
Field: Materials Science — Ceramics and Composites

Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane Chemical vapor deposition ; Chemical vapor infiltration ; Stoichiometry ; Deposition (geology) ; Materials science ; Kinetics ; Analytical Chemistry (journal) ; Ceramic ; Chemical engineering ; Chemistry ; Composite material ; Physical chemistry ; Nanotechnology ; Environmental chemistry Advanced ceramic materials synthesis ; Thermal properties of materials ; Metal and Thin Film Mechanics
Authors: Ping Tuo | Xun Ye | Bicai Pan
Source: The Journal of Chemical Physics (article, 2020-03-16)
Field: Materials Science — Materials Chemistry

A machine learning based deep potential for seeking the low-lying candidates of Al clusters A Machine-Learning based Deep Potential (DP) model for Al clusters is developed through training with an extended database including ab initio data of both bulk and several clusters in only 6 CPU/h. This DP model has good performance in accurately predicting the low-lying candidates of Al clusters in a broad size range. Based on our developed DP model, the low-lying structures of 101 different sized Al clusters are extensively searched, among which the lowest-energy candidates of 69 sized clusters are updated. Our calculations demonstrate that machine-learning is indeed powerful in generating potentials to describe the interaction of atoms in complex materials. Lying ; Computer science ; Ab initio ; Artificial intelligence ; Range (aeronautics) ; Deep learning ; Machine learning ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Boron and Carbon Nanomaterials Research
Authors: Shreyas Honrao | Stephen Xie | Richard G. Hennig
Source: Journal of Applied Physics (article, 2020-08-26)
Field: Materials Science — Materials Chemistry

Augmenting machine learning of energy landscapes with local structural information We present a machine learning approach for accurately predicting formation energies of binary compounds in the context of crystal structure predictions. The success of any machine learning model depends significantly on the choice of representation used to encode the relevant physical information into machine-learnable data. We test different representation schemes based on partial radial and angular distribution functions (RDF+ADF) on Al–Ni and Cd–Te structures generated using our genetic algorithm for structure prediction. We observe a remarkable improvement in predictive accuracy upon transitioning from global to atom-centered representations, resulting in a threefold decrease in prediction errors. We show that a support vector regression model using a combination of atomic radial and angular distribution functions performs best at the formation energy prediction task, providing small root mean squared errors of 3.9 meV/atom and 10.9 meV/atom for Al–Ni and Cd–Te, respectively. We test the performance of our models against common traditional descriptors and find that RDF- and ADF-based representations significantly outperform many of those in the prediction of formation energies. The high accuracy of predictions makes our machine learning models great candidates for the exploration of energy landscapes. Support vector machine ; Context (archaeology) ; Atom (system on chip) ; Artificial intelligence ; Representation (politics) ; Binary number ; Computer science ; Machine learning ; Energy (signal processing) ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Thomas J. Oweida | Akhlak Mahmood | Matthew D. Manning | Sergei Rigin | Yaroslava G. Yingling
Source: MRS Advances (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

Merging Materials and Data Science: Opportunities, Challenges, and Education in Materials Informatics Materials informatics ; Informatics ; Limiting ; Engineering ethics ; Engineering informatics ; Curriculum ; Field (mathematics) ; Data science ; Materials science ; Computer science ; Health informatics ; Engineering ; Political science ; Mechanical engineering ; Health care Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Evgenii Malitckii | Eric Fangnon | Pedro Vilaça
Source: Neural Computing and Applications (article, 2020-03-24)
Field: Materials Science — Metals and Alloys

Study of correlation between the steels susceptibility to hydrogen embrittlement and hydrogen thermal desorption spectroscopy using artificial neural network Abstract Steels are the most used structural material in the world, and hydrogen content and localization within the microstructure play an important role in its properties, namely inducing some level of embrittlement. The characterization of the steels susceptibility to hydrogen embrittlement (HE) is a complex task requiring always a broad and multidisciplinary approach. The target of the present work is to introduce the artificial neural network (ANN) computing system to predict the hydrogen-induced mechanical properties degradation using the hydrogen thermal desorption spectroscopy (TDS) data of the studied steel. Hydrogen sensitivity parameter (HSP) calculated from the reduction of elongation to fracture caused by hydrogen was linked to the corresponding hydrogen thermal desorption spectra measured for austenitic, ferritic, and ferritic-martensitic steel grades. Correlation between the TDS input data and HSP output data was studied using two ANN models. A correlation of 98% was obtained between the experimentally measured HSP values and HSP values predicted using the developed densely connected layers ANN model. The performance of the developed ANN models is good even for never-before-seen steels. The ANN-coupled system based on the TDS is a powerful tool in steels characterization especially in the analysis of the steels susceptibility to HE. Hydrogen embrittlement ; Materials science ; Hydrogen ; Austenite ; Embrittlement ; Microstructure ; Desorption ; Thermal desorption ; Spectroscopy ; Thermal desorption spectroscopy ; Artificial neural network ; Characterization (materials science) ; Metallurgy ; Computer science ; Chemistry ; Nanotechnology ; Artificial intelligence ; Corrosion ; Physical chemistry ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Corrosion Behavior and Inhibition
Authors: Markus Stricker | W.A. Curtin
Source: The Journal of Physical Chemistry C (article, 2020-11-23)
Field: Materials Science — Biomaterials

Prismatic Slip in Magnesium Magnesium is the lowest-density structural metal but has low ductility that limits applications. The low ductility is related to the hexagonally close-packed crystal structure where activation of nonbasal slip is required for general plasticity. Here, our recent neural network potential (NNP) for Mg, trained using Kohn–Sham density functional theory (DFT), is used to examine slip of ⟨a⟩ dislocations on the prismatic plane. The generalized stacking fault surface energies (GSFEs) for basal and prismatic slip are computed and agree better with Kohn–Sham density functional theory (KS-DFT) than orbital-free density functional theory (OF-DFT) and modified embedded atom method (MEAM), which predict spurious minima. Consistent with the generalized stacking fault energy (GSFE), direct simulations of the prismatic ⟨a⟩ screw dislocation show it is unstable to dissociate into the ⟨a⟩ basal screw dislocation; this is mostly consistent with OF-DFT while MEAM predicts stability. Prismatic slip is thus achieved by a double-cross-slip process of the stable basal dislocations driven by a resolved shear stress on the orthogonal prismatic plane; this is consistent with the process deduced from experiments. The Nudged Elastic Band method is used with the NNP to examine the atomistic path and the stress-dependent enthalpy barrier for this mechanism; this requires many tens of thousands of atoms. The basal-prismatic cross-slip occurs in increments of c/2 via basal constriction, cross-slip on the prism plane, cross-slip back onto the basal plane, and lateral motion of the created jogs to extend the new basal dislocation. Comparisons with experimental deductions show some agreement and some notable disagreement. Resolution of the differences points toward further large-scale studies that require the accuracy and efficiency of KS-DFT-trained NNP, an approach that is also naturally extendable to the important domain of Mg alloys. Slip (aerodynamics) ; Density functional theory ; Peierls stress ; Stacking fault ; Materials science ; Dislocation ; Stacking-fault energy ; Crystallography ; Ductility (Earth science) ; Physics ; Computational chemistry ; Dislocation creep ; Chemistry ; Composite material ; Thermodynamics Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Metal and Thin Film Mechanics
Authors: Hua Bai
Source: Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Mechanism analysis, anti-corrosion techniques and numerical modeling of corrosion in energy industry Pipelines are the main transmission systems in energy industry thanks to its distinguished advantages on the low cost and high reliability. In order to ensure a safe and smooth pipeline operation, careful attentions must be paid to avoid structure failure and surface defect, which can both be attributed to corrosion. The past two decades has borne remarkable progress in our understanding of corrosion, with a tremendous increase in publications studying corrosion mechanisms and simulation. A review is constructed in this paper with three parts: mechanism analysis, anti-corrosion technique introduction, and numerical modeling and simulation. In mechanism, varieties of corrosions are found in engineering practice based on different electrochemical reaction conditions. The effect of temperature, pH, pressure, flow properties and steel types are reviewed and the mutual influence of these factors is presented. Short introductions to protect the pipelines from these corrosion mechanisms are attached respectively and a thorough comparison is also conducted. Advantages and shortcomings of popular coating techniques are concluded based on published studies, and special focus is paid on the idea of cathodic protection. Modeling of the electrochemical reaction process is incorporated with transportation equations to govern the direct numerical studies on pipeline corrosions. Meanwhile, empirical models have been proposed to estimate corrosion growth from both deterministic and stochastic points of view. Finite element method has been proved to be an effective approach to simulate pipeline corrosion and further integrity analysis. Suggestions on future studies on the numerical modeling and simulation of pipeline corrosion have been provided on three directions: advanced multiphase flow simulation schemes, thermodynamic analysis and the usage of big data technique and machine learning algorithm. This paper is wished to be helpful for the scholars who are interested in the numerical studies on pipeline corrosion and the engineers who are supporting pipeline operation and integrity management. Corrosion ; Cathodic protection ; Pipeline transport ; Pipeline (software) ; Reliability (semiconductor) ; Coating ; Computer science ; Integrity management ; Mechanism (biology) ; Finite element method ; Materials science ; Mechanical engineering ; Engineering ; Structural engineering ; Metallurgy ; Electrochemistry ; Nanotechnology ; Chemistry ; Power (physics) Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Hydrogen embrittlement and corrosion behaviors in metals