jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohamed Trabelssi | S. El-Borgi | Michael I. Friswell
Source: Archive of Applied Mechanics (article, 2020-06-15)
Field: Materials Science — Materials Chemistry

A high-order FEM formulation for free and forced vibration analysis of a nonlocal nonlinear graded Timoshenko nanobeam based on the weak form quadrature element method Abstract The purpose of this paper is to provide a high-order finite element method (FEM) formulation of nonlocal nonlinear nonlocal graded Timoshenko based on the weak form quadrature element method (WQEM). This formulation offers the advantages and flexibility of the FEM without its limiting low-order accuracy. The nanobeam theory accounts for the von Kármán geometric nonlinearity in addition to Eringen’s nonlocal constitutive models. For the sake of generality, a nonlinear foundation is included in the formulation. The proposed formulation generates high-order derivative terms that cannot be accounted for using regular first- or second-order interpolation functions. Hamilton’s principle is used to derive the variational statement which is discretized using WQEM. The results of a WQEM free vibration study are assessed using data obtained from a similar problem solved by the differential quadrature method (DQM). The study shows that WQEM can offer the same accuracy as DQM with a reduced computational cost. Currently the literature describes a small number of high-order numerical forced vibration problems, the majority of which are limited to DQM. To obtain forced vibration solutions using WQEM, the authors propose two different methods to obtain frequency response curves. The obtained results indicate that the frequency response curves generated by either method closely match their DQM counterparts obtained from the literature, and this is despite the low mesh density used for the WQEM systems. Finite element method ; Discretization ; Nyström method ; Quadrature (astronomy) ; Nonlinear system ; Mathematics ; Vibration ; Mathematical analysis ; Timoshenko beam theory ; Applied mathematics ; Boundary value problem ; Structural engineering ; Physics ; Engineering Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Tuan Anh Nguyen | Thi Mai Hương Pham
Source: Journal of Chemistry (article, 2020-12-08)
Field: Materials Science — Polymers and Plastics

Study on the Properties of Epoxy Composites Using Fly Ash as an Additive in the Presence of Nanoclay: Mechanical Properties, Flame Retardants, and Dielectric Properties Small and light fly ash is a by-product of thermal power plants, in which oxides mainly present in fly ash are suitable to reinforce composite materials. Its content accounts for 10, 20, 30, 40, and 50% of those materials. However, due to the smooth surface, it cannot stick completely in plastics. Therefore, in this work, it was studied to combine nanoclay additive (I.30 E) with 1, 3, and 5% by weight to synergize to improve mechanical strength, fire retardation, and electrical properties. Mechanical properties and flame retardant properties have improved markedly. At the combined ratio of 40% by weight of fly ash and 3% nanoclay, nanocomposites have tensile strength values of 64.12 MPa, flexural strength of 89.27 MPa, compressive strength of 215.23 MPa, and impact resistance of Izod 14.45 kJ/m2, oxygen index limited to 26.8% of fire retardant material. In terms of dielectric strength, the electric strength of pure epoxy is 17.5 kV/mm, higher than that before adding nanoclay (12.7 kV/mm). The presence of nanoclays in the material creates a tortuous electric path, slowing the propagation of the power plant, which is the main factor that improves the breaking strength of the nanocomposite. Ultimate tensile strength ; Composite material ; Fly ash ; Flexural strength ; Fire retardant ; Epoxy ; Izod impact strength test ; Nanocomposite ; Compressive strength ; Composite number ; Materials science Polymer Nanocomposites and Properties ; High voltage insulation and dielectric phenomena ; Natural Fiber Reinforced Composites
Authors: Yuqi Song | Joseph Lindsay | Yong Zhao | Alireza Nasiri | Steph-Yves Louis | Jie Ling | Ming Hu | Jianjun Hu
Source: Computational Materials Science (article, 2020-05-30)
Field: Materials Science — Materials Chemistry

Machine Learning based prediction of noncentrosymmetric crystal materials Random forest ; Set (abstract data type) ; Computer science ; Inverse ; Machine learning ; Artificial intelligence ; Algorithm ; Materials science ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystal Structures and Properties
Authors: Bing Bai | Xu Han | Quan Zheng | Lixia Jia | Changyi Zhang | Wen Yang
Source: Fusion Engineering and Design (article, 2020-08-31)
Field: Materials Science — Materials Chemistry

Composition optimization of high strength and ductility ODS alloy based on machine learning Ultimate tensile strength ; Alloy ; Materials science ; Ductility (Earth science) ; Elongation ; Tensile testing ; Composite material ; Creep Fusion materials and technologies ; Nuclear Materials and Properties ; Metal and Thin Film Mechanics
Authors: Raimón Fabregat | Alberto Fabrizio | Benjamin Meyer | Daniel Hollas | Clémence Corminbœuf
Source: Journal of Chemical Theory and Computation (article, 2020-03-26)
Field: Materials Science — Materials Chemistry

Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible medium-size organic molecules at high <i>ab initio</i> level. We offer a modular environment in the python package MORESIM that allows custom design of replica exchange simulations with any level of theory including ML-based potentials. Our specific combination of Hamiltonian and reservoir replica exchange is shown to be a powerful technique to accelerate enhanced sampling simulations and explore free energy landscapes with a quantum chemical accuracy unattainable otherwise (e.g., DLPNO-CCSD(T)/CBS quality). This engine is used to demonstrate the relevance of accessing the <i>ab initio</i> free energy landscapes of molecules whose stability is determined by a subtle interplay between variations in the underlying potential energy and conformational entropy (i.e., a bridged asymmetrically polarized dithiacyclophane and a widely used organocatalyst) both in the gas phase and in solution (implicit solvent). Replica ; Python (programming language) ; Ab initio ; Hamiltonian (control theory) ; Computer science ; Modular design ; Statistical physics ; Potential energy ; Molecule ; Ab initio quantum chemistry methods ; Computational chemistry ; Chemistry ; Chemical physics ; Computational science ; Physics ; Quantum mechanics ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Catalysis and Oxidation Reactions
Authors: Hong‐Jian Feng | Kan Wu | Zun‐Yi Deng
Source: Cell Reports Physical Science (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

Predicting Inorganic Photovoltaic Materials with Efficiencies &gt;26% via Structure-Relevant Machine Learning and Density Functional Calculations Discovering new inorganic photovoltaic materials becomes an efficient way for developing a new generation of solar cells with high efficiency and environmental stability. Using machine learning (ML) and density functional theory calculations, we report four promising inorganic photovoltaic materials—Ba4Te12Ge4, Ba8P8Ge4, Sr8P8Sn4, and Y4Te4Se2—demonstrating notable theoretical photovoltaic performance for use in solar cells. The symmetry-allowed optical transition probability, the large amount of density of states near the conduction band minimum (CBM) and the valence band maximum (VBM), and the strong p-p transition across the band edge contribute to the large optical absorption coefficient, leading to the outstanding theoretical power conversion efficiency (PCE). The separation of the VBM and CBM wave function distributions contribute to the fast separation of the photogenerated electrons and holes and the enhanced carrier lifetimes. Our ML model is an efficient method for fast and atomic-level accuracy prediction of photovoltaic materials with different crystal structures. Photovoltaic system ; Materials science ; Density functional theory ; Energy conversion efficiency ; Optoelectronics ; Multiple exciton generation ; Electron ; Valence (chemistry) ; Band gap ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Electrical engineering Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties
Authors: Xiaoke Li | Wolfgang Paier | Joachim Paier
Source: Frontiers in Chemistry (article, 2020-11-30)
Field: Materials Science — Materials Chemistry

Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfaces The goal of many computational physicists and chemists is the ability to bridge the gap between atomistic length scales of about a few multiples of an Ångström (Å), i. e., 10<sup>-10</sup> m, and meso- or macroscopic length scales by virtue of simulations. The same applies to timescales. Machine learning techniques appear to bring this goal into reach. This work applies the recently published on-the-fly machine-learned force field techniques using a variant of the Gaussian approximation potentials combined with Bayesian regression and molecular dynamics as efficiently implemented in the Vienna <i>ab initio</i> simulation package, VASP. The generation of these force fields follows active-learning schemes. We apply these force fields to simple oxides such as MgO and more complex reducible oxides such as iron oxide, examine their generalizability, and further increase complexity by studying water adsorption on these metal oxide surfaces. We successfully examined surface properties of pristine and reconstructed MgO and Fe<sub>3</sub>O<sub>4</sub> surfaces. However, the accurate description of water-oxide interfaces by machine-learned force fields, especially for iron oxides, remains a field offering plenty of research opportunities. Oxide ; Force field (fiction) ; Ab initio ; Surface (topology) ; Generalizability theory ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Zachary W. Ulissi
Source:  (article, 2020-11-06)
Field: Materials Science — Materials Chemistry

The Open Catalyst 2020 (OC20) Dataset and Community Challenges Catalyst\ndiscovery and optimization is key to solving many societal\nand energy challenges including solar fuel synthesis, long-term energy\nstorage, and renewable fertilizer production. Despite considerable\neffort by the catalysis community to apply machine learning models\nto the computational catalyst discovery process, it remains an open\nchallenge to build models that can generalize across both elemental\ncompositions of surfaces and adsorbate identity/configurations, perhaps\nbecause datasets have been smaller in catalysis than in related fields.\nTo address this, we developed the OC20 dataset, consisting of 1,281,040\ndensity functional theory (DFT) relaxations (∼264,890,000 single-point\nevaluations) across a wide swath of materials, surfaces, and adsorbates\n(nitrogen, carbon, and oxygen chemistries). We supplemented this dataset\nwith randomly perturbed structures, short timescale molecular dynamics,\nand electronic structure analyses. The dataset comprises three central\ntasks indicative of day-to-day catalyst modeling and comes with predefined\ntrain/validation/test splits to facilitate direct comparisons with\nfuture model development efforts. We applied three state-of-the-art\ngraph neural network models (CGCNN, SchNet, and DimeNet++) to each\nof these tasks as baseline demonstrations for the community to build\non. In almost every task, no upper limit on model size was identified,\nsuggesting that even larger models are likely to improve on initial\nresults. The dataset and baseline models are both provided as open\nresources as well as a public leader board to encourage community\ncontributions to solve these important tasks. Computer science Machine Learning in Materials Science
Authors: Kevin Kaufmann | Chaoyi Zhu | Alexander S. Rosengarten | Daniel Maryanovsky | Haoren Wang | Kenneth S. Vecchio
Source: Microscopy and Microanalysis (article, 2020-05-11)
Field: Materials Science — Materials Chemistry

Phase Mapping in EBSD Using Convolutional Neural Networks The emergence of commercial electron backscatter diffraction (EBSD) equipment ushered in an era of information rich maps produced by determining the orientation of user-selected crystal structures. Since then, a technological revolution has occurred in the quality, rate detection, and analysis of these diffractions patterns. The next revolution in EBSD is the ability to directly utilize the information rich diffraction patterns in a high-throughput manner. Aided by machine learning techniques, this new methodology is, as demonstrated herein, capable of accurately separating phases in a material by crystal symmetry, chemistry, and even lattice parameters with fewer human decisions. This work is the first demonstration of such capabilities and addresses many of the major challenges faced in modern EBSD. Diffraction patterns are collected from a variety of samples, and a convolutional neural network, a type of machine learning algorithm, is trained to autonomously recognize the subtle differences in the diffraction patterns and output phase maps of the material. This study offers a path to machine learning coupled phase mapping as databases of EBSD patterns encompass an increasing number of the possible space groups, chemistry changes, and lattice parameter variations. Electron backscatter diffraction ; Convolutional neural network ; Diffraction ; Artificial intelligence ; Computer science ; Materials science ; Machine learning ; Pattern recognition (psychology) ; Optics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Zhuoying Jiang | Jiajie Hu | Babetta L. Marrone | Ghanshyam Pilania | Xiong Yu
Source: Materials (article, 2020-12-14)
Field: Materials Science — Biomaterials

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, <i>T</i><sub>g</sub>, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure-property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties. Artificial neural network ; Artificial intelligence ; Glass transition ; Machine learning ; Quantitative structure–activity relationship ; Lasso (programming language) ; Support vector machine ; Polyhydroxyalkanoates ; Materials science ; Computer science ; Biological system ; Dispersity ; Algorithm ; Polymer ; Polymer chemistry ; Composite material biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Advanced Sensor and Energy Harvesting Materials
Authors: Elyas Goli | Sagar Vyas | Seid Korić | Nahil Sobh | Philippe H. Geubelle
Source: The Journal of Physical Chemistry B (article, 2020-09-11)
Field: Chemistry — Organic Chemistry

ChemNet: A Deep Neural Network for Advanced Composites Manufacturing Among advanced manufacturing techniques for fiber-reinforced polymer-matrix composites (FRPCs) which are critical for aerospace, marine, automotive, and energy industries, frontal polymerization (FP) has been recently proposed to save orders of magnitude of time and energy. However, the cure kinetics of the matrix phase, usually a thermosetting polymer, brings difficulty to the design and control of the process. Here, we develop a deep learning model, ChemNet, to solve an inverse problem for predicting and optimizing the cure kinetics parameters of the thermosetting FRPCs for a desired fabrication strategy. ChemNet consists of a fully connected FeedForward 9 layer deep neural network trained on one million examples, and predicts activation energy and reaction enthalpy given the front characteristics such as speed and maximum temperature. ChemNet provides highly accurate predictions measured by the mean squared error (MSE) and by the maximum absolute error (MAE) metrics. ChemNet's performance on the "hidden" test data set had an MSE of 5.58 × 10<sup>-6</sup> and a MAE of 1 × 10<sup>-3</sup>. Thermosetting polymer ; Artificial neural network ; Materials science ; Mean squared error ; Aerospace ; Computer science ; Inverse ; Fabrication ; Composite material ; Mathematics ; Artificial intelligence ; Engineering ; Aerospace engineering Photopolymerization techniques and applications ; Machine Learning in Materials Science ; Dental materials and restorations
Authors: Natalia Kireeva | В. С. Первов
Source: Batteries & Supercaps (article, 2020-02-03)
Field: Materials Science — Materials Chemistry

Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data Abstract Lithium‐ion batteries (LIBs) are the objects of active research and attract interest as important elements of near‐term energy storage technologies. The ever‐growing requirements for cathode materials of next‐generation LIBs impel the need to screen the materials with high energy and power densities, cycling stability, rate capability, safety and compatibility with other battery elements. This study is focused on Li‐rich layered oxide cathode materials as the materials candidates that are characterized by high energy density and large capacity and are therefore attractive as the potential solution for next‐generation LIBs whereas moderate structural stability hinders their commercialization. Machine learning‐assisted analysis of the collected experimental data has been performed for assessing the contribution of lattice doping, composition of compounds, the method and details of synthesis in the electrochemical characteristics. The possibility to relate parameters with the formation of a certain structure type (composite or solid solution), conceivable phase transformations and space charge layer formation are discussed. From this analysis, the features most probably amenable for electrochemical characteristics enhancement are distinguished and the focus area for further research is defined. Materials science ; Cathode ; Electrochemistry ; Commercialization ; Oxide ; Energy storage ; Nanotechnology ; Electrochemical energy storage ; Engineering physics ; Electrical engineering ; Power (physics) ; Metallurgy ; Chemistry ; Physical chemistry ; Engineering ; Electrode Machine Learning in Materials Science ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Deepak Kamal | Anand Chandrasekaran | Rohit Batra | Rampi Ramprasad
Source: Machine Learning Science and Technology (article, 2020-03-18)
Field: Materials Science — Materials Chemistry

A charge density prediction model for hydrocarbons using deep neural networks Abstract The electronic charge density distribution ρ ( r ) of a given material is among the most fundamental quantities in quantum simulations from which many large scale properties and observables can be calculated. Conventionally, ρ ( r ) is obtained using Kohn–Sham density functional theory (KS-DFT) based methods. But, the high computational cost of KS-DFT renders it intractable for systems involving thousands/millions of atoms. Thus, recently there has been efforts to bypass expensive KS equations, and directly predict ρ ( r ) using machine learning (ML) based methods. Here, we build upon one such scheme to create a robust and reliable ρ ( r ) prediction model for a diverse set of hydrocarbons, involving huge chemical and morphological complexity /(saturated, unsaturated molecules, cyclo-groups and amorphous and semi-crystalline polymers). We utilize a grid-based fingerprint to capture the atomic neighborhood around an arbitrary point in space, and map it to the reference ρ ( r ) obtained from standard DFT calculations at that point. Owing to the grid-based learning, dataset sizes exceed billions of points, which is trained using deep neural networks in conjunction with a incremental learning based approach. The accuracy and transferability of the ML approach is demonstrated on not only a diverse test set, but also on a completely unseen system of polystyrene under different strains. Finally, we note that the general approach adopted here could be easily extended to other material systems, and can be used for quick and accurate determination of ρ ( r ) for DFT charge density initialization, computing dipole or quadrupole, and other observables for which reliable density functional are known. Initialization ; Density functional theory ; Computer science ; Observable ; Dipole ; Artificial neural network ; Grid ; Statistical physics ; Charge density ; Hyperparameter optimization ; Set (abstract data type) ; Algorithm ; Artificial intelligence ; Theoretical computer science ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Quantum mechanics ; Support vector machine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Yanhui Zhang | Alessandro Lunghi | Stefano Sanvito
Source: Acta Materialia (article, 2020-01-14)
Field: Materials Science — Materials Chemistry

Pushing the limits of atomistic simulations towards ultra-high temperature: A machine-learning force field for ZrB2 Aerospace ; Materials science ; Force field (fiction) ; Field (mathematics) ; Ceramic ; Thermal expansion ; Thermal ; Work (physics) ; Mechanical engineering ; Phonon ; Aerospace engineering ; Engineering physics ; Nanotechnology ; Computer science ; Condensed matter physics ; Composite material ; Thermodynamics ; Artificial intelligence ; Engineering ; Physics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research