jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simone Venturi | Tiernan Casey
Source:  (article, 2022-05-01)
Field: Materials Science — Materials Chemistry

Constructing Surrogates for Combustion Chemistry:Operator Learning for Reduced Order Dynamics. Universal approximation theorem of operators [1][1] T. Chen and H. Chen -Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and -1995 [2] L. Lu et al. -Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators -2021 We are interested in advancing the chemical state in time, not necessarily by surrogating source terms (Unstacked) DeepONet [2] Combustion ; Operator (biology) ; Computer science ; Order (exchange) ; Dynamics (music) ; Chemistry ; Physics ; Physical chemistry Crystallization and Solubility Studies ; Statistical and Computational Modeling ; Advanced Control Systems Optimization
Authors: Van-Quyen Nguyen | Viet-Cuong Nguyen | Nguyễn Tiến Cường | Nguyen-Xuan-Vu Nguyen | Tien-Lam Pham
Source: Computational Materials Science (article, 2022-04-06)
Field: Materials Science — Materials Chemistry

Pairwise interactions for potential energy surfaces and atomic forces using deep neural networks Pairwise comparison ; Artificial neural network ; Computer science ; Statistical physics ; Feature (linguistics) ; Atom (system on chip) ; Molecular dynamics ; Artificial intelligence ; Biological system ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Michael J. Waters | James M. Rondinelli
Source: Journal of Physics Condensed Matter (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Benchmarking structural evolution methods for training of machine learned interatomic potentials When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. We benchmark two other modalities of evolving structures, contour exploration (CE) and dimer-method (DM) searches against MD for their ability to produce diverse and robust density functional theory training data sets for MLIPs. We also discuss the generation of initial structures which are either from known structures or from random structures in detail to further formalize the structure-sourcing processes in the future. The polymorph-rich zirconium-oxygen composition space is used as a rigorous benchmark system for comparing the performance of MLIPs trained on structures generated from these structural evolution methods. Using Behler-Parrinello neural networks as our MLIP models, we find that CE and the DM searches are generally superior to MD in terms of spatial descriptor diversity and statistical accuracy. Benchmark (surveying) ; Benchmarking ; Computer science ; Molecular dynamics ; Artificial intelligence ; Machine learning ; Space (punctuation) ; Artificial neural network ; Statistical physics ; Algorithm ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Inorganic Chemistry and Materials
Authors: Ruimin Chen | Ming Ren | Yanshun Yu | Ming Dong | Xia Chen
Source: IET conference proceedings. (article, 2022-03-29)
Field: Materials Science — Materials Chemistry

Prediction of pollution flashovervoltage based on multiple characteristics of leakage current With the accumulation of dirt and chemicals from the environment, outdoor insulator pollution flashover accidents tend to occur, which are threatening the operation safety of the power system. In this paper, a model is presented to predict the pollution flashover voltage by analyzing multiple characteristics of leakage current under AC operating voltage. By means of setting different equivalent salt deposit density(ESDD) and non-soluble deposit density(NSDD), insulators could be tested under simulated environment. Then, leakage current flowing along the surface of composite material insulators(FXBW4-10/70) can be monitored when an increasing voltage is applied. According to the time and frequency domain analysis, different parameters, i.e. the root mean square(RMS), proportion of harmonics and so on can also be measured. Finally, by adopting the theory ofprincipal component analysis(PCA) and artificial neural network(ANN), the value of pollution flashover voltage is predictedaccurately with simplifying data sets. This model modifies traditional diagnostic patterns which are only connected with singlecharacteristic and it is expected to become a multi-characteristic method for the prediction of pollution flashover voltage in thepower system. Arc flash ; Insulator (electricity) ; Voltage ; Dirt ; Pollution ; Environmental science ; Harmonics ; Root mean square ; Three gorges ; Acoustics ; Materials science ; Engineering ; Electrical engineering ; Geotechnical engineering ; Mechanical engineering High voltage insulation and dielectric phenomena ; Elevator Systems and Control ; Power Transformer Diagnostics and Insulation
Authors: S. A. Shcherbinin | Svetlana V. Ustiuzhanina | Elena A. Korznikova | Andrey A. Kistanov
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

First-Principles Investigation of Two-Dimensional Magnesium Chloride: Environmental Stability and Fundamental Properties Magnesium ; Stability (learning theory) ; Materials science ; Computer science ; Metallurgy Layered Double Hydroxides Synthesis and Applications ; Magnesium Oxide Properties and Applications
Authors: Vandenhaute, Sander | Van Speybroeck, Veronique | Verstraelen, Toon | Cools-Ceuppens, Maarten
Source: Ghent University Academic Bibliography (Ghent University) (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

Machine learning potentials for metal-organic frameworks with thermodynamic transferability Computational modeling of physical processes in realistic metal-organic frameworks (MOFs) is extremely challenging because of the omnipresence of spatial heterogeneities as well as complex operating conditions [1]. While density functional theory (DFT) is routinely used to describe interatomic interactions at quantum chemical accuracy, it becomes prohibitively expensive for realistic MOF systems because the length and time scales often stretch far beyond the nanometer and picosecond range [2]. In this work, we build upon the most recent advances in data-efficient machine learning potentials (MLPs) to construct efficient interaction potentials for framework materials in order to enable large-scale simulations at quantum chemical accuracy. To this end, we exploit the power of neural equivariant interatomic potentials (NequIP), which complement invariant distance-like features with equivariant vector-like features and achieve unprecedented data efficiency [3]. For both rigid (UiO-66(Zr)) and flexible (MIL-53(Al)) frameworks, we demonstrate that a few hundred reference configurations are sufficient to obtain accurate potentials that are thermodynamically transferable across a wide range of temperatures and pressures. The structural and mechanical properties of the frameworks are correctly predicted, and additional large-scale simulations of the phase transition in MIL-53(Al) are used to provide transition pressure estimates between 18 - 20 MPa, which are in excellent agreement with mercury porosimetry experiments. The proposed methodology is universally applicable and may induce a paradigm shift in both the accuracy as well as the time and length scale of computational models for framework materials. [1] Dalton Trans., 45:4113–4126, 2016. [2] Chem. Rev., 114(20):10575–10612, 2014. [3] arXiv:2101.03164 [physics.comp-ph] Invariant (physics) ; Granularity ; Range (aeronautics) ; Physical system ; Interatomic potential ; Exploit ; Quantum ; Visualization Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Magnetism in coordination complexes
Authors: Alexander Ryabov | Iskander Akhatov | Petr Zhilyaev
Source: Research Square (preprint, 2022-05-27)
Field: Materials Science — Materials Chemistry

Application of two-component neural network for exchange-correlation functional interpolation Abstract Density functional theory (DFT) is one of the primary approaches to solving the many-body Schrodinger equation. The essential part of the DFT theory is the exchange-correlation (XC) functional, which can not be obtained in analytical form. Accordingly, the accuracy improvement of the DFT is mainly based on the development of XC functional approximations. Commonly, they are built upon analytic solutions in low-and high-density limits and result from quantum Monte Carlo or post-Hartree-Fock numerical calculations. However, there is no universal functional form to incorporate these data into XC functional. Instead, various parameterizations use heuristic rules to build a specific XC functional. The neural network (NN) approach to interpolate the data from higher precision theories can give a unified path to parametrize an XC functional. Moreover, data from many existing quantum chemical databases could provide the XC functional with improved accuracy. We develop NN XC functional, which gives exchange potential and energy density without direct derivatives of exchange-correlation energy density. Proposed NN architecture consists of two parts NN-E and NN-V, which could be trained in separate ways, adding new flexibility to XC functional. We also show that the developed NN XC functional converges in the self-consistent cycle and gives reasonable energies when applied to atoms, molecules, and crystals. Density functional theory ; Hybrid functional ; Orbital-free density functional theory ; Interpolation (computer graphics) ; Energy functional ; Functional theory ; Statistical physics ; Artificial neural network ; Physics ; Algorithm ; Computer science ; Quantum mechanics ; Artificial intelligence ; Classical mechanics Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Inorganic Chemistry and Materials
Authors: Steve Cranford
Source: Patterns (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Navigating the “Kessel Run” of digital materials acceleration Computational methods such as machine learning, artificial intelligence, and big data in physical sciences, particularly materials science, have been exponentially growing in terms of progress, method development, and number of studies and related publications. This aggregate momentum of the community is palpable, and many exciting discoveries are likely on the horizon. But, like all endeavors, some thought should be given to the current trajectory of the field, ensuring the full potential of the new digital space. Acceleration ; Computer science ; Computer graphics (images) ; Physics ; Classical mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Alexandre Melhorance Barboza | Luiz César Rodríguez Aliaga | Daiara Faria | Ivan Napoleão Bastos
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Bilayer Graphene Kirigami Graphene ; Bilayer graphene ; Materials science ; Nanotechnology Graphene research and applications ; Graphene and Nanomaterials Applications ; Advanced Materials and Mechanics
Authors: Ihsan Ali Suwarno | Nafis Khuriyati | Makhmudun Ainuri
Source: Advances in biological sciences research/Advances in Biological Sciences Research (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Detection of the Existence Rhodamine B in Chili Paste with Digital Image Processing The need for chili paste is increasing in line with the increasing variety of types and menus of dishes that use chili paste. To improve the colour of chili paste some traders use the textile dye Rhodamine B which causes significant health risks. The purpose of this study is to develop an image processing system that can detect the existence of Rhodamine B in chili paste quickly and practically using image processing and artificial neural networks (ANN). Image data retrieval using digital image acquisition techniques with the help of a webcam which is placed on the image capture toolbox and a computer. The number of samples used were 172 training data, 16 validation data, and 12 test data consisting of chili paste with the addition of various variations in the amount of Rhodamine B, namely 0 gram; 0.25 gram; 0.5 gram; 1 gram of Rhodamine B in 1 kg of chili paste. The data that has been extracted from the colour and texture features are then further processed and trained with an artificial neural network using the backpropagation method in order to detect the existence of Rhodamine B in chili paste properly. The resulting test system was then tested on 20 market samples taken from 4 different markets. The results of this study indicate that the parameters used to detect the existence of Rhodamine B in chili paste are parameters a, green, L, and b. The structure of the artificial neural network system consists of 4 input layers, 24 hidden layers, and 2 output layers. The GUI (Graphical User Interface) can detect the existence of Rhodamine B in chili paste with variations in the amount of Rhodamine B with the accuracy validation of the ANN system in detecting Rhodamine B in chili paste samples by 100% and testing the ANN system by 83%. Testing market samples with the ANN system gets an accuracy of 50%, the accuracy of the market samples is because the materials used are not controlled or heterogeneous. Computer science ; Image processing ; Digital image processing ; Digital image ; Image (mathematics) ; Rhodamine B ; Computer vision ; Computer graphics (images) ; Artificial intelligence ; Chemistry Dye analysis and toxicity ; Spectroscopy and Chemometric Analyses
Authors: Saketh Sridhara | Aaditya Chandrasekhar | Krishnan Suresh
Source: arXiv (Cornell University) (preprint, 2022-07-13)
Field: Materials Science — Materials Chemistry

A Generalized Framework for Microstructural Optimization using Neural Networks Microstructures, i.e., architected materials, are designed today, typically, by maximizing an objective, such as bulk modulus, subject to a volume constraint. However, in many applications, it is often more appropriate to impose constraints on other physical quantities of interest. In this paper, we consider such generalized microstructural optimization problems where any of the microstructural quantities, namely, bulk, shear, Poisson ratio, or volume, can serve as the objective, while the remaining can serve as constraints. In particular, we propose here a neural-network (NN) framework to solve such problems. The framework relies on the classic density formulation of microstructural optimization, but the density field is represented through the NN's weights and biases. The main characteristics of the proposed NN framework are: (1) it supports automatic differentiation, eliminating the need for manual sensitivity derivations, (2) smoothing filters are not required due to implicit filtering, (3) the framework can be easily extended to multiple-materials, and (4) a high-resolution microstructural topology can be recovered through a simple post-processing step. The framework is illustrated through a variety of microstructural optimization problems. Smoothing ; Constraint (computer-aided design) ; Mathematical optimization ; Simple (philosophy) ; Optimization problem ; Computer science ; Variety (cybernetics) ; Artificial neural network ; Topology optimization ; Sensitivity (control systems) ; Topology (electrical circuits) ; Algorithm ; Mathematics ; Artificial intelligence ; Engineering ; Finite element method ; Geometry ; Structural engineering Machine Learning in Materials Science ; Topology Optimization in Engineering
Authors: Shuang Tang | Lucy Dow | Emmanuel Ojukwu
Source: arXiv (Cornell University) (preprint, 2022-01-15)
Field: Materials Science — Materials Chemistry

Pattern Recognition for the Electronic Phase of Bismuth Antimony Thin Films There are many applications involving the use of bismuth antimony thin films. However, due to the low crystalline symmetry and strong coupling between the electronic band edges, it has always been challenging to infer the electronic phase of such a material. Fortunately, with the development of pattern recognition technology, scientists can build many black-box tools for predicting various materials properties. In this present work, we have developed several pattern recognition tools to predict the electronic phase of a bismuth antimony thin film. The support vector machine, the decision tree, and the artificial neural network are used to achieve a prediction accuracy of ~90%, ~95% and ~100%, respectively. Bismuth ; Antimony ; Materials science ; Thin film ; Phase (matter) ; Artificial neural network ; Decision tree ; Coupling (piping) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Nanotechnology ; Physics ; Metallurgy Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Advanced Thermoelectric Materials and Devices
Authors: Andrés I. Bertoni | Cristián G. Sánchez
Source: arXiv (Cornell University) (preprint, 2022-05-12)
Field: Materials Science — Materials Chemistry

Data-driven approach for benchmarking DFTB-approximate excited state methods In this work we propose a chemically-informed data-driven approach to benchmark the approximate density-functional tight-binding (DFTB) excited state (ES) methods that are currently available within the DFTB+ suite. By taking advantage of the large volume of low-detail ES-data in the machine learning (ML) dataset, QM8, we were able to extract valuable insights regarding the limitations of the benchmarked methods in terms of the approximations made to the parent formalism, density-functional theory (DFT), while providing recommendations on how to overcome them. For this benchmark, we compared the first singlet-singlet vertical excitation energies ($E_1$) predicted by the DFTB-approximate methods with predictions of less approximate methods from the reference ML-dataset. For the nearly 21,800 organic molecules in the GDB-8 chemical space, we were able to identify clear trends in the $E_1$ prediction error distributions, with respect to second-order approximate coupled cluster (CC2), showing a strong dependence on chemical identity. Benchmark (surveying) ; Excited state ; Coupled cluster ; Computer science ; Singlet state ; Tight binding ; Statistical physics ; Formalism (music) ; Benchmarking ; Suite ; Density functional theory ; Algorithm ; Physics ; Molecule ; Chemistry ; Computational chemistry ; Quantum mechanics ; Electronic structure Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Mohamed Amine Benaimeche
Source: theses.fr (ABES) (preprint, 2022-01-28)
Field: Materials Science — Materials Chemistry

Modélisation et simulation des effets mécaniques d’expansions internes dues à la corrosion d’inclusions métalliques dans des matrices cimentaires This thesis deals with the disposal of low-level, long-lived waste from ORANO, composed mainly of graphite, magnesium and uranium residues. The waste is immobilized in a metal container by a matrix based on Alcali-Activated slag cement. The presence of free water in the cement pores can accelerate the corrosion reactions of the metallic particles, such as magnesium, and uranium. The expansion due to the formation of corrosion products might cause microcracks in the cement matrix. The cemented waste package is characterized by a strong material heterogeneity at different scales, and its mechanical behavior is impacted by the existence of potential cracks at the microscopic scale. The EF² method has been introduced as a general multiscale method to solve problems of nonlinear heterogeneous structures. At the macroscopic scale, each integration point of the finite element mesh is associated with a representative volume element (RVE). The major drawback of this method is the prohibitive computational cost, since it is required, for each computational step, to solve a nonlinear problem over RVE at each integration point at the macroscopic scale. A new machine-learning based multiscale method, called k-means FE², is developed to solve general nonlinear multiscale problems with internal variables and loading history dependent behavior. The macroscale problem is reduced by constructing clusters of Gauss points in a structure that are estimated to be in the same mechanical state. A machine-learning k-means clustering algorithm is used to select Gauss points based on their strain state. Then, for all Gauss points in a cluster, a single nonlinear microscopic problem is solved, and its answer is transferred to all integration points in the cluster in terms of mechanical properties. The convergence of the k-means FE² method must be verified with respect to the number of clusters in the macroscopic structure, and the EF² reference solution. The k-means FE² method is applied on a cemented waste problem considering microstructures with several volume fractions of graphite Non-metallic inclusions ; Corrosion ; Cementitious ; Materials science ; Metal ; Metallurgy ; Composite material ; Structural engineering ; Forensic engineering ; Engineering ; Cement Structural mechanics and materials ; Concrete and Cement Materials Research
Authors: Yuetian Jia | Zhixiang Fan | Chao Qian | Hongsheng Chen
Source:  (article, 2022-11-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Global reconstruction of form-free metasurfaces for customized demand via a learning paradigm Metasurfaces, as a class of two-dimensional artificially structured materials, have recently attracted extensive interest due to their rich optical properties and practically preferable natures, meriting a plethora of unprecedented applications. In all these metasurfaces-related works, a pivotal prerequisite is to obtain high-performance metastructures with the intended optical properties, which is conventionally solved by tedious and inefficient numerical simulations. To facilitate this, state-of-art works introduced deep learning algorithms to streamline the forward and inverse design of individual micro-structures. However, for complex metasurfaces and devices, it still has remained unexplored, with the substantial challenges from unveiling elusive light-metasurfaces interaction, algorithm modeling, and experiment demonstration. To address these, here we report a global and direct intelligent design method for complex metasurfaces and devices, via a deep learning architecture. Illustrative examples of conformal metasurfaces are presented, showing a high-fidelity inverse design from the far-field in the simulation and experiment. Computer science ; Deep learning ; Conformal map ; High fidelity ; Architecture ; Fidelity ; Computer architecture ; Inverse problem ; Inverse ; Electronic engineering ; Artificial intelligence ; Computer engineering ; Engineering ; Electrical engineering ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Animal Vocal Communication and Behavior