The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
S V Starikov - One of the best experts on this subject based on the ideXlab platform.
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atomistic simulation of si au melt crystallization with novel Interatomic Potential
Computational Materials Science, 2018Co-Authors: S V Starikov, D E Smirnova, Yu N Lopanitsyna, S V MakarovAbstract:Abstract In this work we studied crystallization of the liquid Si-Au system at rapid cooling. For this purpose we performed atomistic simulation with novel Interatomic Potential. Results of the simulations showed that crystallization proceeds in different ways for pure silicon and Si-Au melt. For the studied binary system, the main factor limiting crystallization is diffusion of Au atoms in the liquid state. Threshold cooling rate for crystallization significantly depends on the Au content.
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an Interatomic Potential for simulation of zr nb system
Computational Materials Science, 2017Co-Authors: D E Smirnova, S V StarikovAbstract:Abstract We report a new attempt to study properties of Zr-Nb structural alloys. For this purpose we constructed an angular-dependent many-body Interatomic Potential. The Potential functions were fitted towards the ab initio data computed for a large set of reference structures. The fitting procedure is described, and its accuracy is discussed. We show that the structure and properties of all Nb and Zr phases existing in the Zr-Nb binary system are reproduced with good accuracy. The Interatomic Potential is appropriate for study of the high-pressure hexagonal ω -phase of Zr. We also estimated characteristics of the point defects in α -Zr, β -Zr and Nb; results are proven to correlate with the existing experimental and theoretical data. In case of α -Zr the model reveals anisotropy of the vacancy diffusion, in agreement with previous calculations and experiments. The Potential provides an opportunity for simulation of Zr-Nb alloys based on α -Zr and β -Zr. This conclusion is illustrated by the results obtained for the alloys with different niobium concentrations: up to 7% in case of hcp alloys and up to 50% for bcc alloys.
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a ternary eam Interatomic Potential for u mo alloys with xenon
Modelling and Simulation in Materials Science and Engineering, 2013Co-Authors: D E Smirnova, S V Starikov, V V Stegailov, Yu A Kuksin, Z Insepov, J RestAbstract:A new Interatomic Potential for a uranium–molybdenum system with xenon is developed in the framework of an embedded atom model using a force-matching technique and a dataset of ab initio atomic forces. The verification of the Potential proves that it is suitable for the investigation of various compounds existing in the system as well as for simulation of pure elements: U, Mo and Xe. Computed lattice constants, thermal expansion coefficients, elastic properties and melting temperatures of U, Mo and Xe are consistent with the experimentally measured values. The energies of the point defect formation in pure U and Mo are proved to be comparable to the density-functional theory calculations. We compare this new U–Mo–Xe Potential with the previously developed U and Mo–Xe Potentials. A comparative study between the different Potential functions is provided. The key purpose of the new model is to study the atomistic processes of defect evolution taking place in the U–Mo nuclear fuel. Here we use the Potential to simulate bcc alloys containing 10 wt% of intermetallic Mo and U2Mo.
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Interatomic Potential for uranium in a wide range of pressures and temperatures
Journal of Physics: Condensed Matter, 2012Co-Authors: D E Smirnova, S V Starikov, V V StegailovAbstract:Using the force-matching method we develop an Interatomic Potential that allows us to study the structure and properties of ?-U, ?-U and liquid uranium. The Potential is fitted to the forces, energies and stresses obtained from ab initio calculations. The model gives a good comparison with the experimental and ab initio data for the lattice constants of ?-U and ?-U, the elastic constants, the room-temperature isotherm, the normal density isochore, the bond-angle distribution functions and the vacancy formation energies. The calculated melting line of uranium at pressures up to 80?GPa and the temperature of the ??? transition at 3?GPa agree well with the experimental phase diagram of uranium.
D E Smirnova - One of the best experts on this subject based on the ideXlab platform.
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atomistic simulation of si au melt crystallization with novel Interatomic Potential
Computational Materials Science, 2018Co-Authors: S V Starikov, D E Smirnova, Yu N Lopanitsyna, S V MakarovAbstract:Abstract In this work we studied crystallization of the liquid Si-Au system at rapid cooling. For this purpose we performed atomistic simulation with novel Interatomic Potential. Results of the simulations showed that crystallization proceeds in different ways for pure silicon and Si-Au melt. For the studied binary system, the main factor limiting crystallization is diffusion of Au atoms in the liquid state. Threshold cooling rate for crystallization significantly depends on the Au content.
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an Interatomic Potential for simulation of zr nb system
Computational Materials Science, 2017Co-Authors: D E Smirnova, S V StarikovAbstract:Abstract We report a new attempt to study properties of Zr-Nb structural alloys. For this purpose we constructed an angular-dependent many-body Interatomic Potential. The Potential functions were fitted towards the ab initio data computed for a large set of reference structures. The fitting procedure is described, and its accuracy is discussed. We show that the structure and properties of all Nb and Zr phases existing in the Zr-Nb binary system are reproduced with good accuracy. The Interatomic Potential is appropriate for study of the high-pressure hexagonal ω -phase of Zr. We also estimated characteristics of the point defects in α -Zr, β -Zr and Nb; results are proven to correlate with the existing experimental and theoretical data. In case of α -Zr the model reveals anisotropy of the vacancy diffusion, in agreement with previous calculations and experiments. The Potential provides an opportunity for simulation of Zr-Nb alloys based on α -Zr and β -Zr. This conclusion is illustrated by the results obtained for the alloys with different niobium concentrations: up to 7% in case of hcp alloys and up to 50% for bcc alloys.
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a ternary eam Interatomic Potential for u mo alloys with xenon
Modelling and Simulation in Materials Science and Engineering, 2013Co-Authors: D E Smirnova, S V Starikov, V V Stegailov, Yu A Kuksin, Z Insepov, J RestAbstract:A new Interatomic Potential for a uranium–molybdenum system with xenon is developed in the framework of an embedded atom model using a force-matching technique and a dataset of ab initio atomic forces. The verification of the Potential proves that it is suitable for the investigation of various compounds existing in the system as well as for simulation of pure elements: U, Mo and Xe. Computed lattice constants, thermal expansion coefficients, elastic properties and melting temperatures of U, Mo and Xe are consistent with the experimentally measured values. The energies of the point defect formation in pure U and Mo are proved to be comparable to the density-functional theory calculations. We compare this new U–Mo–Xe Potential with the previously developed U and Mo–Xe Potentials. A comparative study between the different Potential functions is provided. The key purpose of the new model is to study the atomistic processes of defect evolution taking place in the U–Mo nuclear fuel. Here we use the Potential to simulate bcc alloys containing 10 wt% of intermetallic Mo and U2Mo.
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Interatomic Potential for uranium in a wide range of pressures and temperatures
Journal of Physics: Condensed Matter, 2012Co-Authors: D E Smirnova, S V Starikov, V V StegailovAbstract:Using the force-matching method we develop an Interatomic Potential that allows us to study the structure and properties of ?-U, ?-U and liquid uranium. The Potential is fitted to the forces, energies and stresses obtained from ab initio calculations. The model gives a good comparison with the experimental and ab initio data for the lattice constants of ?-U and ?-U, the elastic constants, the room-temperature isotherm, the normal density isochore, the bond-angle distribution functions and the vacancy formation energies. The calculated melting line of uranium at pressures up to 80?GPa and the temperature of the ??? transition at 3?GPa agree well with the experimental phase diagram of uranium.
Aidan P Thompson - One of the best experts on this subject based on the ideXlab platform.
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extending the accuracy of the snap Interatomic Potential form
Journal of Chemical Physics, 2018Co-Authors: Mitchell Wood, Aidan P ThompsonAbstract:The Spectral Neighbor Analysis Potential (SNAP) is a classical Interatomic Potential that expresses the energy of each atom as a linear function of selected bispectrum components of the neighbor atoms. An extension of the SNAP form is proposed that includes quadratic terms in the bispectrum components. The extension is shown to provide a large increase in accuracy relative to the linear form, while incurring only a modest increase in computational cost. The mathematical structure of the quadratic SNAP form is similar to the embedded atom method (EAM), with the SNAP bispectrum components serving as counterparts to the two-body density functions in EAM. The effectiveness of the new form is demonstrated using an extensive set of training data for tantalum structures. Similar to artificial neural network Potentials, the quadratic SNAP form requires substantially more training data in order to prevent overfitting. The quality of this new Potential form is measured through a robust cross-validation analysis.
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extending the accuracy of the snap Interatomic Potential form
arXiv: Materials Science, 2017Co-Authors: Mitchell Wood, Aidan P ThompsonAbstract:The Spectral Neighbor Analysis Potential (SNAP) is a classical Interatomic Potential that expresses the energy of each atom as a linear function of selected bispectrum components of the neighbor atoms. An extension of the SNAP form is proposed that includes quadratic terms in the bispectrum components. The extension is shown to provide a large increase in accuracy relative to the linear form, while incurring only a modest increase in computational cost. The mathematical structure of the quadratic SNAP form is similar to the embedded atom method (EAM), with the SNAP bispectrum components serving as counterparts to the two-body density functions in EAM. It is also argued that the quadratic SNAP form is a special case of an artificial neural network (ANN). The effectiveness of the new form is demonstrated using an extensive set of training data for tantalum structures. Similarly to ANN Potentials, the quadratic SNAP form requires substantially more training data in order to prevent overfitting, as measured by cross-validation analysis.
C. Z. Wang - One of the best experts on this subject based on the ideXlab platform.
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crystallization of the p3sn4 phase upon cooling p2sn5 liquid by molecular dynamics simulation using a machine learning Interatomic Potential
Journal of Physical Chemistry C, 2021Co-Authors: Tongqi Wen, Chao Zhang, Yang Sun, Haidi Wang, Feng Zhang, C. Z. WangAbstract:We performed molecular dynamics simulations to study the crystallization of the P3Sn4 phase from P2Sn5 liquid using a machine learning (ML) Interatomic Potential with desirable efficiency and accur...
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development of Interatomic Potential for al tb alloys using a deep neural network learning method
Physical Chemistry Chemical Physics, 2020Co-Authors: L. Tang, Matthew J. Kramer, Zejin Yang, Tongqi Wen, C. Z. WangAbstract:An Interatomic Potential for the Al–Tb alloy around the composition of Al90Tb10 is developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total Potential energies and forces on each atom obtained from ab initio molecular dynamics (AIMD) simulations are collected to train a DNN model to construct the Interatomic Potential for the Al–Tb alloy. We show that the obtained DNN model can well reproduce the energies and forces calculated by AIMD simulations. Molecular dynamics (MD) simulations using the DNN Interatomic Potential also accurately describe the structural properties of the Al90Tb10 liquid, such as partial pair correlation functions (PPCFs) and bond angle distributions, in comparison with the results from AIMD simulations. Furthermore, the developed DNN Interatomic Potential predicts the formation energies of the crystalline phases of the Al–Tb system with an accuracy comparable to ab initio calculations. The structure factors of the Al90Tb10 metallic liquid and glass obtained by MD simulations using the developed DNN Interatomic Potential are also in good agreement with the experimental X-ray diffraction data. The development of short-range order (SRO) in the Al90Tb10 liquid and the undercooled liquid is also analyzed and three dominant SROs, i.e., Al-centered distorted icosahedron (DISICO) and Tb-centered ‘3661’ and ‘15551’ clusters, respectively, are identified.
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Development of Interatomic Potential for Al-Tb Alloy by Deep Learning Method
arXiv: Materials Science, 2020Co-Authors: L. Tang, Z. J. Yang, T. Q. Wen, Matthew J. Kramer, C. Z. WangAbstract:An Interatomic Potential for Al-Tb alloy around the composition of Al90Tb10 was developed using the deep learning method with DeePMD-kit package. The atomic configurations and the corresponding total Potential energies and forces on each atom obtained in the ab initio molecular dynamics (AIMD) simulations are collected to train a deep neural network model to construct the Interatomic Potential for Al-Tb alloy. We show the obtained deep neural network model can well reproduce the energies and forces calculated by AIMD. MD simulations using the neural network Interatomic Potential also describe the structural and dynamical properties of Al90Tb10 liquid well, such as the partial pair correlation functions, the bond angle distribution, and the time dependence of mean-square displacement, in comparison with the results from AIMD. Furthermore, the developed neural network Interatomic Potential predicts the formation energies of crystalline phases of Al-Tb system with the same accuracy as ab initio calculations. The structure factor of Al90Tb10 metallic glass obtained by MD simulation using the developed neural network Interatomic Potential is also in good agreement with the experimental X-ray diffraction data.
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Development of Interatomic Potential for Al-Tb Alloy by Deep Neural Network Learning Method
arXiv: Materials Science, 2020Co-Authors: L. Tang, Z. J. Yang, T. Q. Wen, Matthew J. Kramer, C. Z. WangAbstract:An Interatomic Potential for Al-Tb alloy around the composition of Al90Tb10 was developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total Potential energies and forces on each atom obtained from ab initio molecular dynamics (AIMD) simulations are collected to train a DNN model to construct the Interatomic Potential for Al-Tb alloy. We show the obtained DNN model can well reproduce the energies and forces calculated by AIMD. Molecular dynamics (MD) simulations using the DNN Interatomic Potential also accurately describe the structural properties of Al90Tb10 liquid, such as the partial pair correlation functions (PPCFs) and the bond angle distributions, in comparison with the results from AIMD. Furthermore, the developed DNN Interatomic Potential predicts the formation energies of crystalline phases of Al-Tb system with the accuracy comparable to ab initio calculations. The structure factor of Al90Tb10 metallic glass obtained by MD simulation using the developed DNN Interatomic Potential is also in good agreement with the experimental X-ray diffraction data.
Gabor Csanyi - One of the best experts on this subject based on the ideXlab platform.
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machine learning a general purpose Interatomic Potential for silicon
Physical Review X, 2018Co-Authors: Albert P Bartok, James R Kermode, Noam Bernstein, Gabor CsanyiAbstract:The success of first-principles electronic-structure calculation for predictive modeling in chemistry, solid-state physics, and materials science is constrained by the limitations on simulated length scales and timescales due to the computational cost and its scaling. Techniques based on machine-learning ideas for interpolating the Born-Oppenheimer Potential energy surface without explicitly describing electrons have recently shown great promise, but accurately and efficiently fitting the physically relevant space of configurations remains a challenging goal. Here, we present a Gaussian approximation Potential for silicon that achieves this milestone, accurately reproducing density-functional-theory reference results for a wide range of observable properties, including crystal, liquid, and amorphous bulk phases, as well as point, line, and plane defects. We demonstrate that this new Potential enables calculations such as finite-temperature phase-boundary lines, self-diffusivity in the liquid, formation of the amorphous by slow quench, and dynamic brittle fracture, all of which are very expensive with a first-principles method. We show that the uncertainty quantification inherent to the Gaussian process regression framework gives a qualitative estimate of the Potential’s accuracy for a given atomic configuration. The success of this model shows that it is indeed possible to create a useful machine-learning-based Interatomic Potential that comprehensively describes a material on the atomic scale and serves as a template for the development of such models in the future.
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machine learning a general purpose Interatomic Potential for silicon
arXiv: Materials Science, 2018Co-Authors: Albert P Bartok, James R Kermode, Noam Bernstein, Gabor CsanyiAbstract:The success of first principles electronic structure calculation for predictive modeling in chemistry, solid state physics, and materials science is constrained by the limitations on simulated length and time scales due to computational cost and its scaling. Techniques based on machine learning ideas for interpolating the Born-Oppenheimer Potential energy surface without explicitly describing electrons have recently shown great promise, but accurately and efficiently fitting the physically relevant space of configurations has remained a challenging goal. Here we present a Gaussian Approximation Potential for silicon that achieves this milestone, accurately reproducing density functional theory reference results for a wide range of observable properties, including crystal, liquid, and amorphous bulk phases, as well as point, line, and plane defects. We demonstrate that this new Potential enables calculations that would be extremely expensive with a first principles electronic structure method, such as finite temperature phase boundary lines, self-diffusivity in the liquid, formation of the amorphous by slow quench, and dynamic brittle fracture. We show that the uncertainty quantification inherent to the Gaussian process regression framework gives a qualitative estimate of the Potential's accuracy for a given atomic configuration. The success of this model shows that it is indeed possible to create a useful machine-learning-based Interatomic Potential that comprehensively describes a material, and serves as a template for the development of such models in the future.
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machine learning based Interatomic Potential for amorphous carbon
Physical Review B, 2017Co-Authors: Volker L Deringer, Gabor CsanyiAbstract:We introduce a Gaussian approximation Potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine learning representation of the density-functional theory (DFT) Potential-energy surface, such Interatomic Potentials enable materials simulations with close-to DFT accuracy but at much lower computational cost. We first determine the maximum accuracy that any finite-range Potential can achieve in carbon structures; then, using a hierarchical set of two-, three-, and many-body structural descriptors, we construct a GAP model that can indeed reach the target accuracy. The Potential yields accurate energetic and structural properties over a wide range of densities; it also correctly captures the structure of the liquid phases, at variance with a state-of-the-art empirical Potential. Exemplary applications of the GAP model to surfaces of ``diamondlike'' tetrahedral amorphous carbon ($\mathit{ta}$-C) are presented, including an estimate of the amorphous material's surface energy and simulations of high-temperature surface reconstructions (``graphitization''). The presented Interatomic Potential appears to be promising for realistic and accurate simulations of nanoscale amorphous carbon structures.