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Y Mishin - One of the best experts on this subject based on the ideXlab platform.

  • physically informed artificial neural networks for Atomistic Modeling of materials
    Nature Communications, 2019
    Co-Authors: G Purja P Pun, Rohit Batra, Rampi Ramprasad, Y Mishin
    Abstract:

    Large-scale Atomistic computer simulations of materials heavily rely on interatomic potentials predicting the energy and Newtonian forces on atoms. Traditional interatomic potentials are based on physical intuition but contain few adjustable parameters and are usually not accurate. The emerging machine-learning (ML) potentials achieve highly accurate interpolation within a large DFT database but, being purely mathematical constructions, suffer from poor transferability to unknown structures. We propose a new approach that can drastically improve the transferability of ML potentials by informing them of the physical nature of interatomic bonding. This is achieved by combining a rather general physics-based model (analytical bond-order potential) with a neural-network regression. This approach, called the physically informed neural network (PINN) potential, is demonstrated by developing a general-purpose PINN potential for Al. We suggest that the development of physics-based ML potentials is the most effective way forward in the field of Atomistic simulations. Traditional machine learning potentials suffer from poor transferability to unknown structures. Here the authors present an approach to improve the transferability of machine-learning potentials by including information on the physical nature of interatomic bonding.

  • physically informed artificial neural networks for Atomistic Modeling of materials
    arXiv: Materials Science, 2018
    Co-Authors: G Purja P Pun, Rohit Batra, Rampi Ramprasad, Y Mishin
    Abstract:

    Large-scale Atomistic computer simulations of materials heavily rely on interatomic potentials predicting the potential energy and Newtonian forces on atoms. Traditional interatomic potentials are based on physical intuition but contain few adjustable parameters and are usually not accurate. The emerging machine-learning (ML) potentials achieve highly accurate interpolation between the energies in a large DFT database but, being purely mathematical constructions, suffer from poor transferability to unknown structures. We propose a new approach that can drastically improve the transferability of ML potentials by informing them of the physical nature of interatomic bonding. This is achieved by combining a rather general physics-based model (analytical bond-order potential) with a neural-network regression. The network adjusts the parameters of the physics-based model on the fly during the simulations according to the local environments of individual atoms. This approach, called the physically-informed neural network (PINN) potential, is demonstrated by developing a general-purpose PINN potential for Al. The potential provides a DFT-level accuracy of energy predictions and excellent agreement with experimental and DFT data for a wide range of physical properties. We suggest that the development of physics-based ML potentials is the most effective way forward in the field of Atomistic simulations.

  • phase transformations at interfaces observations from Atomistic Modeling
    Current Opinion in Solid State & Materials Science, 2016
    Co-Authors: Timofey Frolov, Mark Asta, Y Mishin
    Abstract:

    Abstract We review the recent progress in theoretical understanding and Atomistic computer simulations of phase transformations in materials interfaces, focusing on grain boundaries (GBs) in metallic systems. Recently developed simulation approaches enable the search and structural characterization of GB phases in single-component metals and binary alloys, calculation of thermodynamic properties of individual GB phases, and Modeling of the effect of the GB phase transformations on GB kinetics. Atomistic simulations demonstrate that the GB transformations can be induced by varying the temperature, loading the GB with point defects, or varying the amount of solute segregation. The atomic-level understanding obtained from such simulations can provide input for further development of thermodynamics theories and continuous models of interface phase transformations while simultaneously serving as a testing ground for validation of theories and models. They can also help interpret and guide experimental work in this field.

  • Atomistic Modeling of interfaces and their impact on microstructure and properties
    Acta Materialia, 2010
    Co-Authors: Y Mishin, Mark Asta
    Abstract:

    Atomic-level Modeling of materials provides fundamental insights into phase stability, structure and properties of crystalline defects, and to physical mechanisms of many processes ranging from atomic diffusion to interface migration. This knowledge often serves as a guide for the development of mesoscopic and macroscopic continuum models, with input parameters provided by Atomistic models. This paper gives an overview of the most recent developments in the area of Atomistic Modeling with emphasis on interfaces and their impact on microstructure and properties of materials. Modern computer simulation methodologies are discussed and illustrated by several applications related to thermodynamic, kinetic and mechanical properties of materials. Existing challenges and future research directions in this field are outlined.

  • Atomistic Modeling of point defects and diffusion in copper grain boundaries
    Interface Science, 2003
    Co-Authors: A Suzuki, Y Mishin
    Abstract:

    The atomic structure of several symmetrical tilt grain boundaries (GBs) in Cu and their interaction with vacancies and interstitials as well as self-diffusion are studied by molecular statics, molecular dynamics, kinetic Monte Carlo (KMC), and other Atomistic simulation methods. Point defect formation energy in the GBs is on average lower than in the lattice but variations from site to site within the GB core are very significant. The formation energies of vacancies and interstitials are close to one another, which makes the defects equally important for GB diffusion. Vacancies show interesting effects such as delocalization and instability at certain GB sites. They move in GBs by simple vacancy-atom exchanges or by “long jumps” involving several atoms. Interstitial atoms can occupy relatively open positions between atoms, form split dumbbell configurations, or form highly delocalized displacement zones. They diffuse by direct jumps or by the indirect mechanism involving a collective displacement of several atoms. Diffusion coefficients in the GBs have been calculated by KMC simulations using defect jump rates determined within the transition state theory. GB diffusion can be dominated by vacancies or interstitials, depending on the GB structure. The diffusion anisotropy also depends on the GB structure, with diffusion along the tilt axis being either faster or slower than diffusion normal to the tilt axis. In agreement with Borisov's correlation, the activation energy of GB diffusion tends to decrease with the GB energy.

Hakim Iddir - One of the best experts on this subject based on the ideXlab platform.

  • graphite lithiation under fast charging conditions Atomistic Modeling insights
    Journal of Physical Chemistry C, 2020
    Co-Authors: Juan C. Garcia, Ira Bloom, Dennis W Dees, Christopher S. Johnson, Hakim Iddir
    Abstract:

    Charging lithium ion batteries in a fast and safe manner is critical for promoting mass adoption of electric vehicles. Li intercalation in graphite electrodes is known to be one of the bottlenecks ...

  • graphite lithiation under fast charging conditions Atomistic Modeling insights
    The Journal of Physical Chemistry, 2020
    Co-Authors: Juan C. Garcia, Ira Bloom, Dennis W Dees, Christopher S. Johnson, Hakim Iddir
    Abstract:

    The charging of lithium ion batteries in a fast and safe manner is critical for promoting the mass adoption of electric vehicles. Li intercalation in graphite electrodes is known to be one of the bottlenecks during the fast charging process. The mechanism of Li diffusion in highly polarized graphite anode at high current rates remains, however, not well understood. Herein, Density Functional Theory (DFT) calculations are used to gain insights into the Li diffusion process in graphite when it is far from equilibrium under fast charging conditions. The effect of uncompensated charges on Li mobility is determined in the highly polarized regions of the anode close to the interfaces. The extra charge was found to increase the interlayer spacing in the diffusion layer and adjacent channels, increasing the diffusivity and promoting the formation of Li clusters. A concerted diffusion mechanism at the edge of high-concentration Li domains is proposed to enhance the diffusion of Li.

Juan C. Garcia - One of the best experts on this subject based on the ideXlab platform.

  • graphite lithiation under fast charging conditions Atomistic Modeling insights
    Journal of Physical Chemistry C, 2020
    Co-Authors: Juan C. Garcia, Ira Bloom, Dennis W Dees, Christopher S. Johnson, Hakim Iddir
    Abstract:

    Charging lithium ion batteries in a fast and safe manner is critical for promoting mass adoption of electric vehicles. Li intercalation in graphite electrodes is known to be one of the bottlenecks ...

  • graphite lithiation under fast charging conditions Atomistic Modeling insights
    The Journal of Physical Chemistry, 2020
    Co-Authors: Juan C. Garcia, Ira Bloom, Dennis W Dees, Christopher S. Johnson, Hakim Iddir
    Abstract:

    The charging of lithium ion batteries in a fast and safe manner is critical for promoting the mass adoption of electric vehicles. Li intercalation in graphite electrodes is known to be one of the bottlenecks during the fast charging process. The mechanism of Li diffusion in highly polarized graphite anode at high current rates remains, however, not well understood. Herein, Density Functional Theory (DFT) calculations are used to gain insights into the Li diffusion process in graphite when it is far from equilibrium under fast charging conditions. The effect of uncompensated charges on Li mobility is determined in the highly polarized regions of the anode close to the interfaces. The extra charge was found to increase the interlayer spacing in the diffusion layer and adjacent channels, increasing the diffusivity and promoting the formation of Li clusters. A concerted diffusion mechanism at the edge of high-concentration Li domains is proposed to enhance the diffusion of Li.

Dennis W Dees - One of the best experts on this subject based on the ideXlab platform.

  • graphite lithiation under fast charging conditions Atomistic Modeling insights
    Journal of Physical Chemistry C, 2020
    Co-Authors: Juan C. Garcia, Ira Bloom, Dennis W Dees, Christopher S. Johnson, Hakim Iddir
    Abstract:

    Charging lithium ion batteries in a fast and safe manner is critical for promoting mass adoption of electric vehicles. Li intercalation in graphite electrodes is known to be one of the bottlenecks ...

  • graphite lithiation under fast charging conditions Atomistic Modeling insights
    The Journal of Physical Chemistry, 2020
    Co-Authors: Juan C. Garcia, Ira Bloom, Dennis W Dees, Christopher S. Johnson, Hakim Iddir
    Abstract:

    The charging of lithium ion batteries in a fast and safe manner is critical for promoting the mass adoption of electric vehicles. Li intercalation in graphite electrodes is known to be one of the bottlenecks during the fast charging process. The mechanism of Li diffusion in highly polarized graphite anode at high current rates remains, however, not well understood. Herein, Density Functional Theory (DFT) calculations are used to gain insights into the Li diffusion process in graphite when it is far from equilibrium under fast charging conditions. The effect of uncompensated charges on Li mobility is determined in the highly polarized regions of the anode close to the interfaces. The extra charge was found to increase the interlayer spacing in the diffusion layer and adjacent channels, increasing the diffusivity and promoting the formation of Li clusters. A concerted diffusion mechanism at the edge of high-concentration Li domains is proposed to enhance the diffusion of Li.

Ira Bloom - One of the best experts on this subject based on the ideXlab platform.

  • graphite lithiation under fast charging conditions Atomistic Modeling insights
    Journal of Physical Chemistry C, 2020
    Co-Authors: Juan C. Garcia, Ira Bloom, Dennis W Dees, Christopher S. Johnson, Hakim Iddir
    Abstract:

    Charging lithium ion batteries in a fast and safe manner is critical for promoting mass adoption of electric vehicles. Li intercalation in graphite electrodes is known to be one of the bottlenecks ...

  • graphite lithiation under fast charging conditions Atomistic Modeling insights
    The Journal of Physical Chemistry, 2020
    Co-Authors: Juan C. Garcia, Ira Bloom, Dennis W Dees, Christopher S. Johnson, Hakim Iddir
    Abstract:

    The charging of lithium ion batteries in a fast and safe manner is critical for promoting the mass adoption of electric vehicles. Li intercalation in graphite electrodes is known to be one of the bottlenecks during the fast charging process. The mechanism of Li diffusion in highly polarized graphite anode at high current rates remains, however, not well understood. Herein, Density Functional Theory (DFT) calculations are used to gain insights into the Li diffusion process in graphite when it is far from equilibrium under fast charging conditions. The effect of uncompensated charges on Li mobility is determined in the highly polarized regions of the anode close to the interfaces. The extra charge was found to increase the interlayer spacing in the diffusion layer and adjacent channels, increasing the diffusivity and promoting the formation of Li clusters. A concerted diffusion mechanism at the edge of high-concentration Li domains is proposed to enhance the diffusion of Li.