The Experts below are selected from a list of 84 Experts worldwide ranked by ideXlab platform

Isao Tanaka - One of the best experts on this subject based on the ideXlab platform.

  • Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms
    Advanced Energy Materials, 2013
    Co-Authors: Koji Fujimura, Kazuki Shitara, Akihide Kuwabara, Craig A. J. Fisher, Hiroki Moriwake, Yukinori Koyama, Ippei Kishida, Atsuto Seko, Isao Tanaka
    Abstract:

    Concerted efforts continue to be made in the search for superior lithium-ion conducting solids to replace the highly reactive liquid electrolytes typically used in rechargeable batteries. LISICON-type Materials have been studied extensively over the last few decades, providing abundant experimental data, but to date no overall Design principle for achieving high conductivity has been forthcoming. In this communication we present results of systematic sets of fi rst-principles calculations based on the cluster expansion method, as well as fi rst-principles molecular dynamics (FPMD) simulations carried out to calculate Li-ion conductivities at high temperature, for a diverse range of compositions. A machine-learning technique is used to combine theoretical and experimental datasets to predict the conductivity of each composition at 373 K. The insights obtained show that an iterative combination of fi rst-principles calculations and focused experiments can greatly accelerate the Materials Design Process by enabling a wide compositional and structural phase space to be examined effi ciently. Lithium-conducting oxides in the system LiO 1/2 A O m /2 B O n /2 (where m and n denote the formal valences of cations A and B , respectively), known as LISICONs and corresponding to general formula Li 8 − c A a B b O 4 (where c = ma + n b ), [ 1 ] have been intensively studied since the 1970s. The original LISICON composition, Li 3.5 Zn 0.25 GeO 4 , was reported to exhibit an ionic conductivity of over 10 − 1 S cm − 1 at 673 K, [ 2 ] and stimulated a fl urry of new research. Although the conducting properties of many different LISICONs have since been reported by various groups, there are still many compositions that have yet to be

  • Accelerated Materials Design of lithium superionic conductors based on first-principles calculations and machine learning algorithms
    Advanced Energy Materials, 2013
    Co-Authors: Koji Fujimura, Kazuki Shitara, Akihide Kuwabara, Craig A. J. Fisher, Hiroki Moriwake, Yukinori Koyama, Ippei Kishida, Atsuto Seko, Isao Tanaka
    Abstract:

    A method for efficiently screening a wide compositional and structural phase space of LISICON-type superionic conductors is presented that utilizes a machine-learning technique for combining theoretical and experimental datasets. By iteratively performing systematic sets of first-principles calculations and focused experiments, it is shown how the Materials Design Process can be greatly accelerated, suggesting potentially superior candidate lithium superionic conductors.

Koji Fujimura - One of the best experts on this subject based on the ideXlab platform.

  • Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms
    Advanced Energy Materials, 2013
    Co-Authors: Koji Fujimura, Kazuki Shitara, Akihide Kuwabara, Craig A. J. Fisher, Hiroki Moriwake, Yukinori Koyama, Ippei Kishida, Atsuto Seko, Isao Tanaka
    Abstract:

    Concerted efforts continue to be made in the search for superior lithium-ion conducting solids to replace the highly reactive liquid electrolytes typically used in rechargeable batteries. LISICON-type Materials have been studied extensively over the last few decades, providing abundant experimental data, but to date no overall Design principle for achieving high conductivity has been forthcoming. In this communication we present results of systematic sets of fi rst-principles calculations based on the cluster expansion method, as well as fi rst-principles molecular dynamics (FPMD) simulations carried out to calculate Li-ion conductivities at high temperature, for a diverse range of compositions. A machine-learning technique is used to combine theoretical and experimental datasets to predict the conductivity of each composition at 373 K. The insights obtained show that an iterative combination of fi rst-principles calculations and focused experiments can greatly accelerate the Materials Design Process by enabling a wide compositional and structural phase space to be examined effi ciently. Lithium-conducting oxides in the system LiO 1/2 A O m /2 B O n /2 (where m and n denote the formal valences of cations A and B , respectively), known as LISICONs and corresponding to general formula Li 8 − c A a B b O 4 (where c = ma + n b ), [ 1 ] have been intensively studied since the 1970s. The original LISICON composition, Li 3.5 Zn 0.25 GeO 4 , was reported to exhibit an ionic conductivity of over 10 − 1 S cm − 1 at 673 K, [ 2 ] and stimulated a fl urry of new research. Although the conducting properties of many different LISICONs have since been reported by various groups, there are still many compositions that have yet to be

  • Accelerated Materials Design of lithium superionic conductors based on first-principles calculations and machine learning algorithms
    Advanced Energy Materials, 2013
    Co-Authors: Koji Fujimura, Kazuki Shitara, Akihide Kuwabara, Craig A. J. Fisher, Hiroki Moriwake, Yukinori Koyama, Ippei Kishida, Atsuto Seko, Isao Tanaka
    Abstract:

    A method for efficiently screening a wide compositional and structural phase space of LISICON-type superionic conductors is presented that utilizes a machine-learning technique for combining theoretical and experimental datasets. By iteratively performing systematic sets of first-principles calculations and focused experiments, it is shown how the Materials Design Process can be greatly accelerated, suggesting potentially superior candidate lithium superionic conductors.

Atsuto Seko - One of the best experts on this subject based on the ideXlab platform.

  • Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms
    Advanced Energy Materials, 2013
    Co-Authors: Koji Fujimura, Kazuki Shitara, Akihide Kuwabara, Craig A. J. Fisher, Hiroki Moriwake, Yukinori Koyama, Ippei Kishida, Atsuto Seko, Isao Tanaka
    Abstract:

    Concerted efforts continue to be made in the search for superior lithium-ion conducting solids to replace the highly reactive liquid electrolytes typically used in rechargeable batteries. LISICON-type Materials have been studied extensively over the last few decades, providing abundant experimental data, but to date no overall Design principle for achieving high conductivity has been forthcoming. In this communication we present results of systematic sets of fi rst-principles calculations based on the cluster expansion method, as well as fi rst-principles molecular dynamics (FPMD) simulations carried out to calculate Li-ion conductivities at high temperature, for a diverse range of compositions. A machine-learning technique is used to combine theoretical and experimental datasets to predict the conductivity of each composition at 373 K. The insights obtained show that an iterative combination of fi rst-principles calculations and focused experiments can greatly accelerate the Materials Design Process by enabling a wide compositional and structural phase space to be examined effi ciently. Lithium-conducting oxides in the system LiO 1/2 A O m /2 B O n /2 (where m and n denote the formal valences of cations A and B , respectively), known as LISICONs and corresponding to general formula Li 8 − c A a B b O 4 (where c = ma + n b ), [ 1 ] have been intensively studied since the 1970s. The original LISICON composition, Li 3.5 Zn 0.25 GeO 4 , was reported to exhibit an ionic conductivity of over 10 − 1 S cm − 1 at 673 K, [ 2 ] and stimulated a fl urry of new research. Although the conducting properties of many different LISICONs have since been reported by various groups, there are still many compositions that have yet to be

  • Accelerated Materials Design of lithium superionic conductors based on first-principles calculations and machine learning algorithms
    Advanced Energy Materials, 2013
    Co-Authors: Koji Fujimura, Kazuki Shitara, Akihide Kuwabara, Craig A. J. Fisher, Hiroki Moriwake, Yukinori Koyama, Ippei Kishida, Atsuto Seko, Isao Tanaka
    Abstract:

    A method for efficiently screening a wide compositional and structural phase space of LISICON-type superionic conductors is presented that utilizes a machine-learning technique for combining theoretical and experimental datasets. By iteratively performing systematic sets of first-principles calculations and focused experiments, it is shown how the Materials Design Process can be greatly accelerated, suggesting potentially superior candidate lithium superionic conductors.

Hiroki Moriwake - One of the best experts on this subject based on the ideXlab platform.

  • Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms
    Advanced Energy Materials, 2013
    Co-Authors: Koji Fujimura, Kazuki Shitara, Akihide Kuwabara, Craig A. J. Fisher, Hiroki Moriwake, Yukinori Koyama, Ippei Kishida, Atsuto Seko, Isao Tanaka
    Abstract:

    Concerted efforts continue to be made in the search for superior lithium-ion conducting solids to replace the highly reactive liquid electrolytes typically used in rechargeable batteries. LISICON-type Materials have been studied extensively over the last few decades, providing abundant experimental data, but to date no overall Design principle for achieving high conductivity has been forthcoming. In this communication we present results of systematic sets of fi rst-principles calculations based on the cluster expansion method, as well as fi rst-principles molecular dynamics (FPMD) simulations carried out to calculate Li-ion conductivities at high temperature, for a diverse range of compositions. A machine-learning technique is used to combine theoretical and experimental datasets to predict the conductivity of each composition at 373 K. The insights obtained show that an iterative combination of fi rst-principles calculations and focused experiments can greatly accelerate the Materials Design Process by enabling a wide compositional and structural phase space to be examined effi ciently. Lithium-conducting oxides in the system LiO 1/2 A O m /2 B O n /2 (where m and n denote the formal valences of cations A and B , respectively), known as LISICONs and corresponding to general formula Li 8 − c A a B b O 4 (where c = ma + n b ), [ 1 ] have been intensively studied since the 1970s. The original LISICON composition, Li 3.5 Zn 0.25 GeO 4 , was reported to exhibit an ionic conductivity of over 10 − 1 S cm − 1 at 673 K, [ 2 ] and stimulated a fl urry of new research. Although the conducting properties of many different LISICONs have since been reported by various groups, there are still many compositions that have yet to be

  • Accelerated Materials Design of lithium superionic conductors based on first-principles calculations and machine learning algorithms
    Advanced Energy Materials, 2013
    Co-Authors: Koji Fujimura, Kazuki Shitara, Akihide Kuwabara, Craig A. J. Fisher, Hiroki Moriwake, Yukinori Koyama, Ippei Kishida, Atsuto Seko, Isao Tanaka
    Abstract:

    A method for efficiently screening a wide compositional and structural phase space of LISICON-type superionic conductors is presented that utilizes a machine-learning technique for combining theoretical and experimental datasets. By iteratively performing systematic sets of first-principles calculations and focused experiments, it is shown how the Materials Design Process can be greatly accelerated, suggesting potentially superior candidate lithium superionic conductors.

Ippei Kishida - One of the best experts on this subject based on the ideXlab platform.

  • Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms
    Advanced Energy Materials, 2013
    Co-Authors: Koji Fujimura, Kazuki Shitara, Akihide Kuwabara, Craig A. J. Fisher, Hiroki Moriwake, Yukinori Koyama, Ippei Kishida, Atsuto Seko, Isao Tanaka
    Abstract:

    Concerted efforts continue to be made in the search for superior lithium-ion conducting solids to replace the highly reactive liquid electrolytes typically used in rechargeable batteries. LISICON-type Materials have been studied extensively over the last few decades, providing abundant experimental data, but to date no overall Design principle for achieving high conductivity has been forthcoming. In this communication we present results of systematic sets of fi rst-principles calculations based on the cluster expansion method, as well as fi rst-principles molecular dynamics (FPMD) simulations carried out to calculate Li-ion conductivities at high temperature, for a diverse range of compositions. A machine-learning technique is used to combine theoretical and experimental datasets to predict the conductivity of each composition at 373 K. The insights obtained show that an iterative combination of fi rst-principles calculations and focused experiments can greatly accelerate the Materials Design Process by enabling a wide compositional and structural phase space to be examined effi ciently. Lithium-conducting oxides in the system LiO 1/2 A O m /2 B O n /2 (where m and n denote the formal valences of cations A and B , respectively), known as LISICONs and corresponding to general formula Li 8 − c A a B b O 4 (where c = ma + n b ), [ 1 ] have been intensively studied since the 1970s. The original LISICON composition, Li 3.5 Zn 0.25 GeO 4 , was reported to exhibit an ionic conductivity of over 10 − 1 S cm − 1 at 673 K, [ 2 ] and stimulated a fl urry of new research. Although the conducting properties of many different LISICONs have since been reported by various groups, there are still many compositions that have yet to be

  • Accelerated Materials Design of lithium superionic conductors based on first-principles calculations and machine learning algorithms
    Advanced Energy Materials, 2013
    Co-Authors: Koji Fujimura, Kazuki Shitara, Akihide Kuwabara, Craig A. J. Fisher, Hiroki Moriwake, Yukinori Koyama, Ippei Kishida, Atsuto Seko, Isao Tanaka
    Abstract:

    A method for efficiently screening a wide compositional and structural phase space of LISICON-type superionic conductors is presented that utilizes a machine-learning technique for combining theoretical and experimental datasets. By iteratively performing systematic sets of first-principles calculations and focused experiments, it is shown how the Materials Design Process can be greatly accelerated, suggesting potentially superior candidate lithium superionic conductors.