NANIWA series is a computational code for performing first principles quantum dynamics calculations. As the description implies, it is a quantum mechanical version of classical molecular dynamics (MD) calculations. A classical description of the system involved in, e.g., surface reactions (dissociative scattering, molecular scattering, dissociative adsorption, associative desorption, etc.) can be used, when quantum effects, such as tunneling, diffractions, and electronic excitations, play no essential role in the dynamics. In addition to this, the kinetic energy of, e.g., the impinging particle must be large enough, to ensure that the de Broglie wavelength is much smaller than the lattice constant of the solid (typically of the order of a few Angstroms), to be able to neglect interference phenomena. For hydrogen, with a translational energy of say 20 meV, the de Broglie wavelength is a few Angstroms. This dictates that we treat hydrogen as a quantum particle! For all the relevant surface reactions, there is a strong interaction between the impinging particle and the surface. This compounds the situation because interactions imply coupling between the internal degrees-of-freedom (e.g., vibration, rotation, and translation) of the particles immediately involved in the reaction. The vibrational motion, e.g., requires a quantum description, esp., when the respective quanta are large. Thus, the coupling between the internal degrees-of-freedom also requires a quantum mechanical description. As one would expect, the is computation code could also handle such problems as quantum transport, and quantum scattering in general.
For the first principles quantum dynamics calculation done by NANIWA series can be broken down into two main stages, viz.,
1) Determination of the effective potential energy (hyper-) surface
(PES) governing the reaction, based on the density functional
theory [1].
2) Solution of the corresponding multi-dimensional Schrodinger
equation for the reaction described by the above-determined
PES, based on the coupled-channel method [2,3] and the
concept of a local reflection matrix [4].
[1] P. Hohenberg, W. Kohn, Phys. Rev. 136 (1964) B864.
[2] W. Brenig, H. Kasai, Surf. Sci. 213 (1989) 170.
[3] H. Kasai, A. Okiji, Prog. Theor. Phys. Suppl. 106 (1991) 341.
[4] W. Brenig, T. Brunner, A. Gross, R. Russ, Z. Phys. B93 (1993) 91.
Source : http://www.dyn.ap.eng.osaka-u.ac.jp/web/naniwa_series.html
Tuesday, July 27, 2010
AkaiKKR (MACHIKANEYAMA)
AkaiKKR (MACHIKANEYAMA) is a software package used for first-principles calculation of the electronic structures of metals, semiconductors and compounds, within the framework of the local density approximation or generalized gradient approximation (LDA/GGA) of density functional theory.
The package, which features both high speed and high accuracy, uses the KKR–Green’s function method. This is an all-electron method and does not suffer from any serious truncation errors such as those associated with plane-wave cutoffs. Moreover, the CPA (coherent potential approximation) is integrated into the package making it applicable not only to crystals but also to disordered systems such as impurity systems, random substitutional alloys and mixed crystals. Since the Green’s function of the system is calculated, the package provides a good starting point for first-principles calculations of linear response theory, many-body effects, and so on.
The package has been in continuous development since the late 1970s and this development continues today. It is written in Fortran 77 and is completely self-contained (no additional libraries are required). It runs equally well on a notebook PC and a supercomputer. It can be used on any platform (UNIX, Linux, Mac OS, Windows etc.) where a Fortran compiler is installed. The memory required depends on the physical system to be calculated. For instance, a spin-polarized calculation of a system with a single atom per unit cell requires no more than a megabyte of memory. However, a larger system with, say, 20 atoms per unit cell, may require 1GB of memory.
Source : http://kkr.phys.sci.osaka-u.ac.jp/
The package, which features both high speed and high accuracy, uses the KKR–Green’s function method. This is an all-electron method and does not suffer from any serious truncation errors such as those associated with plane-wave cutoffs. Moreover, the CPA (coherent potential approximation) is integrated into the package making it applicable not only to crystals but also to disordered systems such as impurity systems, random substitutional alloys and mixed crystals. Since the Green’s function of the system is calculated, the package provides a good starting point for first-principles calculations of linear response theory, many-body effects, and so on.
The package has been in continuous development since the late 1970s and this development continues today. It is written in Fortran 77 and is completely self-contained (no additional libraries are required). It runs equally well on a notebook PC and a supercomputer. It can be used on any platform (UNIX, Linux, Mac OS, Windows etc.) where a Fortran compiler is installed. The memory required depends on the physical system to be calculated. For instance, a spin-polarized calculation of a system with a single atom per unit cell requires no more than a megabyte of memory. However, a larger system with, say, 20 atoms per unit cell, may require 1GB of memory.
Source : http://kkr.phys.sci.osaka-u.ac.jp/
Thursday, May 6, 2010
Asia Computational Materials Design and Quantum Engineering Workshop 2010
INTRODUCTION
We are pleased to inform you that the Computational Material Design and Quantum Engineering Laboratory, Research Group of Engineering Physics, Faculty of Industrial Technology, Institute of Technology Bandung (ITB), in cooperation with the Kasai Laboratory of the Osaka University will organize the 3rd
Computational materials design is a computational approach aimed at developing new materials with specified properties and functionalities. The basic ingredient is the use of quantum simulations to solve the material science problems in order to design a material that suits this specification. CMD has the high potentiality to impact the real industrial research and development.
Although the subject covered in this workshop is advance nevertheless we will present it to you step by step. The workshop will be started with the overview of possible roles of CMD in Indonesia, some CMD applications, CMD in surface interactions and nano-spintronics, and followed by the development of quantum simulators. We plan the following 3 hands-on experiences :
1.) First-principles molecular dynamics program code : STATE-Senri
(developed by Yoshitada MORIKAWA)
2.) First-principles calculation code by real-space formalism : RSPACE
(developed by Tomoya ONO)
(developed by Tomoya ONO)
3.) KKR-CPA-LDA electronic - structure - computation code :
Machikaneyama (developed by Hisazumi AKAI)
Machikaneyama (developed by Hisazumi AKAI)
We are looking forward to seeing you in the campus of Insitute of Technology Bandung. You will also find pleasant places to see and enjoy recreation around the city of Bandung.
DESCRIPTION
The workshop will provide hands-on experience of the quantum simulation. We chose to use an open source application so that the participants can easily develop for their own purposes without an extra spending on application softwares. Although there is no computational language knowledge requirements, understanding any of it, preferably Fortran or C and Java, will be useful. The lecture, practice, and tutorial will be given by the experts form the Osaka University, the University of Tokyo, and ITB.
OBJECTIVE
After completing this workshop, the participants should familiar with DFT based ab initio computation, development of quantum simulators and computational material design paradigm in general should be able to use modern tools : Machikaneyama, STATE-Senri and RSPACE codes, for quantum simulation and material design; should be able to conduct quantum molecular dynamics and atomic motion based simulations.
WHO SHOULD ATTEND THE WORKSHOP
Lecturers, graduate students as well as practitioners in physics, chemistry, engineering physics, electrical engineering, material science and engineering. Basically every one interested in the subject are welcome. However, we will strictly limit the number of participants to 45 and the decision will be only be based on the first come first served basis.
What would you get : course hands-out and certificate
Workshop duration : 4 full-days
Schedule : July 19-22, 2010
Venue : ITB Campus, Jl. Ganesha 10 Bandung 40132
Fees : Rp 300.000,-
Rp 250.000,- (if paid before July 1, 2010)
SPONSORS
This workshop is partially sponsored by Japan Society for the Promotion of Science (JSPS), Directorate General for Higher Education (DGHE - Dirjen Dikti), Osaka University and ITB.
FACILITIES
Air-conditoned computer laboratory with multimedia facilities, lunch, snacks and certificate. Participants are encouraged to bring their own laptop so that they can install all relevant softwares for their own purpose.
INSTRUCTORS/TUTORS
The lectures will be given by Prof. Hideaki Kasai, Prof. Hiroshi Katayama Yoshida, Prof. Hiroshi Nakanishi, Prof. Yoshitada Morikawa, Prof. Tomoya Ono, Prof. Masaaki Geshi, Kazunori Sato from Osaka University, Prof. Shinji Tsuneyuki from the University of Tokyo, and Hermawan K. Dipojono, Ph.D from ITB
FURTHER CONTACTS
Research Group of Engineering Physics,
Faculty of Industrial Technology,
Faculty of Industrial Technology,
Institute of Technology Bandung (ITB)
Phone/Facs. : 022-2504424 Ext. 213 / 022-2506281
Contact Person : linagani@tf.itb.ac.id or mazna@tf.itb.ac.id
Tuesday, May 4, 2010
Data Modeling
Data modeling in software engineering is the process of creating a data model by applying formal data model descriptions using data modeling techniques.
Data modeling is a method used to define and analyze data requirements needed to support the business processes of an organization. The data requirements are recorded as a conceptual data model with associated data definitions. Actual implementation of the conceptual model is called a logical data model. To implement one conceptual data model may require multiple logical data models. Data modeling defines not just data elements, but their structures and relationships between them Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, eg using data modeling
Data modeling may be performed during various types of projects and in multiple phases of projects. Data models are progressive; there is no such thing as the final data model for a business or application. Instead a data model should be considered a living document that will change in response to a changing business. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time. Whitten (2004) determined two types of data modeling
Data modeling is also a technique for detailing business requirements for a database. It is sometimes called database modeling because a data model is eventually implemented in a database.
Data modeling is a method used to define and analyze data requirements needed to support the business processes of an organization. The data requirements are recorded as a conceptual data model with associated data definitions. Actual implementation of the conceptual model is called a logical data model. To implement one conceptual data model may require multiple logical data models. Data modeling defines not just data elements, but their structures and relationships between them Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, eg using data modeling
Data modeling may be performed during various types of projects and in multiple phases of projects. Data models are progressive; there is no such thing as the final data model for a business or application. Instead a data model should be considered a living document that will change in response to a changing business. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time. Whitten (2004) determined two types of data modeling
Data modeling is also a technique for detailing business requirements for a database. It is sometimes called database modeling because a data model is eventually implemented in a database.
Materials Studio
Materials Studio is software for simulation and modelling of materials developed and distributed by Accelrys, a company specializing in research software for computational chemistry, bioinformatics, cheminformatics, molecular simulation, and quantum mechanics.
This software is used in advanced research of various materials--polymers, nanotubes, catalysts, metals, ceramics, and so on--by universities, research centers and hi-tech companies (e.g., in nanotechnology research by ST Microelectronics)
Materials Studio is a client–server software with Microsoft Windows-based PC clients and Windows and Linux-based servers running on PCs, LINUX IA64 Workstations (including SGI Altix) and HP XC clusters.
This software is used in advanced research of various materials--polymers, nanotubes, catalysts, metals, ceramics, and so on--by universities, research centers and hi-tech companies (e.g., in nanotechnology research by ST Microelectronics)
Materials Studio is a client–server software with Microsoft Windows-based PC clients and Windows and Linux-based servers running on PCs, LINUX IA64 Workstations (including SGI Altix) and HP XC clusters.
Johnson-Holmquist Damage Model
In solid mechanics, the Johnson–Holmquist damage model is used to model the mechanical behavior of damaged brittle materials, such as ceramics, rocks, and concrete, over a range of strain rates. Such materials usually have high compressive strength but low tensile strength and tend to exhibit progressive damage under load due to the growth of microcracks.
There are two variations of the Johnson-Holmquist model that are used to model the impact performance of ceramics under ballistically delivered loads. These models were developed by Gordon R. Johnson and Timothy J. Holmquist in the 1990s with the aim of facilitating predictive numerical simulations of ballistic armor penetration. The first version of the model is called the 1992 Johnson-Holmquist 1 (JH-1) model. This original version was developed to account for large deformations but did not take into consideration progressive damage with increasing deformation; though the multi-segment stress-strain curves in the model can be interpreted as incorporating damage implicitly. The second version, developed in 1994, incorporated a damage evolution rule and is called the Johnson-Holmquist 2 (JH-2) model or, more accurately, the Johnson-Holmquist damage material model.
The Johnson-Holmquist material model (JH-2), with damage, is useful when modeling brittle materials, such as ceramics, subjected to large pressures, shear strain and high strain rates. The model attempts to include the phenomena encountered when brittle materials are subjected to load and damage, and is one of the most widely used models when dealing with ballistic impact on ceramics. The model simulates the increase in strength shown by ceramics subjected to hydrostatic pressure as well as the reduction in strength shown by damaged ceramics. This is done by basing the model on two sets of curves that plot the yield stress against the pressure. The first set of curves accounts for the intact material, while the second one accounts for the failed material. Each curve set depends on the plastic strain and plastic strain rate. A damage variable D accounts for the level of fracture.
The JH-2 material assumes that the material is initially elastic and isotropic and can be described by a relation of the form (summation is implied over repeated indices)
There are two variations of the Johnson-Holmquist model that are used to model the impact performance of ceramics under ballistically delivered loads. These models were developed by Gordon R. Johnson and Timothy J. Holmquist in the 1990s with the aim of facilitating predictive numerical simulations of ballistic armor penetration. The first version of the model is called the 1992 Johnson-Holmquist 1 (JH-1) model. This original version was developed to account for large deformations but did not take into consideration progressive damage with increasing deformation; though the multi-segment stress-strain curves in the model can be interpreted as incorporating damage implicitly. The second version, developed in 1994, incorporated a damage evolution rule and is called the Johnson-Holmquist 2 (JH-2) model or, more accurately, the Johnson-Holmquist damage material model.
The Johnson-Holmquist material model (JH-2), with damage, is useful when modeling brittle materials, such as ceramics, subjected to large pressures, shear strain and high strain rates. The model attempts to include the phenomena encountered when brittle materials are subjected to load and damage, and is one of the most widely used models when dealing with ballistic impact on ceramics. The model simulates the increase in strength shown by ceramics subjected to hydrostatic pressure as well as the reduction in strength shown by damaged ceramics. This is done by basing the model on two sets of curves that plot the yield stress against the pressure. The first set of curves accounts for the intact material, while the second one accounts for the failed material. Each curve set depends on the plastic strain and plastic strain rate. A damage variable D accounts for the level of fracture.
The JH-2 material assumes that the material is initially elastic and isotropic and can be described by a relation of the form (summation is implied over repeated indices)
Materials Science
Materials science or materials engineering is an interdisciplinary field involving the properties of matter and its applications to various areas of science and engineering. This science investigates the relationship between the structure of materials at atomic or molecular scales and their macroscopic properties. It includes elements of applied physics and chemistry. With significant media attention focused on nanoscience and nanotechnology in recent years, materials science has been propelled to the forefront at many universities. It is also an important part of forensic engineering and failure analysis. Materials science also deals with fundamental properties and characteristics of materials.
The material of choice of a given era is often its defining point; the Stone Age, Bronze Age, and Steel Age are examples of this. Materials science is one of the oldest forms of engineering and applied science, deriving from the manufacture of ceramics. Modern materials science evolved directly from metallurgy, which itself evolved from mining. A major breakthrough in the understanding of materials occurred in the late 19th century, when the American scientist Josiah Willard Gibbs demonstrated that the thermodynamic properties related to atomic structure in various phases are related to the physical properties of a material. Important elements of modern materials science are a product of the space race the understanding and engineering of the metallic alloys, and silica and carbon materials, used in the construction of space vehicles enabling the exploration of space. Materials science has driven, and been driven by, the development of revolutionary technologies such as plastics, semiconductors, and biomaterials.
Before the 1960s (and in some cases decades after), many materials science departments were named metallurgy departments, from a 19th and early 20th century emphasis on metals. The field has since broadened to include every class of materials, including ceramics, polymers, semiconductors, magnetic materials, medical implant materials and biological materials (materiomics).
In materials science, rather than haphazardly looking for and discovering materials and exploiting their properties, the aim is instead to understand materials so that new materials with the desired properties can be created.
The material of choice of a given era is often its defining point; the Stone Age, Bronze Age, and Steel Age are examples of this. Materials science is one of the oldest forms of engineering and applied science, deriving from the manufacture of ceramics. Modern materials science evolved directly from metallurgy, which itself evolved from mining. A major breakthrough in the understanding of materials occurred in the late 19th century, when the American scientist Josiah Willard Gibbs demonstrated that the thermodynamic properties related to atomic structure in various phases are related to the physical properties of a material. Important elements of modern materials science are a product of the space race the understanding and engineering of the metallic alloys, and silica and carbon materials, used in the construction of space vehicles enabling the exploration of space. Materials science has driven, and been driven by, the development of revolutionary technologies such as plastics, semiconductors, and biomaterials.
Before the 1960s (and in some cases decades after), many materials science departments were named metallurgy departments, from a 19th and early 20th century emphasis on metals. The field has since broadened to include every class of materials, including ceramics, polymers, semiconductors, magnetic materials, medical implant materials and biological materials (materiomics).
In materials science, rather than haphazardly looking for and discovering materials and exploiting their properties, the aim is instead to understand materials so that new materials with the desired properties can be created.
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