Transcript physical properties

Materials Genome Project
For a fisherman, efficient data
mining means deducing where
he has the highest probability of
finding fish, but does not
guarantee that he will catch
one.
H. Aourag
Tlemcen University, Algeria
Differences-Mass Production of Data
。。。。
Differences-Society
• We should not be divided into developed
countries and developing countries, but we
are developing our common futures together with
the gifts from friends, i.e., data & knowledge.
• We need to work together.
Essential changes are
• Actors and actresses in S/T are not only
experts but people in general in the information
era.
• Techno-democracy by IT may emerge as a
new relation between people and S & T and
experts are the people who can show
exemplars for the people and help people to
do it by themselves.
FAIR COMPETITION!
We have been emerging with mistakes and successes, and we
need to make our experiences into public goods. It starts from our
collaboration fighting against public bads!
Floods
August 15, 2002 Oregon (AP)
August 10, 2002
August 11, 2002
Starvation
Starvation
Water Shortage
Desertification and Refugee
Malaria and Climate Change
Refugee
The Sumatran earthquakes
of 2004 and 2005:
What’s next? What can be done?
Lessons from Failures
We are repeating mistakes.
Is it our intrinsic feature?
To be brave enough for making
challenges (=mistakes and/or
challenges) .
Successes by total quality control
let people stop thinking together
and differently.
How to go beyond a domain differentiated discipline
ensuring universal access to scientific knowledge ?
Eradicate extreme poverty and hunger
Reduce by half the proportion of people living on less than a dollar a day
Reduce by half the proportion of people who suffer from hunger
Achieve universal primary education
Ensure that all boys and girls complete a full course of primary schooling
Promote gender equality and empower women
Eliminate gender disparity in primary and secondary education preferably by 2005, and at all levels by 2015
Reduce child mortality
Reduce by two thirds the mortality rate among children under five
Improve maternal health
Reduce by three quarters the maternal mortality ratio
Combat HIV/AIDS, malaria and other diseases
Halt and begin to reverse the spread of HIV/AIDS
Halt and begin to reverse the incidence of malaria and other major diseases
Ensure environmental sustainability
Integrate the principles of sustainable development into country policies and programmes; reverse loss of environmental resources
Reduce by half the proportion of people without sustainable access to safe drinking water
Achieve significant improvement in lives of at least 100 million slum dwellers, by 2020
Develop a global partnership for development
Develop further an open trading and financial system that is rule-based, predictable and non-discriminatory. Includes a commitment to good governance,
development and poverty reduction—nationally and internationally Address the least developed countries’ special needs. This includes tariff- and quotafree access for their exports; enhanced debt relief for heavily indebted poor countries; cancellation of official bilateral debt; and more generous official
development assistance for countries committed to poverty reduction
Address the special needs of landlocked and small island developing States
Deal comprehensively with developing countries’ debt problems through national and international measures to make debt sustainable in the long term
In cooperation with the developing countries, develop decent and productive work for youth
In cooperation with pharmaceutical companies, provide
access to affordable essential drugs in developing countries In cooperation with the private sector, make available the benefits of new technologies—
especially information and communications technologies
Data Activities in General
• Databases everywhere, but not well
organized.
– Many databases, but too many
duplications
– Less interoperability
• Necessity to make practically useful interface
– Piecewise
• How to integrate for ad hoc application
– Positive incentives to go beyond
“collection”
Working Hypothesis
• Data Science
– Friendly interface for many sciences!
• Design Science
– Value extraction/design/creation from data
• Management Science
– Knowledge(Physics, Chemistry, Mathematics,
Technology)
– Environment(Nature, Artifact, Human beings)
– Society(Politics, Economy, Sociology)
Components : Mind Sets in E-Science
Data Science
• Universality
– Data for everyone
• Sharing, standards, metadata, interoperability,
….
– Data of no one
• Equitable, universal, open, …access
• Individual Care-establishing service
channels
– Data services for each person and each
context with appropriate expression, timing
and contents.
What are our objectives?
The improvement of the quality and accessibility of data, as
well as the methods by which data are acquired, managed,
analyzed and evaluated, with particular emphasis on digital
divide.
The facilitation of national and international co-operation
among those collecting, organizing and using data.
The promotion of an increased awareness in the scientific and
technical community of the importance of these activities.
The consideration of data access and intellectual property
issues.
Let’s work together from
now!
Data Science is not pursued as an end in itself,
but as a means to the attainment of wisdom as
human.
Diagram illustrating how, in particular, information and knowledge derive from
raw data through the understanding of relationships and then patterns.
The concepts of preservation, curation, provenance, discovery,
access in the context of the research lifecycle.
Human-beings : Human Genome
Our Genome
AMASS – 7/25/03
So Why Designing Materials
Systems
Experimentaly Percent
Known
Known
Maximum
Number
Unaries
100
100%
100
Binaries
4000
81%
4950
Ternaries
8000
5%
161700
Quaternaries
1000
<1%
3921225
Combinatorial Materials Techniques
H.Aourag
29
Methodology
• Computational and database software tools
should be configured in a manner that
maximally exploits the synergy between
them
Problem Solving/ Analysis
Crystal structure,
property, phase data,
both experimental
and calculated
data
Theory
Ab initio quatum
mechanical
methods
Correlations
Statistics, rules, regularities,
data patterns, structure
H.Aourag
32
One of the most challenging tasks in materials science is the design of new
materials with tailored properties. Two different approaches are generally
explored:
► The first one consists of simulating the motion of the atoms in the material
and their electronic interactions by performing ab-initio calculations at the
quantum-mechanical level. This approach does (at least in principle) not rely on
experiments, but is computationally demanding and can currently only be
applied to a limited number of rather simple solids.
► The second approach remains at a more pragmatic level: Most of our current
knowledge in materials science has been collected empirically, by searching for
patterns in experimental observations. During the past 100 years, huge amounts
of data have been collected making it possible to use modern computer
technology to search for additional correlations. This approach, however,
depends on the availability of a sufficiently large amount of experimental data
of appropriate quality.
Materials Design
Too many
Possibilities…?
Materials
Unary
Binary
Ternary
Quaternary
…
Multinary
Periodic Table of the Elements
Design
Prediction
(Production Line)
Regularities ?
Needs
Functions
Specification
design
Properties
Function
design
Atomic
Constituents
Structures
Structure
design
Materials Design
(Resolution Line)
Process
design
II. Approaches: Data-Driven Approach
Basic Idea
 Based on the comprehensive materials database
to reveal regularities:
- Formation of compound in a given binary system
- Composition of stable compounds in “compound
formers”
- Structures of a given compound
 Postulation
- Properties of a given compound
Elemental Property
Parameters (EPPs)
Expression
Property of
Materials
 Tool: Materials Databases: Pauling File
phase diagrams + crystal structures + physical properties
together in the world largest database for inorganic compounds
Data-Driven Approach
Mapping
 Purpose of Mapping
Proper Elemental Properties as Axes

Substances in same/similar
structure/properties  Groups
 Two key points in mapping
Characterization: To find optimal coordinates
Classification:
To define meaning of domains
Modeling-Driven Approach
Calculations based on various
physical models provide:
 Complement to empirical data, provide new data;
 Further screening and prediction of hypothesis;
 Understanding of insight into the origin;
 Prediction of materials with required properties.
Modeling-Driven Approach
Theoretical Approaches
• First Principles Electronic Structures (FLAPW, Wien)
• Car-Parrinello Molecular Dynamics (CPMD, VASP)
• Cluster Expansion Method (CEM)
• Cluster Variation Method (CVM)
• Phase Field Method (PPM)
• Classical Molecular Dynamics (MD)
• ……
Data/Modeling-Driven Approach
Materials
Too many
Possibilities…?
Unary
Binary
Ternary
Quaternary
…
Multinary
Periodic Table of the Elements
Design
Regularities
Density vs. Melting point
Each property clusterstructures
Model-Driven Approach  Origin
Data-Drive Approach  Discovery
Structural Map
Purpose: Regularity between Crystal structure & Element properties
Optimal coordinates
56
Element Property
Parameters
?
Definition of domains
~3,500
Conventional
Structures Types
 6 most distinct EPP groups
Atomic number
Group number
Mendeleev number
Cohesion energy
Electrochemical factor
Size
2-3
Optimal EPP
Expressions
!
Distribution,
Patterns, …… ☺
 Operations
Decreasing
possibilities
Sum
EP(A)+EP(B)
Difference EP(A)-EP(B)
Product
EP(A)*EP(B)
RatiostructureEP(A)/EP(B)
Conventional
types
Maximum Max(EP(A),EP(B))
Minimum structure
Min(EP(A),EP(B))
Conventional
types
 EPP
EP(tot) = EP(A) op EP(B)
Max Gap
~30
Atomic Environment
Types
Compound Formation Map
Separation of 2,330 binary
systems
into
compound
formers (blue) and nonformers (yellow) in a
compound formation map
showing max[PN(A) / PNmax,
PN(B) / PNmax] (y-axis) vs.
[PN(A) / PNmax × PN(B) /
PNmax] (x-axis), where PN is
the Periodic Number (a
distinct integer assigned to
each chemical element based
on its position in Mendeleev's
periodic system)
atomic environment type stability map for AB compounds
Atomic
environment
type (AET) stability map
showing the Periodic
Number PNmax (y-axis)
vs. PNmin / PNmax (x-axis)
for
equiatomic
AB
compounds [4] . AET of
the element with the
highest Periodic Number
is given on the left-hand
side of x = 1, AET of the
element with the lowest
Periodic Number in the
same compound on the
right-hand side in the
same row.
generalized atomic environment type matrix
AET
matrix PN(A) vs.
PN(B), which is
independent of the
stoichiometry and the
number of chemical
elements
in
the
compound [5]. The
element A occupying
the center of the AET
is given on the y-axis
and the coordinating
element B on the xaxis.
Generalized
Phase Diagrams
distribution according to publication year
38'592 database entries processed 06.2012
binary systems 11'027
ternary systems 27'565
distribution according to chemical class
crystal structures
● journals :81'290
publications processed 09.2012
distribution according to journal
Acta Crystallographica
Journal of Alloys and Compounds
Journal of Solid State Chemistry
Zeitschrift für Anorganische und
Allgemeine Chemie
Inorganic Materials
Russian Journal of Inorganic
Chemistry
Inorganic Chemistry
Physical Review B
Zeitschrift für Kristallographie
C.R. des Seances de l'Academie des
Sciences
Materials Research Bulletin
American Mineralogist
others
distribution according to publication year
252'599 database entries processed 09.2012
1 or 2 elements
52'769
3 elements
99'438
4 or more elements
100'392
distribution according to chemical class
physical properties
33'458 publications processed 06.2012
Distribution according to journal
● journals
Physical Review B
Journal of Alloys and Compounds
Solid State Communications
Physica B+C
Journal of Magnetism and
Magnetic Materials
Journal of Solid State Chemistry
Journal of Physics: Condensed
Matter
Physica Status Solidi A
Journal of the Physical Society of
Japan
Physical Review Letters
Materials Research Bulletin
Journal of Applied Physics
others
distribution according to publication year
91'134 database entries processed 06.2012
1 or 2 elements
34'889
3 elements
30'627
4 or more elements
25'618
distribution according to chemical class
● property class
1 mechanical properties
2 thermal and thermodynamic
properties
3 electronic and electrical properties
4 optical properties
5 ferroelectric properties
6 magnetic properties
7 superconductor properties
● data
distribution according to property class
category
from bottom to top:
- numerical values
- figure descriptions
- additional data
Predicting Properties with Atomistic Modeling
Atomistic modeling
• Atom positions
• Electronic structure
• Energies
Band Gap
Elastic Constants
?
Macroscopic properties
• Elastic properties
• Conductivity
• Toxicity
Direct
calculation
Band Gap
Elastic Constants
Segregation Energies
Physical laws
Activation Barriers
Constitutive relations
Embrittlement
Transport
Atomic Scale
Descriptors
Weldabilit
y
Toxicity
Data Mining
AMASS – 7/25/03
Power of Data Mining
Use known data to establish R
Calculated Atomistic
Properties Database
R
Measured Macroscopic
Properties Database
R
Predicted Macroscopic
Properties Database
Use R to predict new data
Calculated Atomistic
Properties Database
• Does not require complete and accurate multiscale
theories
• New physics in relationships R
• Quick, cheap screening for desired properties, errors,
etc. – can be qualitative
AMASS – 7/25/03
Atomic scale
descriptors
Key Issues
Data Mining
Macroscopi
c Properties
– Descriptors accessible to modeling
– Descriptors optimally chosen
• Use known relationships/physics
• Optimize from large set of possibilities
– Descriptors→Property relationship is robust
• Sensible choice of methods
• tested with cross validation, test sets
– Data
• Large enough
• Clean enough
AMASS – 7/25/03
It is common for chemists to propose new compounds from the
substitution of another, chemically similar, ion. For instance, as
illustrated in Figure 1, knowing that BaTiO3 forms a perovskite
structure, one can deduct that it is likely for another chemically
similar ion as Ca2+ to form the same structur
Data mined tendency for ionic substitutions. Red
indicates high substitution tendency. Blue indicates
that the two ions tend to not substitute
Procedure for proposing new compound candidates in a
quaternary system using the ionic substitution probability
• The Materials Genome Initiative will create a new
era of materials innovation that will serve as a
foundation for strengthening domestic industries in
these fields. This initiative offers a unique
opportunity for the United States to discover,
develop, manufacture, and deploy advanced
materials at least twice as fast as possible today, at
a fraction of the cost. Essential to this effort is the
development of a data infrastructure that will
provide the needed data and tools to support this
effort. Some of the fundamental data needed for
this infrastructure is phase based material data.
Quantum Materials Informatics
Project
On-Line Distributed Materials Development
the aflowlib.org Consortium
Stefano Curtarolo, Duke University, DMR 0639822
INTELLECTUAL MERIT
Creation of the AFLOWLIB.ORG
Repository of electronic structures.
•High-throughout data-mining
•Phenomenological rules
•Automatic Correlations
Take home message:
high-throughput ab-initio is used to
study:
• Thermoelectrics
• Photovoltaics
• Topological insulators
• Scintillators
• Magnetic alloys
ACS Comb. Sci. 13(4), 382-390 (2011),
Comp. Mat. Sci. 49, 299-3
On-Line Distributed Materials Development
the aflowlib.org Consortium
Stefano Curtarolo, Duke University, DMR 0639822
BROADER IMPACT
Technological outputs: scintillators design
Serving the community: onlineinfrastructure, aconvasp-online (web interface
for high-throughput electronic structure
calculations)
Pearson's Crystal Data is a crystallographic database published by ASM International
(Materials Park, Ohio, USA), edited by Pierre Villars and Karin Cenzual. It has its roots in the
well-known PAULING FILE project and contains crystal structures of a large variety of
inorganic materials and compounds. The "PCD" (as it is typically abbreviated) is a
collaboration between ASM International and Material Phases Data System, Vitznau,
Switzerland (MPDS), aiming to create and maintain the world's largest critically evaluated
"Non-organic database".
The current release 2013/14 contains more than 242,600 structural data sets (including atom
coordinates and displacement parameters, when determined) for about 141,600 different
chemical formulas, roughly 16,000 experimental powder diffraction patterns and about
220,000 calculated patterns (interplanar spacings, intensities, Miller indices). This release
achieves nearly full overlap with ICSD entries.
Let’s work together from
now!
Complementary
Set
Data Science is not pursued as an end in itself,
but as a means to the attainment of wisdom as
human.