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In [[artificial intelligence]] and [[operations research]], '''constraint satisfaction''' is the process of finding a solution to a set of [[Constraint (mathematics)|constraint]]s that impose conditions that the [[Variable (mathematics)|variables]] must [[satisfiability|satisfy]]. A solution is therefore a vector of variables that satisfies all constraints.
In [[condensed matter physics]], the '''Fermi surface''' is an abstract boundary in [[reciprocal space]] useful for predicting the thermal, electrical, magnetic, and optical properties of [[metal]]s, [[semimetal]]s, and doped [[semiconductor]]s. The shape of the Fermi surface is derived from the periodicity and symmetry of the [[crystalline lattice]] and from the occupation of [[electronic band structure|electronic energy bands]].   The existence of a Fermi surface is a direct consequence of the [[Pauli exclusion principle]], which allows a maximum of two electrons per quantum state.


The techniques used in constraint satisfaction depend on the kind of constraints being considered. Often used are constraints on a finite domain, to the point that [[constraint satisfaction problem]]s are typically identified with problems based on constraints on a finite domain. Such problems are usually solved via [[Search algorithm|search]], in particular a form of [[backtracking]] or [[local search (constraint satisfaction)|local search]]. [[Constraint propagation]] are other methods used on such problems; most of them are incomplete in general, that is, they may solve the problem or prove it unsatisfiable, but not always. Constraint propagation methods are also used in conjunction with search to make a given problem simpler to solve. Other considered kinds of constraints are on real or rational numbers; solving problems on these constraints is done via [[variable elimination]] or the [[simplex algorithm]].
==Theory==
Consider a spinless ideal [[Fermi gas]] of <math>N</math> particles. According to [[Fermi–Dirac statistics]], the mean occupation number of a state with energy <math>\epsilon_i</math> is given by<ref name='Reif1965dist341'>{{harv|Reif|1965|p=341}}</ref>


Constraint satisfaction originated in the field of [[artificial intelligence]] in the 1970s (see for example {{Harv|Laurière|1978}}). During the 1980s and 1990s, embedding of constraints into a [[programming language]] were developed. Languages often used for [[constraint programming]] are [[Prolog]] and [[C++]].
<math>\langle n_i\rangle =\frac{1}{e^{(\epsilon_i-\mu)/k_BT}+1},</math>


==Constraint satisfaction problem==
where,
{{main|Constraint satisfaction problem}}


As originally defined in artificial intelligence, constraints enumerate the possible values a set of variables may take. Informally, a finite domain is a finite set of arbitrary elements. A constraint satisfaction problem on such domain contains a set of variables whose values can only be taken from the domain, and a set of constraints, each constraint specifying the allowed values for a group of variables. A solution to this problem is an evaluation of the variables that satisfies all constraints. In other words, a solution is a way for assigning a value to each variable in such a way that all constraints are satisfied by these values.
*<math>\left\langle n_i\right\rangle</math> is the mean occupation number


In some circumstances, there may exist additional requirements: one may be interested not only in the solution (and in the fastest or most computationally efficient way to reach it) but in how it was reached; e.g. one may want the "simplest" solution ("simplest" in a logical, non computational sense that has to be precisely defined). This is often the case in logic games such as Sudoku.
*<math>\epsilon_i</math> is the kinetic energy of the <math>i^{th}</math> state


In practice, constraints are often expressed in compact form, rather than enumerating all values of the variables that would satisfy the constraint. One  of the most used constraints is the one establishing that the values of the affected variables must be all different.
*<math>\mu</math> is the ''[[internal chemical potential]]'' (at zero temperature, this is the maximum kinetic energy the particle can have, i.e. [[Fermi energy]] <math>\epsilon_F</math>)


Problems that can be expressed as constraint satisfaction problems are the [[Eight queens puzzle]], the [[Sudoku]] solving problem, the [[Boolean satisfiability problem]], [[Scheduling (production processes)|scheduling]] problems and various problems on graphs such as the [[graph coloring]] problem.
Suppose we consider the limit <math>T\to 0</math>. Then we have,


While usually not included in the above definition of a constraint satisfaction problem, arithmetic equations and inequalities bound the values of the variables they contain and can therefore be considered a form of constraints. Their domain is the set of numbers (either integer, rational, or real), which is infinite: therefore, the relations of these constraints may be infinite as well; for example, <math>X=Y+1</math> has an infinite number of pairs of satisfying values. Arithmetic equations and inequalities are often not considered within the definition of a "constraint satisfaction problem", which is limited to finite domains. They are however used often in [[constraint programming]].
<math>\left\langle n_i\right\rangle\approx\begin{cases}1 & (\epsilon_i<\mu) \\ 0 & (\epsilon_i>\mu)\end{cases}.</math>


===Solving===
By the [[Pauli exclusion principle]], no two fermions can be in the same state. Therefore, in the state of lowest energy, the particles fill up all energy levels below <math>\epsilon_F</math>, which is equivalent to saying that ''<math>\epsilon_F</math> is the energy level below which there are exactly <math>N</math> states.


Constraint satisfaction problems on finite domains are typically solved using a form of [[Search algorithm|search]]. The most used techniques are variants of [[backtracking]], [[constraint propagation]], and [[Local search (optimization)|local search]]. These techniques are used on problems with [[nonlinear]] constraints.
In momentum space, these particles fill up a sphere of radius <math>p_F</math>, the surface of which is called the '''Fermi surface'''<ref>K. Huang, ''Statistical Mechanics'' (2000), p244</ref>


In case there is a requirement on "simplicity", a pure logic, pattern based approach was first introduced for the Sudoku CSP in the book {{lang|en|''The Hidden Logic of Sudoku''}}<ref name="The Hidden Logic of Sudoku">{{en}}{{cite news | first = Denis | last = Berthier | titre = {{lang|en|The Hidden Logic of Sudoku}} | url = http://www.carva.org/denis.berthier/HLS | work = Lulu Publishers, ISBN 978-1-84753-472-9  | date = 16 mai 2007 | accessdate = 16 mai 2007 }}</ref>. It has recently been generalized to any finite CSP in another book by the same author: {{lang|en|''Constraint Resolution Theories''}}<ref name="Constraint Resolution Theories">{{en}}{{cite news | first = Denis | last = Berthier | titre = {{lang|en|Constraint Resolution Theories}} | url = http://www.carva.org/denis.berthier/CRT | work = Lulu Publishers, ISBN 978-1-4478-6888-0  | date = 5 octobre 2011 | accessdate = 5 octobre 2011 }}</ref>.
The linear response of a metal to an electric, magnetic or thermal gradient is determined by the shape of the Fermi surface, because currents are due to changes in the occupancy of states near the Fermi energy. Free-electron Fermi surfaces are spheres of radius


[[Variable elimination]] and the [[simplex algorithm]] are used for solving [[linear]] and [[polynomial]] equations and inequalities, and problems containing variables with infinite domain. These are typically solved as [[Optimization (mathematics)|optimization]] problems in which the optimized function is the number of violated constraints.
<math>k_F = \frac{\sqrt{2 m E_F}} {\hbar}</math>


===Complexity===
determined by the valence electron concentration where <math>\hbar</math> is the [[reduced Planck's constant]].  A material whose Fermi level falls in a gap between bands is an [[Electrical insulation|insulator]] or semiconductor depending on the size of the [[bandgap]].  When a material's Fermi level falls in a bandgap, there is no Fermi surface.
{{main|Complexity of constraint satisfaction}}


Solving a constraint satisfaction problem on a finite domain is an [[NP complete]] problem with respect to the domain size. Research has shown a number of [[Tractable problem|tractable]] subcases, some limiting the allowed constraint relations, some requiring the scopes of constraints to form a tree, possibly in a reformulated version of the problem. Research has also established relationship of the constraint satisfaction problem with problems in other areas such as [[finite model theory]].
[[Image:graphiteFS.png|thumb|A view of the [[graphite]] Fermi surface at the corner H points of the
[[Brillouin zone]] showing the trigonal symmetry of the electron and
hole pockets.]]


A very different aspect of complexity appears when one fixes the size of the domain. It is about the complexity distribution of minimal instances of a CSP of fixed size (e.g. Sudoku(9x9)). Here, complexity is measured according to the above-mentioned "simplicity" requirement (see {{lang|en|''Unbiased Statistics of a CSP - A Controlled-Bias Generator'}}<ref name="Berthier-CBG">Denis Berthier, {{lang|en|''Unbiased Statistics of a CSP - A Controlled-Bias Generator''}}, {{lang|en|International Joint Conferences on Computer}}, {{lang|en|Information}}, {{lang|en|Systems Sciences and Engineering}} (CISSE 09), {{lang|en|December 4-12, 2009}}</ref> or {{lang|en|''Constraint Resolution Theories''}}<ref name="Constraint Resolution Theories"/>). In this context, a minimal instance is an instance with a unique solution such that if any given (or clue) is deleted from it, the resulting instance has several solutions (statistics can only be meaningful on the set of minimal instances).
Materials with complex crystal structures can have quite intricate Fermi surfaces.  The figure illustrates the [[anisotropic]] Fermi surface of graphite, which has both electron and hole pockets in its Fermi surface due to multiple bands crossing the Fermi energy along the <math>\vec{k}_z</math> direction. Often in a metal the Fermi surface radius <math>k_F</math> is larger than the size of the first [[Brillouin zone]] which results in a portion of the Fermi surface lying in the second (or higher) zones.   As with the band structure itself, the Fermi surface can be displayed in an extended-zone scheme where <math>\vec{k}</math> is allowed to have arbitrarily large values or a reduced-zone scheme where wavevectors are shown [[Modular arithmetic|modulo]] <math>\frac{2 \pi} {a}</math> (in the 1-dimensional case) where a is the [[lattice constant]]. In the three-dimensional case the reduced zone scheme means that from any wavevector <math>\vec{k}</math> there is an appropriate number of reciprocal lattice vectors <math>\vec{K}</math> subtracted that the new <math>\vec{k}</math> now is closer to the origin in <math>\vec{k}</math>-space than to any <math>\vec{K}</math>. Solids with a large density of states at the Fermi level become unstable at low temperatures and tend to form [[ground state]]s where the condensation energy comes from opening a gap at the Fermi surface.    Examples of such ground states are [[superconductor]]s, [[ferromagnet]]s, [[Jahn–Teller effect|Jahn–Teller distortions]] and [[spin density wave]]s.


==Constraint programming==
The state occupancy of [[fermion]]s like electrons is governed by [[Fermi–Dirac statistics]] so at finite temperatures the Fermi surface is accordingly broadened.  In principle all fermion energy level populations are bound by a Fermi surface although the term is not generally used outside of condensed-matter physics.
{{main|Constraint programming}}


Constraint programming is the use of constraints as a programming language to encode and solve problems. This is often done by embedding constraints into a [[programming language]], which is called the host language. Constraint programming originated from a formalization of equalities of terms in [[Prolog II]], leading to a general framework for embedding constraints into a [[logic programming]] language. The most common host languages are [[Prolog]], [[C++]], and [[Java (programming language)|Java]], but other languages have been used as well.
==Experimental determination==
Electronic Fermi surfaces have been measured through observation of the oscillation of transport properties in magnetic fields <math>H</math>, for example the [[de Haas–van Alphen effect]] (dHvA)  and the [[Shubnikov–de Haas effect]] (SdH). The former is an oscillation in [[magnetic susceptibility]] and the latter in [[resistivity]].  The oscillations are periodic versus <math>1/H</math> and occur because of the quantization of energy levels in the plane perpendicular to a magnetic field, a phenomenon first predicted by [[Lev Landau]]. The new states are called Landau levels and  are separated by an energy <math>\hbar \omega_c</math> where <math>\omega_c = eH/m^*c</math> is called the [[electron cyclotron resonance|cyclotron frequency]], <math>e</math> is the electronic charge, <math>m^*</math> is the electron [[effective mass (solid-state physics)|effective mass]] and <math>c</math> is the [[speed of light]].    In a famous result, [[Lars Onsager]] proved that the period of oscillation <math>\Delta H</math> is related to the cross-section of the Fermi surface (typically given in <math>\AA^{-2}</math>) perpendicular to the magnetic field direction <math>A_{\perp}</math> by the equation <math>A_{\perp} = \frac{2 \pi e \Delta H}{\hbar c}</math>. Thus the determination of the periods of oscillation for various applied field directions allows mapping of the Fermi surface.


===Constraint logic programming===
Observation of the dHvA and SdH oscillations requires magnetic fields large enough that the circumference of the cyclotron orbit is smaller than a [[mean free path]].  Therefore dHvA and SdH experiments are usually performed at high-field facilities like the [http://www.hfml.ru.nl/ High Field Magnet Laboratory] in Netherlands, [http://ghmfl.grenoble.cnrs.fr/ Grenoble High Magnetic Field Laboratory] in France, the [http://akahoshi.nims.go.jp/TML/english/ Tsukuba Magnet Laboratory] in Japan or the  [http://www.magnet.fsu.edu/ National High Magnetic Field Laboratory] in the United States.
{{main|Constraint logic programming}}


A constraint logic program is a [[Logic programming|logic program]] that contains constraints in the bodies of clauses. As an example, the clause <code>A(X):-X>0,B(X)</code> is a clause containing the constraint <code>X>0</code> in the body. Constraints can also be present in the goal. The constraints in the goal and in the clauses used to prove the goal are accumulated into a set called [[constraint store]]. This set contains the constraints the interpreter has assumed satisfiable in order to proceed in the evaluation. As a result, if this set is detected unsatisfiable, the interpreter backtracks. Equations of terms, as used in logic programming, are considered a particular form of constraints which can be simplified using [[unification (computing)|unification]]. As a result, the constraint store can be considered an extension of the concept of [[substitution]] that is used in regular logic programming. The most common kinds of constraints used in constraint logic programming are constraints over integers/rational/real numbers and constraints over finite domains.
[[Image:Fermi surface of BSCCO exp.jpg|thumb| [[Fermi surface of BSCCO]] measured by [[ARPES]]. The experimental data shown as an intensity plot in yellow-red-black scale. Green dashed rectangle represents the [[Brillouin zone]] of the CuO2 plane of [[BSCCO]].]]


[[Concurrent constraint logic programming]] languages have also been developed. They significantly differ from non-concurrent constraint logic programming in that they are aimed at programming [[concurrent process]]es that may not terminate. [[Constraint handling rules]] can be seen as a form of concurrent constraint logic programming, but are also sometimes used within a non-concurrent constraint logic programming language. They allow for rewriting constraints or to infer new ones based on the truth of conditions.
The most direct experimental technique to resolve the electronic structure of crystals in the momentum-energy space (see [[reciprocal lattice]]), and, consequently, the Fermi surface, is the [[angle resolved photoemission spectroscopy]] ([[ARPES]]). An example of the [[Fermi surface of superconducting cuprates]] measured by [[ARPES]] is shown in figure.


===Constraint satisfaction toolkits===
With [[positron annihilation]] the two photons carry the momentum of the electron away; as the momentum of a thermalized positron is negligible, in this way also information about the momentum distribution can be obtained. Because the positron can be [[polarized]], also the momentum distribution for the two [[Spin (physics)|spin]] states in magnetized materials can be obtained. Another advantage with de Haas–Van Alphen effect is that the technique can be applied to non-dilute alloys. In this way the first determination of a ''smeared Fermi surface'' in a 30% alloy was obtained in 1978.


Constraint satisfaction toolkits are [[Software library|software libraries]] for [[imperative programming language]]s that are used to encode and solve a constraint satisfaction problem.
==See also==
 
*[[Fermi energy]]
* [[Cassowary constraint solver]] is an [[open source]] project for constraint satisfaction (accessible from C, Java, Python and other languages).
*[[Brillouin zone]]
* [[Comet (programming language)|Comet]], a commercial programming language and toolkit
*[[Fermi surface of superconducting cuprates]]
* [[Gecode]], an open source portable toolkit written in C++ developed as a production-quality and highly efficient implementation of a complete theoretical background.
*[[Kelvin probe force microscope]]
* [[JaCoP (solver)]] an open source [http://jacop.osolpro.com/ Java constraint solver]
* [http://www.koalog.com/ Koalog] a commercial Java-based constraint solver.
* [http://www.logilab.org/projects/constraint logilab-constraint] an open source constraint solver written in pure Python with constraint propagation algorithms.
* [http://minion.sourceforge.net/ MINION] an open-source constraint solver written in C++, with a small language for the purpose of specifying models/problems.
* [http://www.bracil.net/CSP/cacp/cacpdemo.html ZDC] is an open source program developed in the [http://www.bracil.net/CSP/cacp/ Computer-Aided Constraint Satisfaction Project] for modelling and solving constraint satisfaction problems.
 
===Other constraint programming languages===
 
Constraint toolkits are a way for embedding constraints into an [[imperative programming language]]. However, they are only used as external libraries for encoding and solving problems. An approach in which constraints are integrated into an imperative programming language is taken in the [[Kaleidoscope programming language]].
 
Constraints have also been embedded into [[Functional programming|functional programming languages]].
 
== See also ==
 
* [[Constraint satisfaction problem]]
* [[Constraint (mathematics)]]
* [[Candidate solution]]
* [[Boolean satisfiability problem]]
* [[Decision theory]]
* [[Satisfiability modulo theories]]


==References==
==References==
{{reflist}}
{{reflist}}
*{{cite book
*N. Ashcroft and N.D. Mermin, ''Solid-State Physics'', ISBN 0-03-083993-9.
| last=Apt| first=Krzysztof
*W.A. Harrison, ''Electronic Structure and the Properties of Solids'', ISBN 0-486-66021-4.
| title=Principles of constraint programming
*[http://www.phys.ufl.edu/fermisurface/ VRML Fermi Surface Database]
| publisher=Cambridge University Press
*J. M. Ziman, ''Electrons in Metals: A short Guide to the Fermi Surface'' (Taylor & Francis, London, 1963), ASIN B0007JLSWS.
| year=2003
| isbn=0-521-82583-0
}}
*{{cite book
| last=Berthier| first=Denis
| title=Constraint Resolution Theories
| publisher=Lulu
| year=2011
| url=http://www.carva.org/denis.berthier/CRT
| isbn=978-1-4478-6888-0
}}
*{{cite book
| last=Dechter | first=Rina
| title=Constraint processing
| publisher=Morgan Kaufmann
| year=2003
| url=http://www.ics.uci.edu/~dechter/books/index.html
| isbn=1-55860-890-7
}}
*{{cite journal
| last=Dincbas | first=M.
| last2=Simonis | first2=H.
| last3=Van Hentenryck | first3=P.
| year= 1990
| title=Solving Large Combinatorial Problems in Logic Programming
| journal=Journal of logic programming
| volume=8 | issue=1–2
| pages=75–93
| doi=10.1016/0743-1066(90)90052-7
}}
*{{cite book
| first=Eugene
| last=Freuder
| coauthors=Alan Mackworth (ed.)
| title=Constraint-based reasoning
| publisher=MIT Press
| year=1994
}}
*{{cite book
| last=Fr&uuml;hwirth | first=Thom
| coauthors=Slim Abdennadher
| title=Essentials of constraint programming
| year=2003
| publisher=Springer
| isbn=3-540-67623-6
}}
*{{cite book
| last=Guesguen | first=Hans
| coauthors=Hertzberg Joachim
| title=A Perspective of Constraint Based Reasoning
| year=1992
| publisher=Springer
| isbn=978-3-540-55510-0
}}
*{{cite journal
| last=Jaffar | first=Joxan
| coauthors=Michael J. Maher
| title=Constraint logic programming: a survey
| journal=Journal of logic programming
| volume=19/20
| pages=503–581
| year=1994
| doi=10.1016/0743-1066(94)90033-7
}}
*{{cite journal
| last=Laurière | first=Jean-Louis
| year=1978
| title=A Language and a Program for Stating and Solving Combinatorial Problems
| journal=[[Artificial Intelligence (journal)|Artificial intelligence]]
| volume=10 | issue=1
| pages=29–127
| doi=10.1016/0004-3702(78)90029-2
}}
*{{cite book
| last=Lecoutre | first=Christophe
| title=Constraint Networks: Techniques and Algorithms
| publisher=ISTE/Wiley
| year=2009
| url=http://www.iste.co.uk/index.php?f=a&ACTION=View&id=250
| isbn=978-1-84821-106-3
}}
*{{cite book
| last=Marriot | first=Kim
| coauthors=Peter J. Stuckey
| title=Programming with constraints: An introduction
| year=1998
| publisher=MIT Press
| isbn=0-262-13341-5
}}
*{{cite book
| last=Rossi | first=Francesca
| coauthors=Peter van Beek, Toby Walsh (ed.)
| title=Handbook of Constraint Programming,
| publisher=Elsevier
| year=2006
| url=http://www.elsevier.com/wps/find/bookdescription.cws_home/708863/description#description
| isbn=978-0-444-52726-4 0-444-52726-5
}}
*{{cite book
| first=Edward
| last=Tsang
| title=Foundations of Constraint Satisfaction
| publisher=Academic Press
| year=1993
| url=http://www.bracil.net/edward/FCS.html
| isbn=0-12-701610-4
}}
*{{cite book
| first=Pascal
| last=Van Hentenryck
| title=Constraint Satisfaction in Logic Programming
| publisher=MIT Press
| year=1989
| isbn=0-262-08181-4
}}


==External links==
==External links==
*[http://4c.ucc.ie/web/outreach/tutorial.html CSP Tutorial]


{{DEFAULTSORT:Constraint Satisfaction}}
[[Category:Condensed matter physics]]
[[Category:Constraint programming| ]]
[[Category:Electric and magnetic fields in matter]]
[[hy:Հարկադիր պարտավորությունների կատարում]]
[[Category:Enrico Fermi|Surface]]

Revision as of 02:47, 13 August 2014

In condensed matter physics, the Fermi surface is an abstract boundary in reciprocal space useful for predicting the thermal, electrical, magnetic, and optical properties of metals, semimetals, and doped semiconductors. The shape of the Fermi surface is derived from the periodicity and symmetry of the crystalline lattice and from the occupation of electronic energy bands. The existence of a Fermi surface is a direct consequence of the Pauli exclusion principle, which allows a maximum of two electrons per quantum state.

Theory

Consider a spinless ideal Fermi gas of particles. According to Fermi–Dirac statistics, the mean occupation number of a state with energy is given by[1]

where,

Suppose we consider the limit . Then we have,

By the Pauli exclusion principle, no two fermions can be in the same state. Therefore, in the state of lowest energy, the particles fill up all energy levels below , which is equivalent to saying that is the energy level below which there are exactly states.

In momentum space, these particles fill up a sphere of radius , the surface of which is called the Fermi surface[2]

The linear response of a metal to an electric, magnetic or thermal gradient is determined by the shape of the Fermi surface, because currents are due to changes in the occupancy of states near the Fermi energy. Free-electron Fermi surfaces are spheres of radius

determined by the valence electron concentration where is the reduced Planck's constant. A material whose Fermi level falls in a gap between bands is an insulator or semiconductor depending on the size of the bandgap. When a material's Fermi level falls in a bandgap, there is no Fermi surface.

A view of the graphite Fermi surface at the corner H points of the Brillouin zone showing the trigonal symmetry of the electron and hole pockets.

Materials with complex crystal structures can have quite intricate Fermi surfaces. The figure illustrates the anisotropic Fermi surface of graphite, which has both electron and hole pockets in its Fermi surface due to multiple bands crossing the Fermi energy along the direction. Often in a metal the Fermi surface radius is larger than the size of the first Brillouin zone which results in a portion of the Fermi surface lying in the second (or higher) zones. As with the band structure itself, the Fermi surface can be displayed in an extended-zone scheme where is allowed to have arbitrarily large values or a reduced-zone scheme where wavevectors are shown modulo (in the 1-dimensional case) where a is the lattice constant. In the three-dimensional case the reduced zone scheme means that from any wavevector there is an appropriate number of reciprocal lattice vectors subtracted that the new now is closer to the origin in -space than to any . Solids with a large density of states at the Fermi level become unstable at low temperatures and tend to form ground states where the condensation energy comes from opening a gap at the Fermi surface. Examples of such ground states are superconductors, ferromagnets, Jahn–Teller distortions and spin density waves.

The state occupancy of fermions like electrons is governed by Fermi–Dirac statistics so at finite temperatures the Fermi surface is accordingly broadened. In principle all fermion energy level populations are bound by a Fermi surface although the term is not generally used outside of condensed-matter physics.

Experimental determination

Electronic Fermi surfaces have been measured through observation of the oscillation of transport properties in magnetic fields , for example the de Haas–van Alphen effect (dHvA) and the Shubnikov–de Haas effect (SdH). The former is an oscillation in magnetic susceptibility and the latter in resistivity. The oscillations are periodic versus and occur because of the quantization of energy levels in the plane perpendicular to a magnetic field, a phenomenon first predicted by Lev Landau. The new states are called Landau levels and are separated by an energy where is called the cyclotron frequency, is the electronic charge, is the electron effective mass and is the speed of light. In a famous result, Lars Onsager proved that the period of oscillation is related to the cross-section of the Fermi surface (typically given in ) perpendicular to the magnetic field direction by the equation . Thus the determination of the periods of oscillation for various applied field directions allows mapping of the Fermi surface.

Observation of the dHvA and SdH oscillations requires magnetic fields large enough that the circumference of the cyclotron orbit is smaller than a mean free path. Therefore dHvA and SdH experiments are usually performed at high-field facilities like the High Field Magnet Laboratory in Netherlands, Grenoble High Magnetic Field Laboratory in France, the Tsukuba Magnet Laboratory in Japan or the National High Magnetic Field Laboratory in the United States.

Fermi surface of BSCCO measured by ARPES. The experimental data shown as an intensity plot in yellow-red-black scale. Green dashed rectangle represents the Brillouin zone of the CuO2 plane of BSCCO.

The most direct experimental technique to resolve the electronic structure of crystals in the momentum-energy space (see reciprocal lattice), and, consequently, the Fermi surface, is the angle resolved photoemission spectroscopy (ARPES). An example of the Fermi surface of superconducting cuprates measured by ARPES is shown in figure.

With positron annihilation the two photons carry the momentum of the electron away; as the momentum of a thermalized positron is negligible, in this way also information about the momentum distribution can be obtained. Because the positron can be polarized, also the momentum distribution for the two spin states in magnetized materials can be obtained. Another advantage with de Haas–Van Alphen effect is that the technique can be applied to non-dilute alloys. In this way the first determination of a smeared Fermi surface in a 30% alloy was obtained in 1978.

See also

References

43 year old Petroleum Engineer Harry from Deep River, usually spends time with hobbies and interests like renting movies, property developers in singapore new condominium and vehicle racing. Constantly enjoys going to destinations like Camino Real de Tierra Adentro.

  • N. Ashcroft and N.D. Mermin, Solid-State Physics, ISBN 0-03-083993-9.
  • W.A. Harrison, Electronic Structure and the Properties of Solids, ISBN 0-486-66021-4.
  • VRML Fermi Surface Database
  • J. M. Ziman, Electrons in Metals: A short Guide to the Fermi Surface (Taylor & Francis, London, 1963), ASIN B0007JLSWS.

External links

  1. Template:Harv
  2. K. Huang, Statistical Mechanics (2000), p244