AM 106/206: Applied Algebra Madhu Sudan 1. Lecture Notes 12

Similar documents
WALLPAPER GROUPS. Julija Zavadlav

Review of Coordinate Systems

Linear Algebra & Geometry why is linear algebra useful in computer vision?

Linear Algebra & Geometry why is linear algebra useful in computer vision?

AM 106/206: Applied Algebra Madhu Sudan 1. Lecture Notes 11

(A B) 2 + (A B) 2. and factor the result.

Notation. 0,1,2,, 1 with addition and multiplication modulo

Geometric Transformations and Wallpaper. Groups. Lance Drager Math Camp

Lecture Notes Introduction to Cluster Algebra

Tangent spaces, normals and extrema

Math Linear Algebra Final Exam Review Sheet

1. Projective geometry

Linear Algebra. Min Yan

is Use at most six elementary row operations. (Partial

Physics 411 Lecture 7. Tensors. Lecture 7. Physics 411 Classical Mechanics II

Linear Algebra Review. Fei-Fei Li

Intro Vectors 2D implicit curves 2D parametric curves. Graphics 2011/2012, 4th quarter. Lecture 2: vectors, curves, and surfaces

Intro Vectors 2D implicit curves 2D parametric curves. Graphics 2012/2013, 4th quarter. Lecture 2: vectors, curves, and surfaces

CS123 INTRODUCTION TO COMPUTER GRAPHICS. Linear Algebra /34

ISOMETRIES OF R n KEITH CONRAD

TBP MATH33A Review Sheet. November 24, 2018

L(C G (x) 0 ) c g (x). Proof. Recall C G (x) = {g G xgx 1 = g} and c g (x) = {X g Ad xx = X}. In general, it is obvious that

Linear Algebra Homework and Study Guide

1 Linear Algebra Problems

Introduction to Matrix Algebra

REFLECTIONS IN A EUCLIDEAN SPACE

Vectors. September 2, 2015

October 25, 2013 INNER PRODUCT SPACES

INNER PRODUCT SPACE. Definition 1

Bricklaying and the Hermite Normal Form

Properties of surfaces II: Second moment of area

Matrices and Matrix Algebra.

Mobile Robotics 1. A Compact Course on Linear Algebra. Giorgio Grisetti

6. Linear Transformations.

Scott Taylor 1. EQUIVALENCE RELATIONS. Definition 1.1. Let A be a set. An equivalence relation on A is a relation such that:

1 Euclidean geometry. 1.1 The metric on R n

1.1 Line Reflections and Point Reflections

5.5. Representations. Phys520.nb Definition N is called the dimensions of the representations The trivial presentation

Elementary maths for GMT

Part II. Geometry and Groups. Year

5 Symmetries and point group in a nut shell

Lecture 2: Isometries of R 2

Elementary linear algebra

Notes on nilpotent orbits Computational Theory of Real Reductive Groups Workshop. Eric Sommers

Properties of Linear Transformations from R n to R m

CS123 INTRODUCTION TO COMPUTER GRAPHICS. Linear Algebra 1/33

2. On integer geometry (22 March 2011)

8. Diagonalization.

IIT Mumbai 2015 MA 419, Basic Algebra Tutorial Sheet-1

Geometric Transformations and Wallpaper Groups

MAT 1339-S14 Class 8

Honors Advanced Mathematics Determinants page 1

To hand in: (a) Prove that a group G is abelian (= commutative) if and only if (xy) 2 = x 2 y 2 for all x, y G.

Math Exam 2, October 14, 2008

Properties of Transformations

1 Last time: determinants

4.2. ORTHOGONALITY 161

1 Matrices and matrix algebra

Mathematics 2203, Test 1 - Solutions

Lecture 4: Affine Transformations. for Satan himself is transformed into an angel of light. 2 Corinthians 11:14

MATH PROBLEM SET 6

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.

CLASSICAL GROUPS DAVID VOGAN

Chapter 1: Linear Equations

Rigid Geometric Transformations

Review of linear algebra

Methods in Computer Vision: Introduction to Matrix Lie Groups

Maths for Signals and Systems Linear Algebra for Engineering Applications

CS 468: Computational Topology Group Theory Fall b c b a b a c b a c b c c b a

Introduction to Group Theory

MA441: Algebraic Structures I. Lecture 14

TIEA311 Tietokonegrafiikan perusteet kevät 2018

2. Diffraction as a means to determine crystal structure

Exercise Sheet 1.

Lecture # 3 Orthogonal Matrices and Matrix Norms. We repeat the definition an orthogonal set and orthornormal set.

Span and Linear Independence

Symmetry in 2D. 4/24/2013 L. Viciu AC II Symmetry in 2D

Lecture 4: Affine Transformations. for Satan himself is transformed into an angel of light. 2 Corinthians 11:14

Final Exam Practice Problems Answers Math 24 Winter 2012

MATH Topics in Applied Mathematics Lecture 12: Evaluation of determinants. Cross product.

Cambridge University Press The Mathematics of Signal Processing Steven B. Damelin and Willard Miller Excerpt More information

Isometry Dimension of Finite Groups

Chapter 1: Linear Equations

Group theory applied to crystallography

Vectors Coordinate frames 2D implicit curves 2D parametric curves. Graphics 2008/2009, period 1. Lecture 2: vectors, curves, and surfaces

INTRODUCTION TO REPRESENTATION THEORY AND CHARACTERS

11. Periodicity of Klein polyhedra (28 June 2011) Algebraic irrationalities and periodicity of sails. Let A GL(n + 1, R) be an operator all of

CSE 167: Introduction to Computer Graphics Lecture #2: Linear Algebra Primer

CS 4300 Computer Graphics. Prof. Harriet Fell Fall 2011 Lecture 11 September 29, 2011

we have the following equation:

Solutions to Math 51 First Exam October 13, 2015

ECS130 Scientific Computing. Lecture 1: Introduction. Monday, January 7, 10:00 10:50 am

Section 10.7 The Cross Product

Lecture 2: Vector-Vector Operations

Chapter 4 - MATRIX ALGEBRA. ... a 2j... a 2n. a i1 a i2... a ij... a in

Equality: Two matrices A and B are equal, i.e., A = B if A and B have the same order and the entries of A and B are the same.

Lecture 7 Cyclic groups and subgroups

PHYS 705: Classical Mechanics. Rigid Body Motion Introduction + Math Review

Leonard pairs and the q-tetrahedron algebra. Tatsuro Ito, Hjalmar Rosengren, Paul Terwilliger

Groups. Contents of the lecture. Sergei Silvestrov. Spring term 2011, Lecture 8

Transcription:

AM 106/206: Applied Algebra Madhu Sudan 1 Lecture Notes 12 October 19 2016 Reading: Gallian Chs. 27 & 28 Most of the applications of group theory to the physical sciences are through the study of the symmetry groups of physical objects e.g. molecules or crystals. Understanding the symmetries helps in understanding the objects physical properties and in determining the structure of the objects from measurements or images. 1 Isometries Recall that symmetry groups of geometric objects are defined in terms of isometries so we begin by understanding those. Def: An isometry of R n is a function T : R n R n such that for every x y R n we have T x T y = x y. Isometries are always permutations bijections. The set of isometries of R n forms a group under composition known as the Euclidean Group E n. Isometries preserve angles: T x T y = x y. Although most physical objects live in R 3 we ll focus on objects in R 2. Symmetry of 2-D objects is useful in surface physics. Everything we ll discuss has generalizations to R 3. Linear-algebraic Description of Isometries: Fact: The isometries of R n are exactly the maps of the form T x = Ax + b where A is an n n orthogonal matrix i.e. AA t = I where A t is the transpose of A and b R n. In R 2 the possible orthogonal matrices A are: cos θ sin θ cos θ sin θ Rot θ = and Ref sin θ cos θ θ = sin θ cos θ for θ [0 2π. Rot θ is a clockwise rotation around the origin by angle θ. Ref θ is a reflection through the axis that is the y-axis rotated clockwise by angle θ/2. Classification of Isometries T x = Ax + b of R 2 : A = Rot 0 = I: T is a translation. 1 These notes are copied mostly verbatim from the lecture notes from the Fall 2010 offering authored by Prof. Salil Vadhan. I will attempt to update them but apologies if some references to old dates and contents remain. 1

A = Rot θ for θ 0 2π: T is a clockwise rotation by θ degrees about the point I A 1 b. I A is invertible because A has no fixed points. A = Ref θ b orthogonal to the axis l of reflection: T is a reflection through the axis l + b/2. A = Ref θ b parallel to axis of reflection: T is a glide-reflection: A = Ref θ b neither parallel nor perpendicular to axis of reflection: T is a glide-reflection along the axis l + b /2 where b is the component of b perpendicular to the axis of reflection. Q: What are the orders of each of the above elements in the group of isometries of R 2 under composition? 2 Symmetry Groups Def: For a set F of points in R n the symmetry group of F is the set IsomF of isometries T : R n R n such that T F = F where T F = {T x : x F } under composition. Many physical objects are not merely sets of points but the points have different types eg they may be different atoms. We can generalize the above definition to allow a set X of types of points where X includes an element that represents no point being present as follows: Def: For a function F : R n X the symmetry group of F is the set IsomF of isometries T : R n R n such that F T = F under composition. That is the type F T x of point T x equals the type F x of point x for all x R n. Examples: Silicon 100 Face. This is the 2-D crystal obtained by cutting a 3-D Silicon crystal along a particular face. When this is done the forces exerted on atoms near the surface changes causing those atoms to shift slightly reducing the symmetry and changing the physical properties as we will see. This process is called reconstruction. The attached sheets show both the unreconstructed and reconstructed forms of the Si100 face. All the circles are silicon atoms. The different colors and heights indicate different distances from the surface so we treat these atoms as different from each other. Important subgroups of IsomF :. The translation subgroup TransF is the set of translations in IsomF. This is a normal subgroup of IsomF. For p R n the point group at p is PointF p = stab G p = {T G : T p = p}. Includes rotations around p and reflections through axis containing p. 2

May be different for different points p. From knowledge of TransF and PointF p for a point p of highest rotational symmetry i.e. PointF p contains a rotation of the smallest angle among all points p it is usually not much work to determine the full symmetry group IsomF. See PS7. Let s calculate these subgroups for our Si100 examples. 3 Crystallographic Groups Def: A 2-D crystal is an infinite arrangement F : R 2 X of molecules or atoms whose translation subgroup is isomorphic to Z Z. That is there are two linearly independent vectors v 1 v 2 R 2 such that the translational symmetries of F are exactly those of the form x x + k 1 v 1 + k 2 v 2 for integers k 1 k 2. The points LattF = {k 1 v 1 + k 2 v 2 } are a 2-dimensional lattice consisting of points that are symmetric with the origin under translational symmetry. We call this the translation lattice of F. Thm: If F : R 2 X is a 2-D crystal and p R 2 then RotF p is of order 1 2 3 4 or 6. Hence RotF p is isomorphic to Z n or D n for n {1 2 3 4 6}. Proof: We ll show n 6. The proof that n 5 is similar. By translating we can assume p = 0. Let v be a shortest nonzero vector in the translation lattice for F and T v be the corresponding translation. If Rot θ PointF 0 then Rot θ T v Rot 1 θ = T Rotθ v is also in the translation subgroup. Thus v Rot θ v is also in the translation lattice. Since v was the shortest nonzero vector in the translation lattice we have v Rot θ v v i.e. sinθ/2 1/2. If θ = 1/n for a positive integer n then this implies n 6. Example: The 5 geometrically distinct 2-D lattices parallelogram square rectangular rhombic hexagonal and their point groups Gallian Figure 28.20. Whether or not these full symmetry groups are retained depends on what is placed in the fundamental region of the lattice. The Plane Crystallographic Groups: There are 17 different plane cystallographic groups. This holds whether our notion of equivalence between IsomF 1 and IsomF 2 is group isomoporphism or geometric equivalence the groups are identical under an affine-linear change of coordinates. 3

Gallian Figure 28.18 gives a flowchart for classifying plane figures of these 17 groups. Let s apply this to our Si100 examples. 4 Using Symmetry to Understand Physical Properties Any physical property of a molecule or crystal must be invariant on its symmetry group. For example let γ : R 2 R be a scalar quantity giving some physical property of F at each point such as the surface energy or line energy density. Then it most hold that γ T = γ for every T in the symmetry group of F. Note that it suffices to impose this condition just for a set {T 1... T k } of generators of the symmetry group G i.e. G = T 1... T k. This reduces the number of degrees of freedom in h. For example the symmetry group of the square lattice has generators Rot π/2 Ref 0 T 10 and T 01. This imposes the following constraints on γ: γx y = γy x γx y = γ x y γx y = γx + 1 y γx y = γx y + 1 Group theory specifically the theory of group representations characterizes the form of possible functions h that can satisfy the above conditions. Many physical quantities of interest such as electric fields and current density are not scalars but vector fields h : R 2 R 2 or h : R 3 R 3. Two examples of interest in surface physics are the mass flux J : R 2 R 2 which gives the amount and direction of flow of a substance at a point on the surface and the concentration gradient φ : R 2 R 2. The relation between these two quantities is a physical property of the crystal known as the diffusivity D which is a 2 2 matrix. Fick s Law says that J = D φ. That is for every u v R 2 we have: Jx u v J y u v Dxx D = yx D xy D yy φx u v φ y u v Note that we are assuming that the diffusivity D has no dependence on the position u v which is reasonable because diffusion is a macroscopic property. Thus its behavior is automatically invariant under translation. However the point group still imposes additional constraints on D. Specifically if we apply a symmetry element A PointF 0 0 to the concentration gradient φ0 0 at zero then we should get current density AJ0 0. So DA φ0 0 = AJ0 0 = A D φ0 0. Since this holds for all vectors φ0 0 we have or equivalently ADA 1 = D. DA = AD. 4

For example if the point group at 0 were D 4 as in the square lattice or the unreconstructed Si100 face if we treat atoms at different heights as equivalent then taking A = Rot π/2 = 0 1 we get the following constraints on σ: 1 0 Dyx D xx D yy D xy Dxy D = yy D xx D yx which tells us that D xx = D yy and D xy = D yx. Taking A = Ref 0 we get: Dxx D yx Dxx D = yx D xy D yy D xy D yy which tells us that D xy = D yx = 0. Thus we conclude that D is simply of the form: D = α 0 0 α i.e. the diffusivity is a fixed constant independent of direction. When a physical property is idependent of direction like this it is called isotropic. On the other hand in the case of the Si100 surfaces either the reconstructed case or the unreconstructed case if we treat atoms at different heights as inequivalent the point group is generated by Ref 0 and I. The latter commutes with every matrix and hence imposes no additional constraints. Thus we can only conclude that the diffusivity is a diagonal matrix but it may have different constants of proportionality in the x and y directions. That is in this case the diffusivity may be anisotropic. 5