SlideShare a Scribd company logo
WELCOME TO YOU ALL
PHYSICAL
CHEMISTRY
PRESENTATION
M.SC.-II –
(SEM. III )
2013–
2014
PERTURBATION
Presented by :–
Dharmendra R. Prajapati
RAMNIRANJAN JHUNJHUNWALA
COLLEGE
Perturbation: Background
Algebraic
Differential Equations
Perturbation

Original Equation
X 2 − 25 = 0

Y = X 2 + ε X − 25
Perturbed equation
X 2 + ε X − 25 = 0

Y vs X
15

10

0 ≤ ε <1

5

0
-8

-6

-4

-2

0

2

4

6

8

-5
Y

Epsilon=0.8
-10

Epsilon=0.5
Epsilon=0.0

-15

-20

-25

-30
X
Perturbation

Perturbed equation
X 2 + ε X − 25 = 0

Change in result (absolute values) vs Change in equation

Perturbation in the result
Perturbation in the result (root)

Root -1
-2

-0.15
-0.15

Simple (Regular)
Perturbation

0.06
ε=0.1
ε=0.1

0.05
ε=-0.1
ε=-0.1

0.04
0.03

Answer can be
in the form

0.02

-0.1
-0.1

0.01
0.01
ε=-0.01
ε=-0.01
0
0
-0.05
0
-0.05
0

ε=0.01
ε=0.01
0.05
0.05

Epsilon (perturbation)
Epsilon (perturbation)

0.1
0.1

0.15
0.15

X = X 0 + ε φ ( X 0 ) + ε 2 ...
Y = εX + X − 5
2

Y vs X

Perturbation

Original Equation
X −5 = 0

Perturbed equation
ε X 2 + X −5 = 0

40
35
30
25
Epsilon=0

Two roots instead

15

Y

20

Epsilon=0.8

one

10

Epsilon=1

5
0
-6

-4

-2

-5 0
-10
X

2

4

6

Roots are not
close to the
original root

of
Perturbation

Y = εX + X − 5
Change in result (absolute values) vs Change in equation
2

Other root varies
from the original
root dramatically,
as epsilon
approaches zero!

Root-1

Root-2
3.5

1200

Perturbation
Perturbation in Resultin Result

3

2.5
1000
2

800
1.5

600
1
0.5
400
0

200
0

0.2

0.4

0.6

0.8

1

1.2

Epsilon
0
0

0.2

0.4

0.6

Epsilon

0.8

1

1.2

Singular
perturbation
Answer may NOT
be in the form

X = X 0 + ε φ ( X 0 ) + ε 2 ...
dy
=a
dx
Solution

Differential Equations
a <1
y =ax

Perturbation-1
Solution

y = 0, at x = 0

dy
= a +ε y
dx

(

y = 0, at x = 0

)

a εx
 
a 
ε 2 x2
y = e −1
=  1 + ε x +
+ ... − 1
ε

ε 
2
 


a
ε 2 x2
= ε x +
+ ... 


ε
2

ε → 0, y → ax
 εx

= ax1 + + ... 
2



Regular Perturbation
§ Time-Independent Perturbation
Theory
 FIRST ORDER PERTURBATION:•
We are often interested in systems for which we
could solve the Schreodinger equation if the
potential energy were slightly different.
•
Consider a one-dimensional example for which
we can write the actual potential energy as
Vactual(x) = V(x) + υ(x)
(12.1)
• where υ(x) is a small perturbation added to the
unperturbed potential V(x).
•

The Hamiltonian operator of the “unperturbed”
(i.e. exactly solvable) system is

• and the Schreodinger equation of that system
therefore is
H0ψl = Elψl (12.3)
with a known set of eigenvalues El and
eigenfunctions ψl.
•

The Schreodinger equation of the “perturbed”
system contains the additional term υ(x) in the
Hamiltonian:
[H0 + υ(x)]ψn’ = En’ψn’
(12.4)
•
• which may make it impossible to solve directly for
the eigenfunctions ψn’ and eigenvalues En’.
•
However, Postulate 3 tells us that any
acceptable wave function may be expanded in a
series of eigenfunctions of the unperturbed
Hamiltonian.
Therefore we may write each function ψn’ as a
•
series of the functions ψl with constant coefficients:

• (The letter l is simply an index, having nothing to do
with the angular momentum quantum number; this
•

Substitution into Eq.(12.4) gives

•

The technique used in finding coefficients in a
Fourier series may be employed here.
We multiply each side of Eq.(12.6) by ψm*—the
•
complex conjugate of a particular unperturbed
eigenfunction ψm. We then integrate both sides over
all x, to obtain
∞

∞

∞

∞

−∞

l =1

−∞

l =1

*
'
*
ψ m [ H 0 + v( x)] ∑ anlψ l dx = ∫ Enψ m ∑ anlψ l dx
∫
•

Integrating each side of Eq.(12.7) term by term
and removing the space-independent factors anl and
En’ from the integrals. we obtain

•

Because the functions ψ are normalized and
orthogonal, the right-hand side of Eq.(12.8) reduces
to the single term anmEn’ for which l = m, and we
have

•

We can rewrite the left-hand side of Eq.(12.8) by
using the fact that H0ψl = Elψl [Eq.(12.3)]; this
•
•

∞

∞

l =1

−∞

*
'
anl ∫ψ m [ El + v( x)]ψ l dx = anm En
∑

or

∞

∞

∞

∞

*
*
'
anl El ∫ψ mψ l dx + ∑ anl ∫ψ m [ v( x)]ψ l dx = anm En
∑

•
•

(12.9)
Again, because the functions ψ are normalized
and orthogonal, the first term on the left reduces to
anmEm.

•

The second term can be written in abbreviated
form as ∑∞l=1 anlυml, where υml is an abbreviation for the
integral ∫∞-∞ψ*mυ(x)ψl dx, called the matrix element of
the perturbing potential υ(x) between the states m
and l.

l =1

−∞

l =1

−∞
•

(This term can also be written in Dirac notation as
<m|υ(x)|l>.)
• Substitution into Eq.(12.9) now yields

• and after rearranging terms,

•

Equation (12.10) is exact. No approximations
have been used in deriving it. However, it contains
too many unknown quantities to permit an exact
solution in most cases.
•

The most important of the unknown quantities is
the perturbed energy of the nth level, En’,or more
precisely,Δ En = En’ - En . So we let m=n in Eq.(12.10)
to obtain
∞
'
anl vnl = ann ( En − En )
•
∑
(12.10a)
l =0

•

To find a first approximation to this energy
difference, we make the arbitrary assumption that
anl = 1 if l = n and anl = 0 if l ≠ n. In that case, the lefthand side of Eq.(12.10a) collapses to a single term:
vnn.

• Thus Eq.(12.10) is reduced to the approximation
•

This is a first-order approximation to the
perturbed energy En’. You can recognize this
integral from the formula for the expectation value
of an operator.
•
This formula is not exact because the integral
contains the wave function of the unperturbed
system rather than the actual wave function.
Applications of perturbation theory
 Perturbation theory is an important tool for describing
real quantum systems, as it turns out to be very difficult
to find exact solutions to the Schrödinger equation
for Hamiltonians of even moderate complexity.
 The Hamiltonians to which we know exact solutions, such
as the hydrogen atom, the quantum harmonic
oscillator and the particle in a box, are too idealized to
adequately describe most systems.
 Using perturbation theory, we can use the known
solutions of these simple Hamiltonians to generate
solutions for a range of more complicated systems.
RefeRenceS
Reference books: phySical chemiStRy
- Skoog ,
holleR
phySical chemiStRy (3 Rd
edition)
=Silbey &
albeRty
phySical chemiStRy(6 th
edition)
=atkinS p.w.

•

•

The End
Thank You for Your
Attention!

More Related Content

What's hot

The Variational Method
The Variational MethodThe Variational Method
The Variational Method
James Salveo Olarve
 
Time Dependent Perturbation Theory
Time Dependent Perturbation TheoryTime Dependent Perturbation Theory
Time Dependent Perturbation Theory
James Salveo Olarve
 
Time Independent Perturbation Theory, 1st order correction, 2nd order correction
Time Independent Perturbation Theory, 1st order correction, 2nd order correctionTime Independent Perturbation Theory, 1st order correction, 2nd order correction
Time Independent Perturbation Theory, 1st order correction, 2nd order correction
James Salveo Olarve
 
Quantum mechanics a brief
Quantum mechanics a briefQuantum mechanics a brief
Quantum mechanics a briefChaitanya Areti
 
One dimensional box
One dimensional boxOne dimensional box
One dimensional box
MANISHSAHU106
 
Postulates of quantum mechanics
Postulates of quantum mechanicsPostulates of quantum mechanics
Postulates of quantum mechanics
Nįļęşh Påŕmåŕ
 
Statistical mechanics
Statistical mechanics Statistical mechanics
Statistical mechanics Kumar
 
Quantum mechanics
Quantum mechanicsQuantum mechanics
Quantum mechanics
Poojith Chowdhary
 
Particle in 1 D box
Particle in 1 D boxParticle in 1 D box
Particle in 1 D box
Pradeep Samantaroy
 
Mathematical Formulation of Quantum Mechanics
Mathematical Formulation of Quantum Mechanics Mathematical Formulation of Quantum Mechanics
Mathematical Formulation of Quantum Mechanics
rbmaitri123
 
Particle in 3D box
Particle in 3D boxParticle in 3D box
Particle in 3D box
Pradeep Samantaroy
 
CHAPTER 6 Quantum Mechanics II
CHAPTER 6 Quantum Mechanics IICHAPTER 6 Quantum Mechanics II
CHAPTER 6 Quantum Mechanics II
Thepsatri Rajabhat University
 
Specific Heat Capacity
Specific Heat CapacitySpecific Heat Capacity
Specific Heat Capacity
A. S. M. Jannatul Islam
 
Quantum mechanics1
Quantum mechanics1Quantum mechanics1
Quantum mechanics1
RameezaAnsari
 
Green function
Green functionGreen function
Green function
hamza dahoka
 
Schrödinger wave equation
Schrödinger wave equationSchrödinger wave equation
Schrödinger wave equation
HARSHWALIA9
 
Quantum mechanics I
Quantum mechanics IQuantum mechanics I
Tensor 1
Tensor  1Tensor  1
Tensor 1
BAIJU V
 
Quantum
QuantumQuantum

What's hot (20)

The Variational Method
The Variational MethodThe Variational Method
The Variational Method
 
Time Dependent Perturbation Theory
Time Dependent Perturbation TheoryTime Dependent Perturbation Theory
Time Dependent Perturbation Theory
 
Time Independent Perturbation Theory, 1st order correction, 2nd order correction
Time Independent Perturbation Theory, 1st order correction, 2nd order correctionTime Independent Perturbation Theory, 1st order correction, 2nd order correction
Time Independent Perturbation Theory, 1st order correction, 2nd order correction
 
Quantum mechanics a brief
Quantum mechanics a briefQuantum mechanics a brief
Quantum mechanics a brief
 
One dimensional box
One dimensional boxOne dimensional box
One dimensional box
 
Postulates of quantum mechanics
Postulates of quantum mechanicsPostulates of quantum mechanics
Postulates of quantum mechanics
 
Statistical mechanics
Statistical mechanics Statistical mechanics
Statistical mechanics
 
Quantum mechanics
Quantum mechanicsQuantum mechanics
Quantum mechanics
 
Particle in 1 D box
Particle in 1 D boxParticle in 1 D box
Particle in 1 D box
 
Tunneling
TunnelingTunneling
Tunneling
 
Mathematical Formulation of Quantum Mechanics
Mathematical Formulation of Quantum Mechanics Mathematical Formulation of Quantum Mechanics
Mathematical Formulation of Quantum Mechanics
 
Particle in 3D box
Particle in 3D boxParticle in 3D box
Particle in 3D box
 
CHAPTER 6 Quantum Mechanics II
CHAPTER 6 Quantum Mechanics IICHAPTER 6 Quantum Mechanics II
CHAPTER 6 Quantum Mechanics II
 
Specific Heat Capacity
Specific Heat CapacitySpecific Heat Capacity
Specific Heat Capacity
 
Quantum mechanics1
Quantum mechanics1Quantum mechanics1
Quantum mechanics1
 
Green function
Green functionGreen function
Green function
 
Schrödinger wave equation
Schrödinger wave equationSchrödinger wave equation
Schrödinger wave equation
 
Quantum mechanics I
Quantum mechanics IQuantum mechanics I
Quantum mechanics I
 
Tensor 1
Tensor  1Tensor  1
Tensor 1
 
Quantum
QuantumQuantum
Quantum
 

Viewers also liked

Chapter 2 pertubation
Chapter 2 pertubationChapter 2 pertubation
Chapter 2 pertubationNBER
 
The wkb approximation
The wkb approximationThe wkb approximation
The wkb approximation
DHRUBANKA Sarma
 
Born oppenheimer p1 7
Born oppenheimer p1 7Born oppenheimer p1 7
Born oppenheimer p1 7Lim Wei
 
The First Order Stark Effect In Hydrogen For $n=3$
The First Order Stark Effect In Hydrogen For $n=3$The First Order Stark Effect In Hydrogen For $n=3$
The First Order Stark Effect In Hydrogen For $n=3$
Johar M. Ashfaque
 
Max Born Andres Montes
Max Born Andres MontesMax Born Andres Montes
Max Born Andres Montesamontesme0003
 
Max Born By Natalie Martinez
Max Born By Natalie MartinezMax Born By Natalie Martinez
Max Born By Natalie MartinezNahttiee
 
First-order cosmological perturbations produced by point-like masses: all sca...
First-order cosmological perturbations produced by point-like masses: all sca...First-order cosmological perturbations produced by point-like masses: all sca...
First-order cosmological perturbations produced by point-like masses: all sca...
Maxim Eingorn
 
Phy 101 lecture chapter 1
Phy 101 lecture chapter 1Phy 101 lecture chapter 1
Phy 101 lecture chapter 1Sabrina Hassell
 
Engineering physics 2(Electron Theory of metals)
Engineering physics 2(Electron Theory of metals)Engineering physics 2(Electron Theory of metals)
Engineering physics 2(Electron Theory of metals)
Nexus
 
23 h electrostatics
23 h electrostatics23 h electrostatics
23 h electrostatics
Han Ge Liu
 
Insights into Dark Matter
Insights into Dark MatterInsights into Dark Matter
Insights into Dark Matter
Katie Mack
 
Adiabatic compresion and expansion of gases
Adiabatic compresion and expansion of gasesAdiabatic compresion and expansion of gases
Adiabatic compresion and expansion of gases
Gohar Rehman Sani
 
Magnetostatics 3rd 1
Magnetostatics 3rd 1Magnetostatics 3rd 1
Magnetostatics 3rd 1
HIMANSHU DIWAKAR
 
Alpha decay - physical background and practical applications
Alpha decay - physical background and practical applicationsAlpha decay - physical background and practical applications
Alpha decay - physical background and practical applications
Andrii Sofiienko
 
Trm 7
Trm 7Trm 7

Viewers also liked (20)

Chapter 2 pertubation
Chapter 2 pertubationChapter 2 pertubation
Chapter 2 pertubation
 
The wkb approximation
The wkb approximationThe wkb approximation
The wkb approximation
 
Born oppenheimer p1 7
Born oppenheimer p1 7Born oppenheimer p1 7
Born oppenheimer p1 7
 
The First Order Stark Effect In Hydrogen For $n=3$
The First Order Stark Effect In Hydrogen For $n=3$The First Order Stark Effect In Hydrogen For $n=3$
The First Order Stark Effect In Hydrogen For $n=3$
 
Max Born Andres Montes
Max Born Andres MontesMax Born Andres Montes
Max Born Andres Montes
 
Max Born By Natalie Martinez
Max Born By Natalie MartinezMax Born By Natalie Martinez
Max Born By Natalie Martinez
 
0906.2042v2
0906.2042v20906.2042v2
0906.2042v2
 
First-order cosmological perturbations produced by point-like masses: all sca...
First-order cosmological perturbations produced by point-like masses: all sca...First-order cosmological perturbations produced by point-like masses: all sca...
First-order cosmological perturbations produced by point-like masses: all sca...
 
Phy 101 lecture chapter 1
Phy 101 lecture chapter 1Phy 101 lecture chapter 1
Phy 101 lecture chapter 1
 
Engineering physics 2(Electron Theory of metals)
Engineering physics 2(Electron Theory of metals)Engineering physics 2(Electron Theory of metals)
Engineering physics 2(Electron Theory of metals)
 
511
511511
511
 
23 h electrostatics
23 h electrostatics23 h electrostatics
23 h electrostatics
 
Semi conductor diode
Semi conductor diodeSemi conductor diode
Semi conductor diode
 
Dark matter
Dark matterDark matter
Dark matter
 
Insights into Dark Matter
Insights into Dark MatterInsights into Dark Matter
Insights into Dark Matter
 
Maxwell 3D
Maxwell 3DMaxwell 3D
Maxwell 3D
 
Adiabatic compresion and expansion of gases
Adiabatic compresion and expansion of gasesAdiabatic compresion and expansion of gases
Adiabatic compresion and expansion of gases
 
Magnetostatics 3rd 1
Magnetostatics 3rd 1Magnetostatics 3rd 1
Magnetostatics 3rd 1
 
Alpha decay - physical background and practical applications
Alpha decay - physical background and practical applicationsAlpha decay - physical background and practical applications
Alpha decay - physical background and practical applications
 
Trm 7
Trm 7Trm 7
Trm 7
 

Similar to Perturbation

On the Seidel’s Method, a Stronger Contraction Fixed Point Iterative Method o...
On the Seidel’s Method, a Stronger Contraction Fixed Point Iterative Method o...On the Seidel’s Method, a Stronger Contraction Fixed Point Iterative Method o...
On the Seidel’s Method, a Stronger Contraction Fixed Point Iterative Method o...
BRNSS Publication Hub
 
On Application of Power Series Solution of Bessel Problems to the Problems of...
On Application of Power Series Solution of Bessel Problems to the Problems of...On Application of Power Series Solution of Bessel Problems to the Problems of...
On Application of Power Series Solution of Bessel Problems to the Problems of...
BRNSS Publication Hub
 
Linear response theory
Linear response theoryLinear response theory
Linear response theory
Claudio Attaccalite
 
impulse(GreensFn), Principle of Superposition
impulse(GreensFn), Principle of Superpositionimpulse(GreensFn), Principle of Superposition
impulse(GreensFn), Principle of Superposition
Sc Pattar
 
Physical Chemistry Homework Help
Physical Chemistry Homework HelpPhysical Chemistry Homework Help
Physical Chemistry Homework Help
Edu Assignment Help
 
Z Transform And Inverse Z Transform - Signal And Systems
Z Transform And Inverse Z Transform - Signal And SystemsZ Transform And Inverse Z Transform - Signal And Systems
Z Transform And Inverse Z Transform - Signal And Systems
Mr. RahüL YøGi
 
Some Remarks and Propositions on Riemann Hypothesis
Some Remarks and Propositions on Riemann HypothesisSome Remarks and Propositions on Riemann Hypothesis
Some Remarks and Propositions on Riemann Hypothesis
Jamal Salah
 
Lesson 21: More Algebra
Lesson 21: More AlgebraLesson 21: More Algebra
Lesson 21: More Algebra
Kevin Johnson
 
2 classical field theories
2 classical field theories2 classical field theories
2 classical field theories
Solo Hermelin
 
Erin catto numericalmethods
Erin catto numericalmethodsErin catto numericalmethods
Erin catto numericalmethods
oscarbg
 
Berans qm overview
Berans qm overviewBerans qm overview
Berans qm overview
Leonardo Nosce
 

Similar to Perturbation (20)

On the Seidel’s Method, a Stronger Contraction Fixed Point Iterative Method o...
On the Seidel’s Method, a Stronger Contraction Fixed Point Iterative Method o...On the Seidel’s Method, a Stronger Contraction Fixed Point Iterative Method o...
On the Seidel’s Method, a Stronger Contraction Fixed Point Iterative Method o...
 
03_AJMS_166_18_RA.pdf
03_AJMS_166_18_RA.pdf03_AJMS_166_18_RA.pdf
03_AJMS_166_18_RA.pdf
 
03_AJMS_166_18_RA.pdf
03_AJMS_166_18_RA.pdf03_AJMS_166_18_RA.pdf
03_AJMS_166_18_RA.pdf
 
On Application of Power Series Solution of Bessel Problems to the Problems of...
On Application of Power Series Solution of Bessel Problems to the Problems of...On Application of Power Series Solution of Bessel Problems to the Problems of...
On Application of Power Series Solution of Bessel Problems to the Problems of...
 
03_AJMS_209_19_RA.pdf
03_AJMS_209_19_RA.pdf03_AJMS_209_19_RA.pdf
03_AJMS_209_19_RA.pdf
 
03_AJMS_209_19_RA.pdf
03_AJMS_209_19_RA.pdf03_AJMS_209_19_RA.pdf
03_AJMS_209_19_RA.pdf
 
Tesi
TesiTesi
Tesi
 
Linear response theory
Linear response theoryLinear response theory
Linear response theory
 
impulse(GreensFn), Principle of Superposition
impulse(GreensFn), Principle of Superpositionimpulse(GreensFn), Principle of Superposition
impulse(GreensFn), Principle of Superposition
 
lec21.ppt
lec21.pptlec21.ppt
lec21.ppt
 
Physical Chemistry Homework Help
Physical Chemistry Homework HelpPhysical Chemistry Homework Help
Physical Chemistry Homework Help
 
lec23.ppt
lec23.pptlec23.ppt
lec23.ppt
 
Z Transform And Inverse Z Transform - Signal And Systems
Z Transform And Inverse Z Transform - Signal And SystemsZ Transform And Inverse Z Transform - Signal And Systems
Z Transform And Inverse Z Transform - Signal And Systems
 
lec14.ppt
lec14.pptlec14.ppt
lec14.ppt
 
Some Remarks and Propositions on Riemann Hypothesis
Some Remarks and Propositions on Riemann HypothesisSome Remarks and Propositions on Riemann Hypothesis
Some Remarks and Propositions on Riemann Hypothesis
 
Lesson 21: More Algebra
Lesson 21: More AlgebraLesson 21: More Algebra
Lesson 21: More Algebra
 
2 classical field theories
2 classical field theories2 classical field theories
2 classical field theories
 
Erin catto numericalmethods
Erin catto numericalmethodsErin catto numericalmethods
Erin catto numericalmethods
 
Ch07 6
Ch07 6Ch07 6
Ch07 6
 
Berans qm overview
Berans qm overviewBerans qm overview
Berans qm overview
 

Recently uploaded

"Impact of front-end architecture on development cost", Viktor Turskyi
"Impact of front-end architecture on development cost", Viktor Turskyi"Impact of front-end architecture on development cost", Viktor Turskyi
"Impact of front-end architecture on development cost", Viktor Turskyi
Fwdays
 
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
DanBrown980551
 
Essentials of Automations: Optimizing FME Workflows with Parameters
Essentials of Automations: Optimizing FME Workflows with ParametersEssentials of Automations: Optimizing FME Workflows with Parameters
Essentials of Automations: Optimizing FME Workflows with Parameters
Safe Software
 
Assuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyesAssuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyes
ThousandEyes
 
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
BookNet Canada
 
Accelerate your Kubernetes clusters with Varnish Caching
Accelerate your Kubernetes clusters with Varnish CachingAccelerate your Kubernetes clusters with Varnish Caching
Accelerate your Kubernetes clusters with Varnish Caching
Thijs Feryn
 
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Jeffrey Haguewood
 
UiPath Test Automation using UiPath Test Suite series, part 3
UiPath Test Automation using UiPath Test Suite series, part 3UiPath Test Automation using UiPath Test Suite series, part 3
UiPath Test Automation using UiPath Test Suite series, part 3
DianaGray10
 
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdfFIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance
 
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptxIOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
Abida Shariff
 
Key Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfKey Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdf
Cheryl Hung
 
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdfFIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance
 
When stars align: studies in data quality, knowledge graphs, and machine lear...
When stars align: studies in data quality, knowledge graphs, and machine lear...When stars align: studies in data quality, knowledge graphs, and machine lear...
When stars align: studies in data quality, knowledge graphs, and machine lear...
Elena Simperl
 
Knowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and backKnowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and back
Elena Simperl
 
Mission to Decommission: Importance of Decommissioning Products to Increase E...
Mission to Decommission: Importance of Decommissioning Products to Increase E...Mission to Decommission: Importance of Decommissioning Products to Increase E...
Mission to Decommission: Importance of Decommissioning Products to Increase E...
Product School
 
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdfFIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance
 
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
James Anderson
 
The Future of Platform Engineering
The Future of Platform EngineeringThe Future of Platform Engineering
The Future of Platform Engineering
Jemma Hussein Allen
 
UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4
DianaGray10
 
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMsTo Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
Paul Groth
 

Recently uploaded (20)

"Impact of front-end architecture on development cost", Viktor Turskyi
"Impact of front-end architecture on development cost", Viktor Turskyi"Impact of front-end architecture on development cost", Viktor Turskyi
"Impact of front-end architecture on development cost", Viktor Turskyi
 
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
 
Essentials of Automations: Optimizing FME Workflows with Parameters
Essentials of Automations: Optimizing FME Workflows with ParametersEssentials of Automations: Optimizing FME Workflows with Parameters
Essentials of Automations: Optimizing FME Workflows with Parameters
 
Assuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyesAssuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyes
 
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
 
Accelerate your Kubernetes clusters with Varnish Caching
Accelerate your Kubernetes clusters with Varnish CachingAccelerate your Kubernetes clusters with Varnish Caching
Accelerate your Kubernetes clusters with Varnish Caching
 
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
 
UiPath Test Automation using UiPath Test Suite series, part 3
UiPath Test Automation using UiPath Test Suite series, part 3UiPath Test Automation using UiPath Test Suite series, part 3
UiPath Test Automation using UiPath Test Suite series, part 3
 
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdfFIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
 
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptxIOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
 
Key Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfKey Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdf
 
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdfFIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
 
When stars align: studies in data quality, knowledge graphs, and machine lear...
When stars align: studies in data quality, knowledge graphs, and machine lear...When stars align: studies in data quality, knowledge graphs, and machine lear...
When stars align: studies in data quality, knowledge graphs, and machine lear...
 
Knowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and backKnowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and back
 
Mission to Decommission: Importance of Decommissioning Products to Increase E...
Mission to Decommission: Importance of Decommissioning Products to Increase E...Mission to Decommission: Importance of Decommissioning Products to Increase E...
Mission to Decommission: Importance of Decommissioning Products to Increase E...
 
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdfFIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
 
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
 
The Future of Platform Engineering
The Future of Platform EngineeringThe Future of Platform Engineering
The Future of Platform Engineering
 
UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4
 
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMsTo Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
 

Perturbation

  • 1. WELCOME TO YOU ALL PHYSICAL CHEMISTRY PRESENTATION M.SC.-II – (SEM. III ) 2013– 2014 PERTURBATION Presented by :– Dharmendra R. Prajapati RAMNIRANJAN JHUNJHUNWALA COLLEGE
  • 3. Perturbation Original Equation X 2 − 25 = 0 Y = X 2 + ε X − 25 Perturbed equation X 2 + ε X − 25 = 0 Y vs X 15 10 0 ≤ ε <1 5 0 -8 -6 -4 -2 0 2 4 6 8 -5 Y Epsilon=0.8 -10 Epsilon=0.5 Epsilon=0.0 -15 -20 -25 -30 X
  • 4. Perturbation Perturbed equation X 2 + ε X − 25 = 0 Change in result (absolute values) vs Change in equation Perturbation in the result Perturbation in the result (root) Root -1 -2 -0.15 -0.15 Simple (Regular) Perturbation 0.06 ε=0.1 ε=0.1 0.05 ε=-0.1 ε=-0.1 0.04 0.03 Answer can be in the form 0.02 -0.1 -0.1 0.01 0.01 ε=-0.01 ε=-0.01 0 0 -0.05 0 -0.05 0 ε=0.01 ε=0.01 0.05 0.05 Epsilon (perturbation) Epsilon (perturbation) 0.1 0.1 0.15 0.15 X = X 0 + ε φ ( X 0 ) + ε 2 ...
  • 5. Y = εX + X − 5 2 Y vs X Perturbation Original Equation X −5 = 0 Perturbed equation ε X 2 + X −5 = 0 40 35 30 25 Epsilon=0 Two roots instead 15 Y 20 Epsilon=0.8 one 10 Epsilon=1 5 0 -6 -4 -2 -5 0 -10 X 2 4 6 Roots are not close to the original root of
  • 6. Perturbation Y = εX + X − 5 Change in result (absolute values) vs Change in equation 2 Other root varies from the original root dramatically, as epsilon approaches zero! Root-1 Root-2 3.5 1200 Perturbation Perturbation in Resultin Result 3 2.5 1000 2 800 1.5 600 1 0.5 400 0 200 0 0.2 0.4 0.6 0.8 1 1.2 Epsilon 0 0 0.2 0.4 0.6 Epsilon 0.8 1 1.2 Singular perturbation Answer may NOT be in the form X = X 0 + ε φ ( X 0 ) + ε 2 ...
  • 7. dy =a dx Solution Differential Equations a <1 y =ax Perturbation-1 Solution y = 0, at x = 0 dy = a +ε y dx ( y = 0, at x = 0 ) a εx   a  ε 2 x2 y = e −1 =  1 + ε x + + ... − 1 ε  ε  2     a ε 2 x2 = ε x + + ...    ε 2  ε → 0, y → ax  εx  = ax1 + + ...  2   Regular Perturbation
  • 8. § Time-Independent Perturbation Theory  FIRST ORDER PERTURBATION:• We are often interested in systems for which we could solve the Schreodinger equation if the potential energy were slightly different. • Consider a one-dimensional example for which we can write the actual potential energy as Vactual(x) = V(x) + υ(x) (12.1) • where υ(x) is a small perturbation added to the unperturbed potential V(x).
  • 9. • The Hamiltonian operator of the “unperturbed” (i.e. exactly solvable) system is • and the Schreodinger equation of that system therefore is H0ψl = Elψl (12.3) with a known set of eigenvalues El and eigenfunctions ψl. • The Schreodinger equation of the “perturbed” system contains the additional term υ(x) in the Hamiltonian:
  • 10. [H0 + υ(x)]ψn’ = En’ψn’ (12.4) • • which may make it impossible to solve directly for the eigenfunctions ψn’ and eigenvalues En’. • However, Postulate 3 tells us that any acceptable wave function may be expanded in a series of eigenfunctions of the unperturbed Hamiltonian. Therefore we may write each function ψn’ as a • series of the functions ψl with constant coefficients: • (The letter l is simply an index, having nothing to do with the angular momentum quantum number; this
  • 11. • Substitution into Eq.(12.4) gives • The technique used in finding coefficients in a Fourier series may be employed here. We multiply each side of Eq.(12.6) by ψm*—the • complex conjugate of a particular unperturbed eigenfunction ψm. We then integrate both sides over all x, to obtain ∞ ∞ ∞ ∞ −∞ l =1 −∞ l =1 * ' * ψ m [ H 0 + v( x)] ∑ anlψ l dx = ∫ Enψ m ∑ anlψ l dx ∫
  • 12. • Integrating each side of Eq.(12.7) term by term and removing the space-independent factors anl and En’ from the integrals. we obtain • Because the functions ψ are normalized and orthogonal, the right-hand side of Eq.(12.8) reduces to the single term anmEn’ for which l = m, and we have • We can rewrite the left-hand side of Eq.(12.8) by using the fact that H0ψl = Elψl [Eq.(12.3)]; this
  • 13. • • ∞ ∞ l =1 −∞ * ' anl ∫ψ m [ El + v( x)]ψ l dx = anm En ∑ or ∞ ∞ ∞ ∞ * * ' anl El ∫ψ mψ l dx + ∑ anl ∫ψ m [ v( x)]ψ l dx = anm En ∑ • • (12.9) Again, because the functions ψ are normalized and orthogonal, the first term on the left reduces to anmEm. • The second term can be written in abbreviated form as ∑∞l=1 anlυml, where υml is an abbreviation for the integral ∫∞-∞ψ*mυ(x)ψl dx, called the matrix element of the perturbing potential υ(x) between the states m and l. l =1 −∞ l =1 −∞
  • 14. • (This term can also be written in Dirac notation as <m|υ(x)|l>.) • Substitution into Eq.(12.9) now yields • and after rearranging terms, • Equation (12.10) is exact. No approximations have been used in deriving it. However, it contains too many unknown quantities to permit an exact solution in most cases.
  • 15. • The most important of the unknown quantities is the perturbed energy of the nth level, En’,or more precisely,Δ En = En’ - En . So we let m=n in Eq.(12.10) to obtain ∞ ' anl vnl = ann ( En − En ) • ∑ (12.10a) l =0 • To find a first approximation to this energy difference, we make the arbitrary assumption that anl = 1 if l = n and anl = 0 if l ≠ n. In that case, the lefthand side of Eq.(12.10a) collapses to a single term: vnn. • Thus Eq.(12.10) is reduced to the approximation
  • 16. • This is a first-order approximation to the perturbed energy En’. You can recognize this integral from the formula for the expectation value of an operator. • This formula is not exact because the integral contains the wave function of the unperturbed system rather than the actual wave function.
  • 17. Applications of perturbation theory  Perturbation theory is an important tool for describing real quantum systems, as it turns out to be very difficult to find exact solutions to the Schrödinger equation for Hamiltonians of even moderate complexity.  The Hamiltonians to which we know exact solutions, such as the hydrogen atom, the quantum harmonic oscillator and the particle in a box, are too idealized to adequately describe most systems.  Using perturbation theory, we can use the known solutions of these simple Hamiltonians to generate solutions for a range of more complicated systems.
  • 18. RefeRenceS Reference books: phySical chemiStRy - Skoog , holleR phySical chemiStRy (3 Rd edition) =Silbey & albeRty phySical chemiStRy(6 th edition) =atkinS p.w. 
  • 19. • • The End Thank You for Your Attention!

Editor's Notes

  1. We will start with the mathematical background. And here is the reason. Many books start with the Navier Stokes equation and then say, ‘based on order of magnitude analysis, we throw away these terms and keep these terms. Then using the following transformation, we can solve this equation’. Why do you have to do that way? If you are given an equation, can you figure out this is what you have to do , out of the blue? What are the methods that are normally tried for these kind of problems and why do we choose this method? I would like you to get some idea of these issues and hence we start with some mathematical background.
  2. A perturbation is a ‘disturbance’, usually a small disturbance. Consider an equation, or a system, and say you know the solution of the system. If the equation changes very slightly (perturbed), perhaps the solution will also change slightly. In that case, you can find the solution of the ‘perturbed equation’ to be very close to the solution of the unperturbed equation. Look at the example above.
  3. In the case considered here, if the perturbation is smaller and smaller (i.e. equation becomes closer to the original eqn), then the solution approaches the original solution. These kind of perturbations are called simple perturbations or regular perturbations. If the perturbation parameter (in the original equation) is epsilon, then the solution can be written as “original solution + epsilon * something…”
  4. Let us consider another system, where the situation is not so simple. Here a small perturbation alters the solution drastically.
  5. Even if the perturbation tends to zero, the solution does NOT tend to the original solution. We cannot write the solution like we did in the previous case. These are called ‘singular perturbation’.
  6. We can look at similar examples in differential equations. An example of regular perturbation is given here.