This document discusses Monte Carlo simulations. It provides an overview of Monte Carlo history and methods, including examples of applications. Monte Carlo simulations are stochastic computational algorithms that rely on repeated random sampling to obtain numerical results. They allow modeling complex systems without having to solve detailed deterministic equations. Some key advantages are that they are generally easy to parallelize and provide no dynamical information like molecular dynamics simulations.

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2. MC (Monte Carlo
Calculations)
Student : Azar Larijani
Supervisor : Dr.Mina Ghiasi
AL Zahra-University In the fall of 2018

3. Computational
Chemistry
Electron structure
methods
• Ab inititio
• Semiemperical
• Density Function Theory
(DFT) MD
MC
MM
Molecular
Mechanic-
Quntum
Mechanic
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4. Monte Carlo history
What is Monte Carlo calculations ?
Why Should I Use Monte Carlo Simulation?
Applications
Examples
Summary
Conclusion
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Massa Chosetts
In statute of technology (MIT)
6.0002, fall 2016
Introduction to computational thinking and data science JOHN GOTTAG at ocm.mit.edu

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Introducing Monte Carlo Methods With R,ChristionP,Robert,George Casella,Springer Reliability Of
Structures,Chapter4,Andrzej S.Nowak,Kevin R.Collins,CRCpress.

7. 7 Introducing Monte Carlo Methods With R,ChristionP,Robert,George Casella,Springer Reliability Of
Structures,Chapter4,Andrzej S.Nowak,Kevin R.Collins,CRCpress.
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8. 8 Introducing Monte Carlo Methods With R,ChristionP,Robert,George Casella,Springer Reliability Of
Structures,Chapter4,Andrzej S.Nowak,Kevin R.Collins,CRCpress.
Triple jump
1: Production random sampling of sufficient number ( n )
2: Apply a bet on the produced samples ( underneath the curve )
( m )
3: Measure the desired parameter output
By this m, n

9. About the simulation
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*After the selection of the matter, he had information of the physical laws governing
the issue
*Recognition of the types of forces in nature and consequently the potential energy
available in nature.
the numerical solution of the mathematical equations governing the physical
phenomena
*familiarity with programming language + + C
*Understanding data analysis and computational errors
edu.nano.ir

10. 10 github.com/tmpchem/computational_chemistry

11. 11 Helsinki.fi/~rummukai/lectures/montecarlo_oulu
#include<iostream> ++C
#include<math.h>
#include<stdlib.h>
#include<time.h>
using namespace std;
int main(){
int jmax=1000; // maximum value of HIT number. (Length of
output file)
int imax=1000; // maximum value of random numbers for
producing HITs.
double x,y; // Coordinates
int hit; // storage variable of number of HITs
srand(time(0));
for (int j=0;j<jmax;j++){
hit=0;
x=0; y=0;
for(int i=0;i<imax;i++){
x=double(rand())/double(RAND_MAX);
y=double(rand())/double(RAND_MAX);
cout«"y = "«hit«endl;
if(y<=sqrt(1-pow(x,2))) hit+=1;
} //Choosing HITs according to analytic formula of circle
//cout«""«4.0*double(hit)/double(imax)«endl;
} // Print out Pi number

12. Advantages and Disadvantages of MonteCarlo
MonteCarloSimulations
DavidJ.EarlandMichaelW.Deem chapter2.page 26
Unlike molecular dynamics simulations, MonteCarlo simulations are free from the
restrictions of solving Newton’s equations of motion.MonteCarlo methods are
generally easily parallelizable ,with some techniques being ideal for use with large
CPU clusters.
Because one does not solve Newton’s equations of motion, no dynamical
information can be gathered fromatraditional MonteCarlosimulation.One of the
main difficulties of MonteCarlo simulations of proteins in an explicit solvent is
the difficulty of conducting large-scale moves.
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13. The differences between Monte Carlo methods and
molecular dynamics
13 Basics of Computational Chemistry
Mahtab Gharbi .page 214
Property MC MD
Basic information needed Energy Gradient
Particles moved in each step One All
Coordinates Any Cartesian
Atomic velocities No Yes
Time dimension No Yes
Deterministic No Yes
Sampling Non-physical Physical
Natural ensemble NVT NVE

14. Different kind of Monte Carlo softwares
http://zarbi.chem.yale.edu/software.html download
BOSS* MCPRO
MCCCS
Towhee
DL_MONTE*
Biochemical & Organic
Simolation System
investigation of
enzymatic inhibitors
and drug design
Monte Carlo for
Complex Chemical
System
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16. Summery
Remember that :
MCS Is A Powerful Tool
• To improve forecasting
• To identify priorities
• To create more reliable forecasts
• To increase confidence in models
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17. Conclusion
The practical applications of the Monte Carlo method
in physical chemistry can be referred to the
construction and analysis of the molecular model
which is proposed as an alternative to the
computational dynamic computational method and
quantum chemistry.
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http://yon.ir/Fdgew