Molecular Dynamics Simulations Complete Part 3

Molecular dynamics simulations complete part 3 --- This time we enter the last discussion on molecular dynamics simulations (my version) hehe, namely on QM / MM. Why do I call this last discussion? Well because this discussion is the last chapter of the few references in the lab AIC. I have not found references about simulation MC, AIMD, BOMD etc. in the lab. so I ends meet a series of molecular dynamics simulations to here alone ..

This article will only cover about QM / MM is often done in the lab AIC, although QMCF (Quantum Mechanic Charge Field) also includes QM / MM, but because it looks like a dissertation from Pak Poncho is being borrowed and not returned, then it will not be dealt with on QMCF here , Why should QMCF? I understand to accommodate simulation 3+ ion with a charge up that can not be handled by the QM / MM usual well (well, just as I recall hell).

The basic idea of ​​the QM / MM is very simple, MM can be applied to large systems but less accuracy, while QM has better accuracy but can not be used for large systems with millions of atoms. Therefore, the idea of ​​how important a part they want to learn treated with QM methods while its bulk system is treated with MM. Departing from here, we can limit the system to be treated by QM, eg for ion solvation in water or ammonia, only the first solvation shell or second shell (if you have sufficient computers) that were treated with QM, while the solvent system itself is treated with MM. To adjust the distance to be treated with QM (first solvation shell) could be based on ion distribution function with N from classical molecular dynamics simulation results. Style system can be written as:

Where Fsistem style MM of the total system, FMK is style MK (quantum mechanics) in the region MK and FMK / mm is the molecular mechanics force within the MK. So that the particles can move from region to MM QM will require a smoothing function.

2 smoothingpersamaan function.

Illustration QM / MM can be illustrated in the figure below.

qm-mmgambar 1.

Okay, I think it is quite got here discussion of molecular dynamics simulations that have been gained from lectures, discussions with friends in the lab AIC. Some references that I use are as follows:

# Armunanto, R., 2004, Simulation of Ag +, Au +, Co2 + in Water, Liquid Ammonia and Water-Ammonia

Mixture, Dissertation, Leopold-Franzens-Universität Innsbruck, Austria.

# Urip, 2009, MOLECULAR DYNAMICS SIMULATION scandium (I) IN LIQUID AMMONIA METHOD AB INITIO QUANTUM MECHANICS / MECHANICS MOLECULAR, thesis, University of Gadjah Mada, Yogyakarta.

# Sukir, 2011, MOLECULAR DYNAMICS SIMULATION HYBRID QUANTUM MECHANICS / MECHANICS MOLECULAR Y2 + ION IN LIQUID AMMONIA AND WATER.

# Some good book for learning the molecular dynamics simulations

# Daan Frenkel, Berend Smit, 2002, Understanding Molecular Simulation, ACADEMIC PRESS, USA.

#Akira Satoh, 2011, Introduction to Practice of Molecular Molecular Dynamics Simulation, Monte Carlo, Brownian Dynamics, Lattice Boltzmann, Dissipative Particle Dynamics, Elsevier, japan.

and one paper that discusses the following methods of molecular dynamics simulations modern.

# Smit, B., 2008, Molecular Simulations of Zeolites: Adsorption, Diffusion, and Shape Selectivity, Chem.Rev, 108, 4125-4184.

Okay, enough of and hopefully...see you!

Potential non-Coulombic Interaction: Lennard Jones potential

Potential non-Coulombic interaction -- The second thing thomas taught me is about potential. this should I have learned through rote (to forget the formula) hahaha :D

The third day at IBK thomas directly talk about potential, which is the point I was told to study the literature on the potential to be used. in addition, Thomas also told me which kind of potential good and bad. according to Thomas, there are three types of good potential for explaining non Coulombic interaction, namely the Lennard-Jones potential, buckingham and Morse. okay, we start from a potential Lennard Jones,

Lennard Jones potential is often written as an equation with a rank of 12 - 6. Lennard Jones potential equation form are as follows:

where r ^ 12 is a tribe repulsion and r ^ 6 is the pull rate. it can be understood that the distance is too close, the repulsion between two particles become larger so that the energy would be worth +, if the distance we raise the energy will go down until it reaches the minimum value at the most optimal distance, if we increase the distance continues , the graph of quick energy will move close to 0 up at a distance of infinity. nah, to facilitate the calculations we usually set the cut off at a distance of 5-8 angstroem so that the minimum size of a box of our simulations is 10 angstroem.

in real life, the relationship between two human beings are basically following the Lennard Jones equation, where in close proximity so that mutual repulsion, at great distances weak interaction energy so neglected and we need optimum distance. hmm, so naturally some of my friends who live LDR they usually break up in the middle of the road.

buckingham form second potential is a potential. where this form is a simplification of the equation Lennard Jones.

at a distance of interaction of low energy will be negative because of the coefficient C / r ^ 6 becomes very large, in line with the increase in the distance, then the value of the energy will find the number "optimal" and move it close to 0 at long distances. buckingham potential is often used in molecular dynamics simulations (to make it, I do not understand, hehehe).

Thomas taught third potential is a potential Morse. Morse potential shape of the curve is approximately equal to Lennard Jones. where the minimum energy will be obtained at a distance r = r_e. This potential also contains parts repulsion and attraction, together with the Lennard Jones as well.

Note: non Coulombic interaction is an interaction at close range because of the long distances in value moves closer to 0, the difference with Coulombic interaction.

Molecular Dynamics Simulation (part 2) - Computational Chemistry

Molecular dynamics simulation part 2 --- In this paper we are still discussing the basics of simulation DM is about several important concepts such as the PBC (Periodic Boundary Condition), MIC (Minimum Image Convention) and the key concepts, namely DM simulation ensemble. These three things are important to know. The simple question that had been raised at Mr. Lecturer to us is if we are simulating ion in water, whereas the salt cations and anions there while we simulated only cations and anions to Which?

PBC is one of the key concepts in the simulation, a way for simulations using hundreds of atoms can be unlimited so as to approach the real nature. In this paper we are still discussing the basics of simulation DM is about several important concepts such as the PBC (Periodic Boundary Condition), MIC (Minimum Image Convention) and the key concepts, namely DM simulation ensemble. These three things are important to know. The simple question that had been raised at Mr. Lecturer to us is if we are simulating ion in water, whereas the salt cations and anions there while we simulated only cations and anions to Which?

PBC is one of the key concepts in the simulation, a way for simulations using hundreds of atoms can be unlimited so as to approach the real nature.

Picture 1. PBC

In the middle of the box in Figure 1 is a box of our simulations while other boxes are duplicates of the simulation box, as well as particles and speed of the other boxes, as well as a duplicate of our simulation box. The direction of the arrow illustrates that the particles fill all the space box. So? If there are atoms left the box simulation then another atom will go in the opposite direction to replace atoms that go earlier. Therefore, the number of atoms in the simulation box can be maintained. Furthermore, there are no atoms experience a force due to interfacial atoms missing or moved.

MIC is one way to reduce the computing time caused by the calculation of non-bonded interactions between atoms in the simulation. In addition to the MIC, other ways that could be used is to use non-bonded cutoff. In the energy MIC counted only as long as the cutoff distance limits, beyond the cutoff then the energy will be considered 0. In PBC, the distance should not be cutoff from half the size of the box simulations and in practice, most of the close-range interactions are usually unstable and can be ignored outside cutoff ,

Picture 2. MIC

Well, the last but often forgotten is the ensemble. in-depth discussion about the ensemble can be found in the book of statistical mechanics. The basic idea is how to link the microstate to macrostate state so that an overview of the simulation had been able to represent the state of the real system.

Molecular dynamics calculating the real dynamics of a system in which the properties of the system within a certain time retata can be calculated. The value of nature, A, of a system depends on the position and momentum of N particles that make up the system. A price on the spot can be written as A (p ^ N (t), r ^ N (t), where p ^ N (t) and r ^ N (t) describe the momentum and position of N at time t. Thus, , the price of A will vary over time because of the interaction between the particle simulation. In the experiment, the measured value is an average of A all the time calculation so called time average (mean time). If a measurement is made to be unlimited, it is necessary approach to get the "true" average of the value of A can be written as follows:

1.  The mean time equation

To calculate the average value of the properties of the system, thus simulating the dynamic behavior of the system is required. For each arrangement of atoms in the system, the force between atoms that are caused by interactions with other atoms can be calculated with diferential energy function. From the style of each atom can be determined acceleration through the 2nd law of Newton. Integration of the equations of motion will produce a trajectory that provides position, velocity and acceleration of particles in each period and the average of the properties can be determined by Equation 1. The large number of atoms or molecules in macroscopic circumstances make it impossible to determine the initial configuration of the system , Based on statistical mechanics who do Boltzmann and Gibbs, a single system that changes over time can be replaced by a large number of replicas diangggap system moves simultaneously. Average time can be replaced by ensemble averages.

ensemble mean,

2. The average ensemble equation

Ensemble averages or price expectations indicated by angle brackets, is the average of all the properties A replication of the ensemble generated by simulation. Double integral in the equation above shows the integral sign 6N 6N for position and momentum of all the particles. The probability density of the ensemble, ρ (pN, RN) is the probability to find configuration with Momenta P ^ N (t) and position r ^ N (t). In accordance with the hypothesis ergodik, ensemble mean is equal to the mean time. Under conditions of particle number, volume and temperature are fixed, the probability density is Boltzmann distribution:

3. Boltzmann distribution

Where E (p ^ n, r ^ N) is the energy, Q is the partition function, k_B is the Boltzmann constant and T is the temperature. Partition function for the canonical ensemble (ensemble with N, V ​​and T remains) with N identical particles can be described in equation Hamiltonian H,

nvt,

4. canonical ensemble

Hamiltonian, H, can be described as a total energy E (p ^ n, r ^ N), which is the sum of the kinetic energy K (p ^ N) and potential V (r ^ n) of the system. Factors N! arises from the particles which can not be distinguished, and a factor of 1 / h ^ (3N) is required to ensure the partition functions the same as the calculation of quantum mechanics.

In addition to the canonical ensemble, there is still another ensemble is microcanonical and grandcanonical. A discussion of both friends could find in the book of statistical mechanics.

Analyze Gaussian Output - Computational Chemistry

Analyze Gaussian Output : Eventually so also writing about this GAUSSIAN output, alhamdulilaah. About 3 weeks ago, I had followed the GAUSSIAN training organized by my lab, AIC. Well, there I was quite surprised because it turns NORMAL message termination is not necessarily signify that the system we have running right so that in order to understand whether we have running right or not should analyze the output of GAUSSIAN.Ternyata it is true, we are not just looking for NORMAL Termination , but RIGHT Termination: P.

Well, because we have to understand the output GAUSSIAN, so now we will talk about GAUSSIAN output.

Display of output GAUSSIAN there are three kinds of forms depending on the keywords that we use. First #N or default #, it indicates that the output is normally written, #P output is written to complete, including the cycle is running, the messages, execution time and output #T written briefly, just the important parts are written in the output. Here I assume normal output is written because it is the default of GAUSSIAN.

This is an example of the output single point calculations. By default GAUSSIAN will use a single calculation point with STO-3G basis set if the keyword calculation and basis set is not specified in the route section. The sentence in bold is the explanation of the parts input.

This section is the explanation that GAUSSIAN been executed and the explanation that GAUSSIAN is commercial software and not the public domain

Entering Gaussian System, Link 0 = G98

Input = air-hf-sp-tight.com

Output = water-hf-sp-tight.log

Initial command:

/usr/local/g98/l1.exe /scratch/nky/Gau-6068.inp -scrdir = / scratch / NKY /

Entering Link 1 = /usr/local/g98/l1.exe PID = 6069.

Copyright (c) 1988,1990,1992,1993,1995,1998 Gaussian, Inc.

All Rights Reserved.

This is part of the Gaussian (R) 98 program. It is based on

the Gaussian 94 (TM) system (copyright 1995 Gaussian, Inc.),

the Gaussian 92 (TM) system (copyright 1992 Gaussian, Inc.),

the Gaussian 90 (TM) system (copyright 1990 Gaussian, Inc.),

the Gaussian 88 (TM) system (copyright 1988 Gaussian, Inc.),

the Gaussian 86 (TM) system (Copyright 1986 Carnegie Mellon

University), and the Gaussian 82 (TM) system (copyright 1983

Carnegie Mellon University). Gaussian is a federally registered

trademark of Gaussian, Inc.

This software contains proprietary and confidential information,

Including trade secrets belonging to Gaussian, Inc.

This software is provided under written license and may be

used, copied, transmitted, or stored only in accord with that

written license.

The following legend is applicable only to US Government

contracts under DFARS:

RESTRICTED RIGHTS LEGEND

Use, duplication or disclosure by the US Government is subject

to restrictions as set forth in subparagraph (c) (1) (ii) of the

Rights in Technical Data and Computer Software clause at DFARS

252.227-7013.

Gaussian, Inc.

Carnegie Office Park, Building 6, Pittsburgh, PA 15 106 USA

The following legend is applicable only to US Government

contracts under FAR:

RESTRICTED RIGHTS LEGEND

Use, reproduction and disclosure by the US Government is subject

to restrictions as set forth in subparagraph (c) of the

Commercial Computer Software - Restricted Rights clause at FAR

52.227-19.

Gaussian, Inc.

Carnegie Office Park, Building 6, Pittsburgh, PA 15 106 USA

---------------------

Warning - This program may not be used in any manner that

competes with the business of Gaussian, Inc. or will provide

assistance to any competitor of Gaussian, Inc. The Licensee

of this program is prohibited from giving any competitor of

Gaussian, Inc. access to this program. By using this program,

The user acknowledges that Gaussian, Inc. is engaged in the

business of creating and licensing software in the field of

computational chemistry and represents and warrants to the

licensee that it is not a competitor of Gaussian, Inc. and that

it will not use this program in any manner prohibited above.

----------------------

Official Citation from GAUSSIAN, shall be displayed in all the papers that use GAUSSIAN as software to obtain research data.

Cite this work as:

Gaussian 98, Revision A.9,

MJ Frisch, GW Trucks, HB Schlegel, GE Scuseria,

MA Robb, JR Cheeseman, VG Zakrzewski, JA Montgomery, Jr.,

RE Stratmann, JC Burant, S. Dapprich, JM Millam,

AD Daniels, KN ​​Kudin, MC Strain, O. Farkas, J. Tomasi,

V. Barone, M. Cossi, R. Cammi, B. Mennucci, C. Pomelli, C. Adamo,

S. Clifford, J. Ochterski, GA Petersson, PY Ayala, Q. Cui,

K. Morokuma, DK Malick, AD Rabuck, K. Raghavachari,

JB Foresman, J. Cioslowski, JV Ortiz, AG Baboul,

BB Stefanov, G. Liu, A. Liashenko, P. Piskorz, I. Komaromi,

R. Gomperts, RL Martin, DJ Fox, T. Keith, MA Al-Laham,

CY Peng, A. Nanayakkara, M. Challacombe, PMW Gill,

B. Johnson, W. Chen, MW Wong, Andres JL, C. Gonzalez,

M. Head-Gordon, E. S. Replogle, and J. A. Pople,

Gaussian, Inc., Pittsburgh PA, 1998.

Version of GAUSSIAN used

*********************************************

Gaussian 98: x86-Linux-G98RevA.9 19-Apr-2000

23-Nov-2010

*********************************************

Route section, and the section title input specifications

% Chk = water-sp-hf-tight.chk

% Mem = 6MW

% Nproc = 1

Will use up to 1 processors via shared memory.

--------------

# Sp hf / 6-31g Genome = connectivity scf = tights

--------------

1/38 = 1.57 = 2/1;

2/17 = 6.18 = 5.40 = 1/2;

3/5 = 1.6 = 6.11 = 9.25 = 1.30 = 1 / 1,2,3;

4 // 1;

5/5 = 2.32 = 2.38 = 4/2;

6/7 = 2.8 = 2.9 = 2.10 = 2.28 = 1/1;

99/5 = 1.9 = 1/99;

-------

water hf / 6-31g sp tight

-------

Symbolic Z-matrix:

Charge = 0 Multiplicity = 1

O

H 1 B1

H 1 B2 2 A1

Variables:

B1 0.96

B2 0.96

A1 109.47122

1 tetrahedral angles replaced.

------------------------

Z-MATRIX (angstroms AND Degrees)

Length CD Cent Atom N1 / N2 X Alpha / Beta N3 Y / Z J

------------------------

1 1 O

2 2 H 1 0.960000 (1)

3 3 H 1 0.960000 (2) 2 109,471 (3)

------------------------

Z-Matrix orientation:

-----------------------

Center Atomic Atomic Coordinates (Angstroms)

Number Number Type X Y Z

-----------------------

1 8 0 0.000000 0.000000 0.000000

2 1 0 0.000000 0.000000 0.960000

3 1 0 0.905097 0.000000 -0.320000

-----------------------

Distance matrix (angstroms):

1 2 3

1 O 0.000000

2 H 0.960000 0.000000

3 H 0.960000 1.567673 0.000000

Interatomic angles:

H2-O1-H3 = 109.4712

Stoichiometry H2O

Framework group C2V [C2 (O), SGV (H2)]

Deg. of freedom 2

Full point group C2V Nov 4

Largest Abelian subgroups NOP C2V 4

Largest Concise Abelian subgroup C2 NOP 2

Standard orientation is a coordinate system that is used internally by the program to run calculations. Here we see that the starting point O atom is located on the axis Z and H atoms in the plane XZ. After this part there is a section which describes the symmetry of the molecule, the base set is used, orbital symmetry and how many base functions to be undertaken by GAUSSIAN.

Standard orientation:

-----------------------

Center Atomic Atomic Coordinates (Angstroms)

Number Number Type X Y Z

-----------------------

1 8 0 0.000000 0.000000 0.110851

2 1 0 0.000000 0.783837 -0.443405

3 1 0 0.000000 -0.783837 -0.443405

-----------------------

Rotational constants (GHZ): 919.0228448 408.0852596 282.5991906

Isotopes: O-16, H-1, H-1

Standard base: 6-31G (6D, 7F)

There are 7 symmetry adapted basis functions of A1 symmetry.

There are 0 symmetry adapted basis functions of A2 symmetry.

There are two symmetry adapted basis functions of B1 symmetry.

There are 4 symmetry adapted basis functions of B2 symmetry.

Crude estimate of an integral set of expansion from redundant integrals = 1,247.

Integral buffers will be 262 144 words long.

Raffenetti 1 integral format.

Two-electron integral symmetry is turned on.

13 basis functions 30 primitive Gaussians

5 alpha 5 beta electrons electrons

Repulsion nuclear energy Hartrees 9.1571766093.

One-electron integrals computed using PRISM.

NBasis = 13 RedAO = T NBF = 7 0 2 4

NBsUse = 13 1.00D-04 NBFU = 7 0 2 4

Projected INDO Guess.

Initial guess orbital symmetries:

Occupied (A1) (A1) (B2) (A1) (B1)

Virtual (A1) (B2) (A1) (A1) (A1) (B1) (B2) (B2)

Requested convergence on RMS density matrix = 1.00D-08 within 64 cycles.

Requested convergence on MAX density matrix = 1.00D-06.

Keep R1 integrals in memory in canonical form, NReq = 410 137.

The sections below explain how the energy from the calculation of the single point that we do, the method (RHF), convergence criteria, total spin, and virial ratio. Total spin relates to the number of unpaired electrons in our system. The value of the total spin can also indicate whether the spin contamination occur in our system. Spin where the contamination is the result of the wave function expected a spin state that we want, but it is a mixture of several spin state or in a language that is easier to change the spin / electron configuration in the system. Spin contamination can affect the geometry of the calculation and also slow down the calculation. Well, to check whether the spin contamination occurs or not is by looking at the value of S ** 2. S ** 2 or total spin is calculated by the formula s (s + 1). Where s is ½ of the number of unpaired electrons. In water molecules, all electrons in pairs, then s = 0 so the value S ** 2 = 0 (0 + 1) = 0. So, there is no spin contamination in the calculation :). oia, -V / T is usually worth 2.

SCF Done: E (RHF) = -75.9850783135 A.U. after 11 cycles

Convg = 0.5571D-09 V / T = 2.0001

S ** 2 = 0.0000

The next section is Mulliken population analysis. This analysis divides the total charge of the molecule into the net charge of the atoms. The label shows the estimated total atomic charge atomic total charge of the system.

************************************************** ********************

Population density analysis using the SCF.

************************************************** ********************

Orbital symmetries:

Occupied (A1) (A1) (B2) (A1) (B1)

Virtual (A1) (B2) (B2) (A1) (B1) (A1) (B2) (A1)

The electronic state is 1-A1.

Alpha occ. eigenvalues ​​- -20.55712 -1.34934 -0.71695 -0.55067 -0.49871

Alpha virt. eigenvalues ​​- 0.20412 0.29997 1.08676 1.15984 1.16641

Alpha virt. eigenvalues ​​- 1.20777 1.38369 1.68080

Condensed to atoms (all electrons):

1 2 3

1 O 8.286071 0.260589 0.260589

2 H 0.260589 0.364168 -0.028381

3 H 0.260589 -0.028381 0.364168

Total atomic charges:

1

1 O -0.807248

2 H 0.403624

3 H 0.403624

Sum of Mulliken charges = 0.00000

Atomic charges with hydrogens summed into heavy atoms:

1

1 O 0.000000

2 H 0.000000

3 H 0.000000

Sum of Mulliken charges = 0.00000

Electronic spatial extents (au): = 19.1152

Charge = 0.0000 electrons

Dipole moment (Debye):

X = 0.0000 0.0000 Y = Z = -2.5436 Tot = 2.5436

Quadrupole moment (Debye-Ang):

XX YY = = -7.2160 -3.9708 -6.2609 ZZ =

XY = 0.0000 0.0000 XZ = YZ = 0.0000

Octapole moment (Debye-Ang ** 2):

XXX = 0.0000 0.0000 YYY = ZZZ = -1.5097 XYY = 0.0000

XXY = 0.0000 XXZ = -0.4284 YZZ XZZ = 0.0000 = 0.0000

YYZ = -1.3417 XYZ = 0.0000

Hexadecapole moment (Debye-Ang ** 3):

XXXX = YYYY = -5.4609 -5.1910 -6.1186 ZZZZ = XXXY = 0.0000

XXXZ = 0.0000 YYYZ YYYX = 0.0000 = 0.0000 ZZZX = 0.0000

ZZZY = 0.0000 XXZZ = xxyy = -2.0527 -1.9236 -1.6718 YYZZ =

XXYZ = 0.0000 ZZXY YYXZ = 0.0000 = 0.0000

NN = 9.157176609255D EN + 00 + 02 = -1.989154381533D KE = 7.597651878656D + 01

Symmetry A1 KE = 6.793563727978D + 01

Symmetry A2 KE = 0.000000000000D + 00

Symmetry B1 KE = 4.561049228170D + 00

Symmetry B2 KE = 3.479832278610D + 00

This section is a summary of the results of the calculation.

1 \ 1 \ GINC-TA8-AIC \ SP \ RHF \ 6-31G \ H2O1 \ NKY \ 23-Nov-2010 \\ # SP HF / 6-31G GE

OM = CONNECTIVITY SCF = Tight water \\ hf / 6-31g sp tight \, 1 \ O \ H, 1,0.96 \ H, 1.0

.96,2,109.47122063 \\ Version = x86-Linux-G98RevA.9 \ State = 1-A1 \ HF = -75.9850

783 \ rmsd = 5.571e-10 \ Dipole = 0.817078,0., 0.5777614 \ PG = C02V [C2 (O1), SGV (H2

)] \\ @

Aphorisms drawn at random from the internal database GAUSSIAN.

Make no judgments where you have no compassion.

- Anne McCaffrey

Time calculations and other computer resource usage

Job cpu time: 0 days 0 hours 0 minutes 1.1 seconds.

File lengths (MBytes): RWF = 10 Int = 0 D2E = 0 Chk = 5 Scr = 1

Well, this is what we often expect: D

Normal termination of Gaussian 98.

Thus the explanation for GAUSSIAN output, is still not too detailed, so advice and constructive criticism from the seniors so I hope for the progress of computational chemistry, okay !.

Hope it is useful!

# Exploring Chemistry with Electronic Structure Methods, James, B Foreesman

# Computational Chemistry: A Practical Guide for Applying Techniques to Real-World Problems, David Young.

# Tie sciences with the words, it's better to write what we like than to do the work of others which of course we are not happy to do it.

Berendsen thermostat - Computational Chemistry

Berendsen thermostat - Computational Chemistry: This occasion will discuss important aspects of the simulation using NVT ensemble, in which the number of molecules, volume and temperature made permanent. to set the fixed temperature or in accordance with the target temperature of the simulation we need the name of the thermostat.

before defining the thermostat, we need to know first how to define temperature in statistical thermodynamics. in statistical thermodynamics we know that the kinetic energy = 3/2 HCV. while the kinetic energy is defined as = 1/2 mv ^ 2. if we have a banya particles then it is easy to add all the kinetic energy of each particle to get the total kinetic energy. This relationship is based on easily we know that the target temperature will be proportional to the square of the speed. then to regulate the temperature we need to do scaling in speed by using a factor lambda.

lambda = sqrt (T_o / T_system) where T_o is the target temperature while T_system the current temperature of the system. it is thus when T_system> T_o the lambda value <1. lambda factor is used to download speed scaling so that we obtain:

v '= v * lambda

This concept is called with hard scaling so that when the temperature above / below the target temperature then we "force" in order to reset the target temperature. ya remember this can only be done at any stage of equilibration.

Berendsen propose a more "soft" to perform scaling of the speed.

From the above equation we introduce a new constant called the relaxation time. for example, we use relaxation time 100fs or 0.1 ps. oh yes, this relaxation time should be chosen that mediocrity, wearing a relaxation time that is too big it will be tantamount to using a hard scaling or using relaxation time that is too small will produce fluctuations in temperature that is too small and not realistic. remember yes, the Berendsen thermostat, the speed will always be in-scaling. Okay, enough of and hopefully useful!

Berendsen manostat – Computational Chemistry

Berendsen manostat – Computational Chemistry: This time we will discuss one of the ensembles in molecular dynamics simulations that NPT ensemble, where N (number of molecules), P (pressure) and the temperature is kept. The previous article we have discussed how to maintain a constant temperature by using a Berendsen thermostat.

So, before entering how to maintain constant pressure, the first question how do we measure the pressure in the simulation? er wrong, ding before that if we do not maintain the pressure or temperature of the ensemble that we use is NVE, where E or chemical potential will be kept, if the temperature is kept so ensemble NVT and if the temperature is maintained tetep the NPT ensemble * N and V I assume tetep in the third ensemble.

Okay, back to the question how to measure the pressure in the simulation? the answer is by using the ideal gas equation. where we have a relationship pV = HCV. from here the ideal gas equation we add the virial equation. What is that? Imagine if two particles interact, eh btw, how do we know that two particles interact with each other? yap, we could measure of the potential energy due to the long distances the potential energy can be said to be close to 0 so that no interaction occurs. Besides? we can tell from the style. where we know that the value of w = F x delta_r_ij or w = - F x delta_v_ij. nah ΣΣ virial equation is then ditambahken to the ideal gas equation that had been mentioned.

if the NVT ensemble we perform scaling of the speed (Berendsen thermostat) then the NPT ensemble we do sacaling to the length of the box simulations using lambda coefficient. where lambda (Berendsen manostat) can be written as follows:

Berendsen-manostat

So now we detailed the case in the NPT ensemble. first, if we have a cube box with all the same length, the lambda ribs can be applied to all sides, this is simple, the case is known as isotropic (where all three in the same direction) in kassus semi-anisotropic and anisotropic the lambda value must be calculated differently for each axis x, y and z.

anisotropic

So, we would never know the term transition phase, when in college first material only vaguely pack and bu lecturer explained that somehow I do not understand. well when we do the NPT ensemble then chances are we can experience a transition phase, it can you know if the matrix transpose ent F, T. then you would get in a diagonal matrix FxVx FyVy FzVz. hence the term semi-anisotropic because there form the diagonal matrix.

All right, enough so first and hopefully!

# To prevent the phase transition we should really use the rigorous scaling of the temperature and the time step so that the molecule will not move much.

What is Computational Chemistry? What's Pointless Computational Chemistry?

The use of computational chemistry is to address the problem of chemical. It is inevitable in the use of computers is how to use the software. Hidden problems of this activity is about how well the answers obtained. Several approaches to computational chemistry begin to know is to ask some of these questions:

How accurate the results obtained will be obtained?

How long perhitunganya be completed?

What approach should be made? '

Is the approach used in the calculations are significant to the considered problem?

Source: http://www.scribd.com/doc/73340630/Manfaat-Kimia-Komputasi-Dalam-Penelitian, Dr. Dwi Harno Pranowo, M.Si (Computational Approaches in Learning Chemistry). To answer this, it should be traced to the development of computational chemistry so that it will get a picture of how the utilization of computational chemistry.

Computational Chemistry in Drug Design

Computational chemistry, is the use of computers in reviewing aspects of the chemistry. Description of chemical properties in a computer experiment. Molecular model used is the result of a theoretical chemist, but calculations have used "means" a particular algorithm with computer programming language. The process of designing new drugs and distribute them to the public is a long process for many years (5-7 years) and high costs (50-100 million USD) .This is a challenge for researchers to generate strategies and efforts effective and economical for new drug discovery , One strategy that has been developed to design new drug molecules are computational chemistry.

Beginning of Computational Chemistry

The development of computational chemistry very rapidly began in the 1950s. Some concepts of chemistry, especially on the molecular scale can be studied using molecular model (Leach, 1996). One advantage of this is the use of a computer with the programming language so that the properties of complex molecules can be calculated, and the results perhitunganya provide significant correlation to the experimental data.

DEVELOPMENT OF COMPUTATION CHEMISTRY

The development of computer experiments to substantially alter the traditional relationship between theory and experiment. Simulation requires an accurate method to model the system under study. Simulations can often be done with conditions very similar to the experiments so that the results of computational chemistry calculations can be compared directly with experiment, if this is the case, then the simulation is a very useful tool, not only to understand and interpret experimental data in microscopic level, but may also examine the part that can not be reached experimentally, as a reaction to the conditions of very high pressure gas or a reaction involving dangerous.

Chemical research by means of a computer in the 1950s began with an assessment of the chemical structure of the compound with physiological activity. One chemist who made a large contribution in this field is John Pople who successfully convert theories in physics and mathematics to chemistry through computer programs. Computational chemistry methods allow chemists determined the structure and properties of a chemical system quickly. Field greatly helped by developing computational chemistry is a field of crystallography.

Two researchers in the field of computational chemistry have won Nobel science in 1998 that Walter Kohn with Density Functional Theory (Density Functional Theory, DFT) and John A. Pople who had been instrumental in developing computational methods in quantum chemistry, they have members opportunities chemists studying the molecular properties and interactions between molecules. John Pople has been developing quantum chemistry as a method that can be used by almost all fields of chemistry and chemical bring into a new era that experiment and theory can work together in exploring the properties of molecular systems. One of the products of chemical computing program produced by Pople is GAUSSIAN.

In recent years this can be seen in the increase in the number of people who work in theoretical chemistry, most researchers are working part-time theorist yes that those already working in the field of chemistry in addition to the chemical theory. The increase in the number of researchers in the field of theoretical chemistry is supported by the development of computers and software capabilities are increasingly easy to use, it caused a lot of people who do a job in the field of computational chemistry, even without having enough knowledge about how chemical calculations were carried out by computer, as a result , many people do not know even the very basic explanation of how the calculations once executed so that the resulting work can be the result which actually means or just a "rubbish".

Needs Facilities

Support facilities determines the quality of work. Minimal need for chemical komputaasi actually affordable. A computer with standard specifications and primarily uses minimal operating system Windows 9X. A minimum of a Pentium processor with 16 MB RAM and an empty capacity of approximately 700 MB hard disk - 1500 MB for software installation (depending on the need) The ability of computer graphics higher the better. Only color printers needed to print and make laporan.Kebutuhan important is the application (software). Applications that are used for visualization of molecular structures can be both commercial and freeware (free) or shareware (free for the time being or for free for a limited version of the software).

Role of Computational Chemistry in the field of Molecular Drug Design

Method of in vitro and in vivo commonly used in the drug discovery process. Computers offers a method of in silico, -a method that uses computer capability in the design drug- as the complement of in vitro and in vivo.

Computing power increases exponentially is an opportunity to develop simulations and calculations in designing new drugs.

Computational chemistry software that can be used is as HyperChem (www.hypercub.com) provide adequate facilities to "see" the shape of molecules', enjoy vibration bonding between atoms that is recorded as infrared spectra, and the dynamics of changes in the molecular structure due to the influence of the reaction system.

Drug design is an iterative process begins with a determination of compounds that show important biological properties and ends with optimization measures, both of profile activity and synthesis of chemical compounds.

Without complete knowledge of the biochemical processes responsible for biological activity, drug design hypothesis is generally based on testing of structural similarity and distinction between active and inactive molecules.

The existence of a computer equipped with computational chemistry applications, enabling computational medicinal chemists describe drug compounds in three dimensions (3D) and do a comparison on the basis of similarity and energy with other compounds already known to have high activity (pharmacophore query).

Various derivatives and analogues can be "synthesized" in silico or are often given a hypothetical compound term

Molecular Modeling

Some applications in computational chemistry are molecular modeling. Some use include: molecular graphics: describe a molecule, giving a description of its characteristics; molecular visualizations: visiualisasi form; computational chemistry: computational chemistry; computational quantum chemistry: quantum chemistry theory; theoretical chemistry: theoretical chemistry aspect.

Data on Molecular Modeling

 Molecular Modeling is usually initiated through three main methods:

1. Building using standard geometries - particularly primary bond lengths and angles,

2. Build the molecule using the known fragments logically geometriesnya aspect - this is usually corrected by some method of "optimization"; and

3. Using molecular builds physical data obtained from experiments - usually X-ray crystallography, neutron diffraction, the structure deduced from the data of nuclear magnetic resonance (NMR).

Methods In Computational Chemistry

The term computational chemistry is always used if a mathematical method is intended to be run automatically by komputer.Perlu noted that the word "exact" and "perfect" does not appear in the definition of computational chemistry. Very few aspects of chemistry that can be solved exactly. Almost every aspect of chemistry described in qualitative or kuantitatif.Terdapat three categories in computational chemistry methods: Method ab initio Quantum Mechanics, Quantum Mechanics and Molecular Mechanics semiempirical.

Ab initio method Quantum Mechanics

There are several sets base choice in this program. The set of standard base used, among others, STO-3G, 3-21G, 6-31G * and 6-31G **.

Extra base functions (s, p, d, sp, spd) can be added to individual atoms or group of atoms.

Users can also define their own base set or modify existing base set using HyperChem's documented basis set file format.

 Quantum Mechanics semiempirical

• Hyperchem offers ten methods semiempirical molecular orbital, with the option of organic compounds and compounds of the major groups, for the compounds of the transition and for the simulation of spectra.

• Methods provided are Extended Huckel (by Hoffmann), CNDO and INDO (by Pople et al), MINDO3, MNDO, MNDO / d and AM1 (by Dewar et al) PM3 (by Stewart), ZINDO / 1 and ZINDO / S (by Zerner et al).

Molecular Mechanics

 HyperChem is an application that can be used easily in generating 3D molecular structure, with a choice of four methods of molecular mechanics, geometry optimization techniques to obtain a stable molecular structure and molecular dynamics techniques to get search and investigate conformational changes in the structure.

Four force field method (force field) allows us to explore the stability and dynamics of molecular systems for compounds that have an iron atom mass besar.Untuk general-purpose use MM +, while for biomolecules can be used one of the three methods of force fields: AMBER, BIO + and OPLS

MM +

According to the majority of non-biological species.

• Based on the MM2 (1977) compiled by the NL Allinger.

• Using the parameters set in 1991.

• There will be a default parameter in the case of MM2 parameter is not available

AMBER

• AMBER force field force field composed by Kollman

• Suitable for use on polypeptides and nucleic acids with all hydrogen atoms were included in the calculation

OPLS

• Designed for the calculation of nucleic acids and peptides

• OPLS prepared by Jorgensen

• Parameter not bonded optimized interaction of calculations with solvent included.

     BIO +

• CHARMM force field composed by Karplus

• Devoted to the calculation of macromolecules

• Compiled Primarily designed to explore macromolecules

• Includes CHARMM parameters for the calculation of amino acids.

Reference:

1.  Cramer, C. J., 2004, Essentials of Computational Chemistry, Theories and Models, John Wiley & Sons Ltd

2.  Frisch M. J., Trucks G. W., Schlegel H. B., Scuseria G. E., Robb M. A. et al., 1995, Gaussian98 (Revision A.1), Gaussian, Inc., Pittsburgh PA

3.  Leach, A. R., 2001, Molecular Modelling, Principles and Applications, Pearson Education Ltd., Essex

4.  Rogers, D. W., 2003, Computational Chemistry Using the PC, John Wiley & Sons, Inc.

5.  Young, D. C., 2001, Computational Chemistry, A Practical Guide for Applying Techniques to Real Worlds Problems, Wiley-Interscience, New York

6.  Website : .http://www.molecules.org/.