Lecture 5: Introduction to Quantum Chemical Simulation graduate course taught at MIT in Fall 2014 by Heather Kulik. This course covers: wavefunction theory, density functional theory, force fields and molecular dynamics and sampling.
Molecular Dynamics for Beginners : Detailed OverviewGirinath Pillai
Detailed presentation of what is molecular dynamics, how it is performed, why it is performed, applications, limitations and software resources on how to perform calculations are discussed.
Basics of Quantum and Computational ChemistryGirinath Pillai
Basic fundamentals of theoretical, quantum and computational chemistry. The methods and approaches helps in predicting the electronic structure properties as well as other spectral data.
Lecture 1: Introduction to Quantum Chemical Simulation graduate course taught at MIT in Fall 2014 by Heather Kulik. This course covers: wavefunction theory, density functional theory, force fields and molecular dynamics and sampling.
Lecture 5: Introduction to Quantum Chemical Simulation graduate course taught at MIT in Fall 2014 by Heather Kulik. This course covers: wavefunction theory, density functional theory, force fields and molecular dynamics and sampling.
Molecular Dynamics for Beginners : Detailed OverviewGirinath Pillai
Detailed presentation of what is molecular dynamics, how it is performed, why it is performed, applications, limitations and software resources on how to perform calculations are discussed.
Basics of Quantum and Computational ChemistryGirinath Pillai
Basic fundamentals of theoretical, quantum and computational chemistry. The methods and approaches helps in predicting the electronic structure properties as well as other spectral data.
Lecture 1: Introduction to Quantum Chemical Simulation graduate course taught at MIT in Fall 2014 by Heather Kulik. This course covers: wavefunction theory, density functional theory, force fields and molecular dynamics and sampling.
Metadynamics is a computer simulation method in computational physics, chemistry, and biology. It is used to estimate the free energy and other state functions of a system, where ergodicity is hindered by the form of the system's energy landscape.
Molecular Mechanics in Molecular ModelingAkshay Kank
In this slide you learn about the computational chemistry and its role in designing a drug molecule. Also learn concept about the molecular mechanics and its application to Computer Aided Drug Design. difference between the Quantum mechanics and Molecular Mechanics.
THE ENERGY MINIMIZATION, FOR THE STUDENTS OF M.PHARM, B.PHARM AND OTHERS USEFUL FOR ACADEMIC TOO. THE PRESENT DATA IS MOST USEFUL FOR PHARMACY PURPOSE.
(This presentation is in .pptx format, and will display well when embedded improperly, such as on the SlideShare site. Please download at your discretion, and be sure to cite your source)
Review of the Hartree-Fock algorithm for the Self-Consistent Field solution of the electronic Schroedinger equation. This talk also serves to highlight some basic points in Quantum Mechanics and Computational Chemistry.
March 21st, 2012
Metadynamics is a computer simulation method in computational physics, chemistry, and biology. It is used to estimate the free energy and other state functions of a system, where ergodicity is hindered by the form of the system's energy landscape.
Molecular Mechanics in Molecular ModelingAkshay Kank
In this slide you learn about the computational chemistry and its role in designing a drug molecule. Also learn concept about the molecular mechanics and its application to Computer Aided Drug Design. difference between the Quantum mechanics and Molecular Mechanics.
THE ENERGY MINIMIZATION, FOR THE STUDENTS OF M.PHARM, B.PHARM AND OTHERS USEFUL FOR ACADEMIC TOO. THE PRESENT DATA IS MOST USEFUL FOR PHARMACY PURPOSE.
(This presentation is in .pptx format, and will display well when embedded improperly, such as on the SlideShare site. Please download at your discretion, and be sure to cite your source)
Review of the Hartree-Fock algorithm for the Self-Consistent Field solution of the electronic Schroedinger equation. This talk also serves to highlight some basic points in Quantum Mechanics and Computational Chemistry.
March 21st, 2012
EE380-4 Course project Experimental determination of a Ser.docxjack60216
EE380-4 Course project
Experimental determination of a Servo-Motor State Space Model
Dr. A. Masoud, Course Project, Posted Thursday, October 8, 2015, Due a week before the end of the semester 151.
Objective: To determine experimentally the state space model and the transfer function of the
control laboratory servo-trainer using physical measurements instead of mathematical
derivations. In addition, to be familiarized with the mathematical tools needed for doing this task
Background: The servo-process you will be examining in the EE380 laboratory is the DC
motor (figure-1). By now, you are familiar with how to model theoretically this process. To do
this, you need to know beforehand all the parameters of the system. These parameters are
usually not readily available. Computing them may be difficult or not possible. Therefore, the
only other alternative is to determine the model of the servo-process experimentally.
Figure-1: Equivalent circuit of a DC motor
The motor used in the laboratory servo-trainer is a permanent magnet DC motor. The field is
generated by the permanent magnet is a constant. This makes the armature voltage (Va) the
only means of control available, i.e the motor is in an armature control mode.
The state of the overall system (X) consists of the state of the electrical part which is the current
in the inductor (the armature current Ia), the angular position ( )θ and the velocity of the
mechanical part (θ ). As for the output, it can be selected as anyone of these variables. This
makes writing the output equation of the state space model of the motor a simple and a
straightforward task. The state vector of the overall system is: X=[ Ia θ θ ]T. However, we are
going to use a simplified model that neglects the electrical component and focus only on the
mechanical one. The state vector of the simplified model is: X=[θ θ ]T. The state equation of
the simplified model in (1)
Va,
dc
ba
⎥
⎦
⎤
⎢
⎣
⎡
+⎥
⎦
⎤
⎢
⎣
⎡
⎥
⎦
⎤
⎢
⎣
⎡
=⎥
⎦
⎤
⎢
⎣
⎡
f
e
θ
θ
θ
θ (1)
Determining the model means that the input (Va(t)), the states (θ (t), θ (t)) and their derivatives
can be directly measured or reliably computed. The parameters (a,b,c,d,e,f) are the unknowns
that need to be determined from the measurements.
To determine the parameters, we first need to change the form of the state space equations into
the form in (2)
,
f
e
d
c
b
a
Va0(t)(t)00
0Va00(t)(t)
(t)
(t)
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎡
⎥
⎦
⎤
⎢
⎣
⎡
=⎥
⎦
⎤
⎢
⎣
⎡
θθ
θθ
θ
θ
(2)
Now take “n” measurements of the states, their derivatives and the input and form the equations
in (3). Determining the time instants (t1, t2,..,tn) at which measurements are to be taken are left
up to the student. However, one should make sure that the measurements are distinct, i.e. do
not take the same measurement more than once. ...
Discretizing of linear systems with time-delay Using method of Euler’s and Tu...IJERA Editor
Delays deteriorate the control performance and could destabilize the overall system in the theory of discretetime
signals and dynamic systems. Whenever a computer is used in measurement, signal processing or control
applications, the data as seen from the computer and systems involved are naturally discrete-time because a
computer executes program code at discrete points of time. Theory of discrete-time dynamic signals and systems
is useful in design and analysis of control systems, signal filters, state estimators and model estimation from
time-series of process data system identification. In this paper, a new approximated discretization method and
digital design for control systems with delays is proposed. System is transformed to a discrete-time model with
time delays. To implement the digital modeling, we used the z-transfer functions matrix which is a useful model
type of discrete-time systems, being analogous to the Laplace-transform for continuous-time systems. The most
important use of the z-transform is for defining z-transfer functions matrix is employed to obtain an extended
discrete-time. The proposed method can closely approximate the step response of the original continuous timedelayed
control system by choosing various of energy loss level. Illustrative example is simulated to demonstrate
the effectiveness of the developed method.\
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2. Revisiting some Familiar Concepts
State of a System: The different
configurations in which a system can exist
Specifying the State of a system: State of a
mechanical system is specified by the
positions and momenta of all particles of
the system
3.
Phase space: a space in which all possible
states of a system are represented, with
each possible state of the system
corresponding to one unique point in the
phase space.
•Every state is a point in the
phase space and the dynamics
of a system can be visualised
as its movement through the
phase space.
5. Characteristics of a good Integrator
Minimal need to compute forces(a very
expensive calculation)
Small propagation error, allowing large
time steps
Accuracy
Conserves energy and momentum
Time reversible
Area preserving(Symplectic)
11. Verlet algorithm: Advantages
Integration does not require the velocities,
only position information is taken into
account.
Only a single force evaluation per integration
cycle. (Force evaluation is the most
computationally expensive part in the
simulation).
This formulation, which is based on forward
and backward expansions, is naturally
reversible in time (a property of the
equation of motion).
13. Verlet Algorithm: Disadvantages
Error in velocity approximation is of the
order of time step squared(large errors).
Need to know r(n+1)to calculate v(n).
Numerical imprecision in adding small and
large numbers.
19. Leap Frog : Advantages
Eliminates addition of small numbers to
large ones. Reduces the numerical error
problem of the Verlet algorithm. Here
O(Δt1) terms are added to O(Δt0) terms.
Hence Improved evaluation of velocities.
Direct evaluation of velocities gives a
useful handle for controlling the
temperature in the simulation.
20. Leap Frog : Disadvantages
The velocities at time t are still
approximate.
Computationally a little more expensive
than Verlet.
26. Verlet Algorithm:Advantages
is a second order integration scheme, i. e. the error
term is O ((∂t)3).
is explicit, i. e. without reference into the future:
The system at time t+∂t can be calculated directly
from quantities known at time t.
is self-starting, i. e. without reference into the far
past: The system at time ∂t can be calculated
directly knowing only the system at time t = 0.
allows ∂t to be chosen differently for each time.
This can be very useful when the accelerations vary
strongly over time.
requires only one evaluation of the accelerations
per timestep.
27. Gear Predictor-Corrector
Algorithm
Predict r(t+dt) from the Taylor expansion
at the starting point.
Using this value of r(t+dt), calculate
ac(t+dt).
Similarly calculate v(t+dt), a(t+dt),b(t+dt)
at that point.
The difference between the a(t+dt) and
the predicted ac(t+dt):
31. Gear Predictor-Corrector Algorithm:
Disadvantages
Not time reversible.
Not symplectic, therefore does not
conserve phase space volume.
Not energy conserving, implies over time
there is a gradual energy drift.
