Molecular dynamics simulation is a computational method that analyzes the physical movements of atoms and molecules over time. It works by calculating the acceleration, position, and velocity of atoms in a system using Newton's laws of motion. The forces between atoms are determined from interatomic potential functions, and initial atom velocities are assigned randomly based on temperature using the Boltzmann distribution. The simulation is iterated in small time steps to track how atom positions, velocities, and accelerations change over time. This provides insights into molecular structure, function, and interactions at the atomic scale.
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.
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.
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.
Computational Chemistry aspects of Molecular Mechanics and Dynamics have been discussed in this presentation. Useful for the Undergraduate and Postgraduate students of Pharmacy, Drug Design and Computational Chemistry
Lecture: Interatomic Potentials Enabled by Machine LearningDanielSchwalbeKoda
Lecture for the 4th IKZ-FairMAT Winter School. Describes recent advances in neural network interatomic potentials, deep learning models accelerating quantum chemistry, and more.
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.
Von Neumann worked on the cellular automata in in late 1940’s and 1950's as an abstraction of self replication. Von Neumann's ideas of propagation of information from parent cells to next cycles in a cellular automaton, could be an explanation of the geometry of space-time grid, limitation on the speed of light, Heisenberg’s Uncertainty principle, principles of Quantum theory, Relativity, elementary particles of physics, why universe is expanding,… and the list goes on. If this simple mechanism could explain so many things, why was it not a prominent field of research? The answer is very simple but at the same time quite unexpected: one of the applications of this post war study of Von Neumann on Cellular Automata was cryptography; therefore his results were classified and still kept as top secret by USA government. However it’s time to look at this subject from a different point of view: Can this mechanism be used to explain the physical universe? we are more interested in the secret of existence than encryption-decryption of text or data; where did all these galaxies, stars, cosmological objects come from, when did it start, what it was like at the beginning of time and space
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.
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.
Computational Chemistry aspects of Molecular Mechanics and Dynamics have been discussed in this presentation. Useful for the Undergraduate and Postgraduate students of Pharmacy, Drug Design and Computational Chemistry
Lecture: Interatomic Potentials Enabled by Machine LearningDanielSchwalbeKoda
Lecture for the 4th IKZ-FairMAT Winter School. Describes recent advances in neural network interatomic potentials, deep learning models accelerating quantum chemistry, and more.
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.
Von Neumann worked on the cellular automata in in late 1940’s and 1950's as an abstraction of self replication. Von Neumann's ideas of propagation of information from parent cells to next cycles in a cellular automaton, could be an explanation of the geometry of space-time grid, limitation on the speed of light, Heisenberg’s Uncertainty principle, principles of Quantum theory, Relativity, elementary particles of physics, why universe is expanding,… and the list goes on. If this simple mechanism could explain so many things, why was it not a prominent field of research? The answer is very simple but at the same time quite unexpected: one of the applications of this post war study of Von Neumann on Cellular Automata was cryptography; therefore his results were classified and still kept as top secret by USA government. However it’s time to look at this subject from a different point of view: Can this mechanism be used to explain the physical universe? we are more interested in the secret of existence than encryption-decryption of text or data; where did all these galaxies, stars, cosmological objects come from, when did it start, what it was like at the beginning of time and space
The folding of proteins is an important biological process that determines the structure, role and functionality of proteins. It is often studied by molecular dynamics (MD) simulations, in order to obtain the folding trajectory of all the atoms in the system.
To date, pure MD simulations require huge computational resources and are still unable to access the timescales of folding processes that have biological relevance.
In my work, I am exploiting machine learning techniques and one recent AI milestone, Deepmind’s Alphafold, in order to create an advanced algorithm able to explore the folding trajectories within short computational times. It becomes possible to extract atomistic conformations from the folding pathways, and identify folding intermediates and long-lived states.
This method can be used to facilitate the identification of biologically relevant protein conformations, later to be used for pharmacological targeting or biophysical studies.
Fi ck law
Diffusion: random walk of an ensemble of particles from region of high “concentration” to region of small “concentration”.
Flow is proportional to the negative gradient of the “concentration”.
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.
Quantum computing is the area of study focused on developing computer technology based on the principles of quantum theory. The quantum computer, following the laws of quantum physics, would gain enormous processing power through the ability to be in multiple states, and to perform tasks using all possible permutations simultaneously.
Chemical dynamics and rare events in soft matter physicsBoris Fackovec
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This series of predictable changes that occurs in a community over time is called ecological succession.In another words, Ecological succession is the change in species composition over time; that is, the replacement of one group of species by another group of species. There are two major types of ecological succession: primary succession and secondary succession. In primary succession, a site that is initially absent of species becomes colonized for the very first time. In secondary succession, a site that supports an existing assemblage of species experiences a disturbance that changes the composition of species. Both types of succession can occur in terrestrial and aquatic ecosystems. The first species to arrive and colonize the newly formed habitat are pioneer species. These early colonizers contribute nutrients to the soil through organic matter accumulation from decomposition. Some early successional plant species can fix atmospheric nitrogen and thereby increase nitrogen availability in the soil for other plants. As soil nutrients increase over succession it allows for the colonization of previously nutrient-limited species that were unable to establish initially. This facilitates the turnover in species composition over time. In terrestrial ecosystems, this compositional shift corresponds to a change in life forms and distinct species assemblages transitioning from small herbaceous plants, to shrubs, and ultimately to stands of trees over the course of succession. Secondary succession in terrestrial ecosystems can initiate after fire, tornadoes/ hurricanes, or humans disturb an already established plant community, removing most species but leaving the soil intact. The disturbance changes exposure of the habitat to sunlight, wind, and water that alters colonization and the assemblage trajectory of the new plant community. Some plant species may arrive to the disturbed site from
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.
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.
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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.
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.
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
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.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
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2. Computational method for analyzing the
physical movements of atoms and molecules
Molecular Dynamics Simulation
Relating to molecules Study of Motion Imitation of a situation or
process
2
3. Computational method for analyzing the physical movements of atoms and molecules
Molecular Dynamics Simulation
Relating to molecules Study of Motion Imitation of a situation or process
Molecular
System
= Atom at initial
stage
= Atom after dT time
3
4. Computational method for analyzing the physical movements of atoms and molecules
Molecular Dynamics Simulation
Relating to molecules Study of Motion Imitation of a situation or process
Molecular
System
= Atom at initial
stage
= Atom after dT time
What do We Want to Know?
1. Acceleration (a)
2. Position (r) and
of Atom “X”
after time dT
Acceleration is a vector. Thus,
direction of a moving particle also
can be known from acceleration.
4
5. Computational method for analyzing the physical movements of atoms and molecules
Molecular Dynamics Simulation
Relating to molecules Study of Motion Imitation of a situation or process
Molecular
System
= Atom at initial
stage
= Atom after dT time
What do We Want to Know?
1. Acceleration (a)
2. Position (r) and
of Atom “X”
after time dT
How
Newton’s Second Law:
5
6. Computational method for analyzing the physical movements of atoms and molecules
Molecular Dynamics Simulation
Relating to molecules Study of Motion Imitation of a situation or process
Molecular
System
= Atom at initial
stage
= Atom after dT time
What do We Want to Know?
1. Acceleration (a)
2. Position (r) and
of Atom “X”
after time dT
How
Newton’s Second Law: How to Find the Force (F)?
The potential energy is negative
the integral of the force:
6
7. Molecular
System
= Atom at initial
stage
= Atom after dT time
What do We Want to Know?
1. Acceleration (a)
2. Position (r) and
of Atom “X”
after time dT
How
Newton’s Second Law: How to Find the Force (F)?
The potential energy is negative
the integral of the force:
For More: https://scripts.mit.edu/~srayyan/PERwiki/index.php?title=Module_7_--_Force_and_Potential_Energy
How to Use this Relationship between
Force (F) and Potential Energy (dU)?
7
9. How to Use this Relationship between
Force (F) and Potential Energy (dU)?
By Using Interatomic Potentials. Interatomic potentials are mathematical functions for
calculating the potential energy of a system of atoms with given positions in space.
Interatomic potentials can be written as a series expansion of functional terms
that depend on the position of one, two, three, etc. atoms at a time.
Then the total potential of the system (Vtot) can be written as
For More: https://en.wikipedia.org/wiki/Interatomic_potential
Where are the Interatomic Potentials in Molecule?
9
10. Where are the Interatomic Potentials in Molecule?
A single atom will be affected by the potential energy functions of every atom in the system:
• Bonded Neighbors
• Non-Bonded Atoms (either other atoms in the same molecule, or atoms from different molecules)
10
11. Where are the Interatomic Potentials in Molecule?
A single atom will be affected by the potential energy functions of every atom in the system:
• Bonded Neighbors
• Non-Bonded Atoms (either other atoms in the same molecule, or atoms from different molecules)
How to Calculate these HORRIBLE
Interatomic Potentials? 11
12. How to Calculate these HORRIBLE Interatomic Potentials?
Many research group already developed different Force Field
to calculate all these interatomic potential equations
Force field refers to the functional form and parameter sets used
to calculate the potential energy of a system of atoms in
molecular mechanics and molecular dynamics simulations.
For More: https://en.wikipedia.org/wiki/Force_field_(chemistry)
Example of Force Field: AMBER, CHARMM, OPLS, GROMOS
Things to Explore: When to use which Force Field?
Hint: People believe CHARMM forcefield is better for proteins while AMBER
forcefield is better for DNA simulations. (Don’t Depend on People!)
12
13. Wait!
At the Starting of the Simulation (When time, t=0),
we know nothing about the atoms (acceleration, potentials) other than their positions. Then
how to calculate the potentials of the molecule after dT time?
Remember:
Solution:
Boltzmann Distribution is used to
assign random potentials to all atoms
that give total potentials of the system
at a certain temperature.
For More: https://en.wikipedia.org/wiki/Boltzmann_distribution 13
15. DONE!
We have learnt the basic theory about
the Molecular Dynamics Simulation. ☺
15
16. DONE!
We have learnt the basic theory about
the Molecular Dynamics Simulation.
☺
16
Iterate the Process from Step2. to know
the new position and acceleration after time dT+δ
Now you have new position and acceleration for the atom “X” after time dt
Step 2. Acceleration Calculation
a. Interatomic Potential
Calculation
b. Force Calculation
Step 1. Initial Stage
a. Initial Position b. Boltzman Distribution
Molecular Dynamics Simulation
a. Acceleration b. Position
Just A Recap
17. Why?
•Imagine that an alien lands on Earth, hears about something called a
‘‘bicycle,’’ and wants to understand how it works, how to ride it, and
how to fix it when it breaks.
17
18. Why?
•A molecular biologist trying to understand how a protein or other
biomolecule works faces a similar challenge.
An atomic level structure is
tremendously helpful
The atoms in a biomolecule
are in constant motion. Both
molecular function and
intermolecular interactions
depend on the dynamics of
the molecules involved
Unfortunately, watching the
motions of individual atoms
and perturbing them in a
desired fashion is difficult
attractive alternative is to
work with an atomic-level
computer simulation of the
relevant biomolecules
18
20. Binding of Drugs to their Molecular Targets
20
Microsecond to millisecond time frame (This one shows the binding of imatinib, a tyrosine kinase
inhibitor that’s now been approved by the FDA under the name Gleevec, to the BCR/ABL fusion
protein.) Image: Nagar, et al., Cancer Res. 62, 4236 (2002)
22. Practical Considerations in Using MD Simulations
1. Cutoff Methods
• Ideally, every atom should interact with every other atom. This creates a force calculation algorithm of
quadratic order. We may be able to ignore atoms at large distances from each other without suffering
too much loss of accuracy
• Periodic boundary conditions (PBCs) are a set of boundary
conditions which are often chosen for approximating a large
(infinite) system by using a small part called a unit cell.
• PBCs are often used in computer simulations and
mathematical models.
3. Periodic Boundary Conditions (PBCs)
2. Molecules in Solution
• In real situations, a molecule is rarely isolated. In biological systems,
proteins, RNA, and DNA are immersed in a sea of water molecules
• To accurately portray the effect of the solvent molecules on a system,
the solvent molecules must be free flowing.
• How do we establish computational boundaries while keeping a
realistic solvent simulation? (Ans: PBCs)
22
23. Canonical ensemble or NVT ensemble: a statistical ensemble where the energy is not known exactly
but the number of particles is fixed. In place of the energy, the temperature is specified.
The canonical ensemble is appropriate for describing a closed system.
A statistical ensemble is a collection of various microstates of an equilibrium macroscopic system
as determined by the constraints operating on the system. The choice of ensemble is dictated by
the nature of the physical system under consideration and properties to be computed.
Statistical
Ensemble
Practical Considerations in Using MD Simulations
23