This document contains notes from the first lecture of the MIT course 10.637 (quantum chemical simulation). The key points covered include:
- An introduction to atomistic and quantum chemical simulations and how they can provide insights into materials, catalysts, and chemical systems at the nanoscale.
- An overview of the course content, which will cover classical force fields, electronic structure theory, sampling methods, excited state methods and applications in various fields.
- Details on assignments, grading, and expectations upon completing the course.
- Case studies demonstrating different simulation techniques, including reaction discovery in nanoreactors, modeling protein-ligand binding, predicting singlet fission rates, and computational screening of surface catalysts.
2. MIT
10.637
Lecture 1
Outline
• Introduction to simulations
• Survey
• Course overview: content and
assignments
• Introductions from students: what are you
hoping to learn?
• Case studies in simulations
• XSEDE and lab technical details
3. MIT
10.637
Lecture 1
Atomistic simulations give us a
view of how materials, catalysts, and
chemical systems behave on the
nanoscale (both in time and space).
Quantum chemistry uses
approximations to the Schrodinger
equation to describe the behavior of
electrons around nuclei to give us a
first principles view of chemical
bonding and bond-breaking.
Nanoreactor
simulations of
gas phase
collisions
Properties of salt
solutions confined
in nanotubes
What are simulations?
8. MIT
10.637
Lecture 1
Choosing a computational
model
Empirical models – functional form with parameters
from experimental or other calculated data:
Pair potentials
Many body potentials
Semi-empirical models – model Hamiltonians:
Tight binding
MNDO, AM1…
Quantum mechanical models – approximations to the
Schrödinger equation:
Hartree-Fock
Density functional theory
Post-Hartree-Fock (Configuration interaction, MP2)
Moreefficient
Moretransferable
Best tool for the
job? Depends on
the job!
9. MIT
10.637
Lecture 1
When we need quantum
Potential energy surfaces:
explicit or for force field
development
Bonding and structure:
from first principles
experiment
QM
10. MIT
10.637
Lecture 1
When we need quantum
Interesting phenomena depend on what the electrons are doing!
Optical properties Catalysis
Magnetic
properties
11. MIT
10.637
Lecture 1
A look at the course
Lectures: Tu/Th 4-5:30 PM 26-168
Labs: Tu/Th 4-5:30 PM 14-0637
-see class schedule
Instructor: Professor Heather J. Kulik
Office: E18-558
E-mail: hjkulik@MIT.edu
Office Hours: Thu 3 pm or by appt.
Website:
https://stellar.mit.edu/S/course/10/fa14/10.637/index.html
12. MIT
10.637
Lecture 1
A look at the course
Grading: Homework 80%
Journal article presentation 20%
Undergraduates: Homework only.
Homework: Lab assignments are due one week after
the in course portion.
Policies: Late homework will be accepted up until
the time that solutions are posted but late
submissions will be eligible for at most half
credit.
13. MIT
10.637
Lecture 1
Lectures (18): Cover a
diverse number of topics
in classical and first-
principles simulations.
In class labs (6):
Classical and first-
principles simulations on
high performance
computers. Introduction to
linux and scripting.
Homework (80%):
Six labs with short answers
and demonstration of work.
Independent assignment
(20%): journal article and
simulation at the end of the
course.
No exams.
Meets Tues, Thu 4-5:30pm, Room 26-168 (14-0637 for labs)
Class format
14. MIT
10.637
Lecture 1
Optional background texts
• F. Jensen “Introduction to Computational Chemistry” Wiley
• W. Koch and M. C. Holthausen “A Chemist’s Guide to Density
Functional Theory” Wiley-VCH
• A. Szabo and N. S. Ostlund, “Modern Quantum Chemistry: Introduction to
Advanced Electronic Structure Theory” Dover
• C. J. Cramer “Essentials of Computational Chemistry: Theories and
Models” Wiley
• M. P. Allen and Tildesley “Computer simulations of Liquids” Oxford Science
Publishers
• T. Schlick “Molecular Modeling and Simulation” Springer
• D. A. McQuarrie and J. D. Simon “Physical Chemistry: A molecular
approach” University Science Books
• D. Frenkel and B. Smit “Understanding Molecular Simulations: From
Algorithms to Applications” Academic Press
Specific literature will be provided if necessary on Stellar.
16. MIT
10.637
Lecture 1
• Energy functions (force fields), molecular mechanics,
geometry optimizations, potential energy surfaces,
and classical molecular dynamics
• Theory and application of first-principles computer
simulations methods: Hartree-Fock theory and
density functional theory.
• Sampling methods, ab initio molecular dynamics,
QM/MM, transition-path finding approaches.
• Excited state methods: time-dependent density
functional theory, correlated wavefunction theory,
many-body perturbation theory.
• Discussion of applications in materials science,
biochemistry, and catalysis.
stretch bend torsional
non-bonded
What’s covered?
17. MIT
10.637
Lecture 1
Course layout
1. Classical force fields, geometry optimization and
dynamics (Lab 1 and 2).
2. Electronic structure theory: Hartree-Fock theory
and Density Functional Theory (Labs 3 and 4).
3. Transition-state theory, Ab initio MD, sampling,
and QM/MM (Lab 5 and 6).
4. Advanced wavefunction techniques (TDDFT,
perturbation theory, etc) (Lab 5).
21. MIT
10.637
Lecture 1
Upon completion of this course, you will:
• Be able to assess the relative accuracy and efficiency of
simulations methods and know the applicability of these methods
(system size, elements, properties, etc).
• Be able to read and understand computational/simulations
literature.
• Be able to carry out independent research in computational
catalysis.
• Be versed in several codes spanning classical and first-principles,
biological and nonbiological simulation.
• Have exposure to the commandline, linux, and scripting.
Outcomes
22. MIT
10.637
Lecture 1
Case studies
• Nanoreactor: reaction discovery
• G-Protein coupled receptors
• Predicting singlet fission
• Screening surface catalysts
23. MIT
10.637
Lecture 1
Case study: nanoreactor
We can predict rate constants for simple reactions using a
first-principles, molecular view of how species react.
macroscopic microscopic
Urey-Miller experiment L.P. Wang, et al., submitted to Nature Chem. (2014).
24. MIT
10.637
Lecture 1
Case study: nanoreactor
trr
trr
trr
kmrV
i
ii
0
0
2
0
2
0
Time (ps)
Radius(Å)
r
r’
Dt
t0 t0+Dt
r’
r
How it works, a periodic confining potential
enhances reactivity:
Techniques used:
Ab initio MD with
Hartree-Fock theory.
Plus transition-state
finding approaches:
string method,
nudged elastic band.
28. MIT
10.637
Lecture 1
Case study: G-Protein coupled
receptors
• G-protein-coupled receptors are a
family of membrane-bound a-helical
proteins.
• They regulate physiological
processes by transmitting signals
from extracellular to intracellular.
• GPCRs are good drug targets.
• Specific example b2-adrenergic
receptor (b2AR) is implicated in
diabetes, obesity, and asthma.
• X-ray structures of the active and
inactive states are available.
• Authors wanted to look at the
transition between active and
inactive states.
Techniques used:
Classical molecular dynamics
and Markov state models to
cluster together thousands of
trajectories along with mutual
information analysis and
docking.
K.J. Kohlhoff, et al., Nature Chem. (2014).
30. MIT
10.637
Lecture 1
Case study: G-Protein coupled
receptors
• Used cloud computing to
simulate 2.15 ms of dynamics.
• Run many different simulations
starting from both activated and
inactive forms of the protein.
• Clustered the simulations and
built 150 ms activation
trajectories.
• When activator (agonist) is
present, can stay activated for
periods of 1.5-5 ms
• When inactivator (antagonist) or
apo protein is simulated, then
no activation events.
31. MIT
10.637
Lecture 1
Case study: G-protein coupled
receptors
Mutual information is a method to
identify correlated residues: the
agonist correlates intracellular
and extracellular residues, but
the inverse agonist disrupts
these correlations.
Different molecules dock at different
activation points in MSM model.
32. MIT
10.637
Lecture 1
Case study: singlet fission
Singlet fission (first observed in the 1960s) could allow for solar cells to exceed
100% efficiency and photovoltaics to have power conversion efficiencies of
40%. Occurs in molecular crystals of materials such as pentacene.
Occurs via the mechanism:
Coupling influences the rate:
Energy diagram:
Molecule
A
Molecule
B
Molecule
A
Molecule
B
Molecule
A
Molecule
B
singlet
fission
excited
singlet
triplet
singlet
GS
33. MIT
10.637
Lecture 1
Case study: singlet fission
S.R. Yost et al., Nature Chem. (2014).
Fission occurs very quickly (~80 fs), may be accelerated by charge-transfer
super-exchange.
Weak-coupling regime Strong coupling regime
35. MIT
10.637
Lecture 1
Case study: singlet fission
Techniques used:
Density functional
theory (PBE0/6-31g*),
QM/MM with constraints
and a configuration-
interaction approach.
Good agreement between theory and experiment. Fast fission from Marcus-like
expression predicted for slightly exothermic DG and large coupling.
Fastest endothermic kfis (tetracene – Tc) is 10x slower than slowest exothermic kfis
(DPP). Hc is too exothermic – Marcus inverted region. Tuning Es-2ET good idea for
optimizing singlet fission.
S.R. Yost et al., Nature Chem. (2014).
36. MIT
10.637
Lecture 1
Case study: singlet fission
Pentacene derivatives: V < 20 meV, they are in the non-adiabatic regime. Above
this, they see same fission rate and observe a non-adiabatic to adiabatic transition.
S.R. Yost et al., Nature Chem. (2014).
37. MIT
10.637
Lecture 1
Especially relevant for the chemical engineering discipline:
Case study: catalysis
Techniques used:
Plane-wave density
functional theory with
ultrasoft pseudopotentials
340 eV wavefunction
cutoff/500 eV for the
charge density, RPBE
functional.
Greeley et al., Nature Chem. (2009).
• Computational catalysis screening can identify
new catalytic materials.
• Example: polymer electrolyte membrane fuel
cells (PEMFCs) need faster oxygen reduction
reaction – currently at platinum electrode, but
platinum is expensive.
• ORR catalysts must be stable under corrosive
conditions but must be able to activate O2.
• O2 activation typically occurs via proton and
electron transfer to form OOH before O-O bond
breaking.
• Seek out alloys with Pt or Pd overlayers close to
Pt3X or Pd3X.
38. MIT
10.637
Lecture 1
Case study: catalysis
Greeley et al., Nature Chem. (2009).
Minimizing DG will speed up the reaction. Two rate limiting steps are DG1: proton
transfer and electron transfer for adsorbed OOH and DG2: removal of OH or O from
surface. Both correlate strongly to the stability of O (DEO) on the surface.
39. MIT
10.637
Lecture 1
Case study: catalysis
Greeley et al., Nature Chem. (2009).
• If DEO becomes too positive, then
DG1 increases (bad) but DG2
decreases because it becomes
easier to break Pt-OH and Pt-O
bonds (good).
• These opposing effects lead to a
maximum theoretical turnover
relative to DEO on Pt (DEO
Pt).
• If a surface binds O 0.0-0.4 eV
more weakly than Pt(111), then it
should exhibit ORR activity better
than Pt – optimum at 0.2 eV.
• Focused on stable compounds.
• Correlated to experimental
activities to confirm relationship.
40. MIT
10.637
Lecture 1
Case study: catalysis
Alloying alters the predicted stability of oxygen binding for some cases: 50%
alloying element in 2nd layer (circles) vs 25% alloying element in 2nd layer
(squares). The lighter portion of the graph background indicates optimal activity.
41. MIT
10.637
Lecture 1
XSEDE accounts
Your xsede account should be the same as
your athena username.
This will give you access to computers
where we will run simulations, so it’s
important you do this ASAP!
Point browser to portal.xsede.org
48. MIT
10.637
Lecture 1
Working with athena
• Open terminal and put it into your dock.
• Pick a text editor to use and get
comfortable– nano for most of you.
• Try some basic linux commands.
49. MIT
10.637
Lecture 1
Commandline tips
ls: lists all visible files in the current directory.
Try: ls -ltrh
to view files with long printing (l), last modified sorting (t),
in reverse (r), and human readable (h) file sizes.
Try: ls .*
to view all hidden files that start with ‘.’ such as .bashrc.
Try: ls */*
to view all files in first layer of subdirectories, etc.
Try: ls */ -d
to view all files that match a wildcard with a directory.
50. MIT
10.637
Lecture 1
Commandline tips
cd: change directory.
Try: cd ~/
to change directory to home
Try: cd -
to change directory to the previous one.
Try: cd ../
to move up one directory
51. MIT
10.637
Lecture 1
Commandline tips
mkdir: make a directory.
Try: mkdir -p path/to/directory
to simulatenously make new directory path, with a subdirectory
inside to, with sub-subdirectory inside called ‘directory’
pwd: gets the current working directory.
ln: make a symbolic link for a directory.
Say you have a long source directory like
/usr/local/source/file/compiler/bin/ and want to be able to see its
contents more easily.
Try: ln -s /usr/local/source/file/compiler/bin/ easydir
to make a symbolic link in your existing directory as a
subdirectory called “easydir”.
52. MIT
10.637
Lecture 1
Commandline tips
cp/mv: copy or move files from one place to another. You may want to copy or
move files around from one place to another. Here are some examples of
copying or moving the old file apples.txt to oranges.txt.
Try: cp -i apples.txt oranges.txt
“i” for interactive means that if oranges.txt already exists, it will ask you if you
want to overwrite.
Try: mv -i apples.txt oranges.txt
“i” for interactive means that if oranges.txt already exists, it will ask you if you
want to overwrite the existing oranges.txt.
Try: mv -f apples.txt oranges.txt
“f” forces the move even if an existing oranges.txt is already there.
Try: cp -p apples.txt oranges.txt
“p” means that permissions and timestamps will be preserved.
Try: cp -r apples/ oranges/
“r” means that you’re recursively copying all the files in a directory. You need to
do this if you’re trying to copy a directory.
53. MIT
10.637
Lecture 1
Commandline tips
rm: remove a file.
Much like cp and mv, you can remove files (be
careful with this) using a couple different flags.
Try: rm -i apples.txt
to interactively ”i” remove a file (i.e. get a y/n
statement)
Try: rm -v apples.txt
to get a verbose listing of the files removed.
Try: rm -r apples/
to recursively remove an entire directory.
54. MIT
10.637
Lecture 1
Summary
• Review on your own, more commandline tips:
– http://hjklol.mit.edu/content/bios-203-useful-
commandline-tools
– Or check for cheat sheet on Stellar.
• Make sure you have set up your xsede
account before you leave today!
• Let’s get started next time with molecular
mechanics!
• Any questions?
Water methane ammonia and hydrogen, sparks and quickly found 11 amino acids formed. Similarly we can mix these species in quantum simulations and watch in real time as the species are formed.