che210d principles of modern molecular simulation methodsshell/che210d/... · 2019-09-26 ·...
TRANSCRIPT
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CHE210DPrinciples of Modern
Molecular Simulation Methods
Instructor: M. Scott Shell
Fall Quarter 2019
TuTh 11am-12:15pm in Eng II 1519
www.engr.ucsb.edu/~shell/che210d
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The goals of this course
This course is all about doing.
• designing experiments
• running simulations
• analyzing results
• presenting data
• making movies
• working with existing molecular modeling software tools and online data
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The goals of this course
▪ formulation of molecular models
▪ basic and advanced algorithms for computing thermodynamic and kinetic properties
▪ modern analysis techniques
▪ physical intuition for simulation “experiments”
▪ programming and visualization tools
▪ knowledge of computational issuesand methods for improving efficiency
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What’s required
▪ a basic knowledge of statistical mechanics and molecular physical chemistry
▪ some, but not extensive, programming experience
▪ access to a computer on which you can install (free, open-source) software
▪ NOTE: examples assume Windows PC, but should be portable to other platforms
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Course tracks
▪ normal track
• undergraduate
• 1st year graduate student in any area
• 2nd year+ graduate student NOT involved in computational research
▪ advanced track
• anything other than above
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Recommended course texts
▪ Berend Smit and Daan Frenkel, Understanding Molecular Simulation (2nd edition), Academic Press (2001).
▪ Andrew R. Leach, Molecular Modelling: Principles and Applications (2nd edition), Prentice-Hall (2001).
▪ Mark Tuckerman, Statistical Mechanics: Theory and Molecular Simulation, Oxford University Press (2010)
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Coursework and logistics
▪ readings
▪ short simulation exercises (50% of grade)
▪ final project (50% of grade)
▪ For Tuesday 10/8/19:
• Python and NumPy / SciPy reading
• exercise #1
▪ Office hours?
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Course website
www.engr.ucsb.edu/~shell/che210d
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length scale
time scale
quantum mechanics
classical atomic
coarse-grained molecular
mesoscale
macroscopic or continuum
E = H
bottom-up
top-down
Simulations at different scales
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Topics covered
▪ Ab initio and electronic structure calculations (brief)
▪ Classical semi-empirical force fields
▪ Basic methods for evaluating properties• minimization (structures)
• molecular dynamics (thermo & kinetics)
• Monte Carlo (thermo)
▪ Free energy & phase equilibria methods
▪ Advanced sampling approaches
▪ Multiscale methods and coarse-grained models
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Tools we will use
▪ Python programming language
▪ NumPy and SciPy
▪ Fortran (basics, for numerically intense routines only)
▪ Visualization software (UCSF Chimera)
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Why Python?
▪ free, open source, cross-platform
▪ intuitive, easy to learn, highly legible code
▪ “batteries included” philosophy
▪ very popular
▪ HUGE development community, especially among computational scientists
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www.xkcd.com
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A simple Python program to compute primes
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Python + NumPy + SciPy
▪ NumPy – very fast linear algebra and array routines, random number generation
▪ SciPy – comprehensive and very fast mathematical package with algorithms for things like: integration, optimization, interpolation, Fourier transforms & signal processing, linear algebra, statistics
▪ Python + NumPy + SciPy rivals (exceeds?) commercial packages like Matlab, but is open source
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Why Python + Fortran?
▪ Python alone is slow for raw numerics
▪ Fortran is (probably) the fastest numeric language
▪ Fortran 90 is a modern standard
▪ Much existing shared code in the scientific community is written in Fortran
▪ Fairly simple and easy to learn
▪ Bottleneck routines written Fortran can be imported transparently into Python, almost magically
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Anaconda Python distribution
https://www.anaconda.com/distribution/
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Spyder Python editor (installed with Anaconda)
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UCSF Chimera
Pymol
Molecular Modelling Toolkit
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Can I do this?
▪ No difference between learning a programming language and learning equipment software
▪ Molecular simulation codes are generally not complex software
▪ Many examples / tools / templates available online
▪ Challenge is not so much how to simulate, but what to simulate and what & how to analyze
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Example
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What’s it all good for?
▪ Qualitative frameworks for thinking about molecular processes and mechanisms
▪ Quantitative understanding of different molecular driving forces
▪ Prediction of properties or molecular architectures for engineering design
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Some examples…
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Multiple phases of a simple substance: argon
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A more complex molecule: a protein
dramatization
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A water nanodroplet on a silica surface
simulation by E. R. Cruz-Chu, A. Aksimentiev , and K. Schultenmovie from http://www.ks.uiuc.edu/Gallery/Movies/
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Water transport inside a carbon nanotube
simulation by A. Kolesnikov and coworkersmovie from http://www.anl.gov/Media_Center/News/2005/IPNS050513.html
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Water transport through a protein channel
simulation by E. Tajkhorshid, K. Schulten, Y. Wang, J. Yu, F. Zhu, and M. Jensenmovie from http://www.ks.uiuc.edu/Gallery/Movies/
cell membrane
(not shown)
outside of cell
inside of cell
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Phase separation and equilibria
simulation by A. Delapaz and L. Gelbmovie from http://www.chemistry.wustl.edu/~gelb/gchem/materials/lve/index.html
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Driving forces in small-molecule binding
Young, et al., PNAS, 2007
empty cavity bound biotin
streptavidin binding cavity
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Solvation and binding free energies
simulation by D. Mobley
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Artificial thermodynamic cycles for binding
figure from D. Mobley
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Try out some interactive simulations yourself
www.etomica.org
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𝟑 𝝁𝒎
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electrod
eel
ectr
od
e
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Some early milestones in molecular simulation
▪ 1953: Monte Carlo method applied to hard spheres (Metropolis, Rosenbluth, Rosenbluth, Teller & Teller)
▪ 1954: perturbation approach to free energies (Zwanwig)
▪ 1956: molecular dynamics of hard spheres (Alder and Wainwright)
▪ 1963: computation of the chemical potential (Widom)
▪ 1964: molecular dynamics of liquid argon (Rahman)
▪ 1971: molecular dynamics of liquid water (Rahman & Stillinger)
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Advances in models and algorithms
▪ 1976: optimal estimates of free energy differences (Bennett)
▪ 1976: first simulation of protein dynamics (McCammon et al.)
▪ 1977: non-Boltzmann sampling and artificial ensembles (Torrie & Valleau)
▪ early 1980s: community-developed transferable classical potential models and software suites (CHARMM, AMBER)
▪ 1987-1995: robust & rigorous techniques for predicting phase equilibria (Panagiotopoulos, Wilding, Kofke)
▪ 1989, 1992: generalized, optimal techniques for extracting free energy estimates (Ferrenberg, Swendsen, et al)
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Recent accomplishments
▪ 1997-1999: theory for equilibrium properties from nonequilibrium measurements (Jarzynski, Crooks)
▪ 1998: 1 s simulation of miniprotein folding (Duan and Kollman)
▪ 1999-2002: generalized and extended ensemble methods (Sugita & Okamoto, Wang & Landau)
▪ 2002: water freezing from 6 s simulation (Matsumoto et al.)
▪ 2002-2003: massive distributed computing for small protein folding (Folding@Home, Pande et al.)
▪ 2004: design of an entirely new protein fold (Baker et al.)
▪ 2010 - present: emergence of GPU-based molecular dynamics
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System size versus time
K.Y. Sanbonmatsu and C.S. Tung, “Performance computing in biology: Multimillion atom simulations of nanoscale systems,” J. Structural Biology, 157, 470 (2007)
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Moore’s law
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Today’s supercomputers are PC clusters
Summit Supercomputerat Oak Ridge
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Current top supercomputers
www.top500.org
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Growth of simulation power
▪ 107 increase in single processor speed since 1977
▪ 20-500 further increase due to parallelization
▪ 104 – 106 further increase due to algorithms
▪ NET: 13-15 orders of magnitude improvement
▪ BUT: still orders of magnitude behind reality (longest molecular dynamics simulations are ~100s s)
adapted from K. Gubbins at http://chumba.che.ncsu.edu/che596m/
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Secrets to modeling (AKA, the hard parts)
▪ Develop a molecular model capable of capturing the behavior of interest
• scaling laws? basic driving forces? molecular structures? quantitative predictions?
▪ Use a simulation approach that addresses the physics of interest and any bottlenecks / challenges
• long time scales? pathways? specific interactions?
▪ Connect results to statistical-mechanics
• free energies? phase behavior?
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This week and next
▪ Review of probability and statistical mechanics (brief)
▪ Introduction to Python, NumPy, and SciPy(mostly through reading)
▪ Ab initio methods
▪ Classical semi-empirical models
▪ Exploring the potential energy landscape
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Pay attention to Pythonic coding styles
TASK: determine if a string is in an array
arr = ["apples", "oranges", "bananas", "grapes"]
s = "cherries"
found = False
size = len(arr)for i in range(0, size):
if arr[i] == s:
found = True
VERSUS
arr = ["apples", "oranges", "bananas", "grapes"]
found = "cherries" in arr
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Pay attention to Pythonic coding styles
TASK: find the centroid of a set of coordinates
Centroid = numpy.zeros(3)
n = len(Pos)
for i in range(0, n):
Centroid = Centroid + Pos(i,:)
Centroid = Centroid / n
VERSUS
Centroid = numpy.mean(Pos, axis=0)
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Fig. 5. Engine efficiency vs. compression ratio. The graph plots
two temperature cases, showing that a higher temperature provides a
slight increase in efficiency at elevated compression ratios.
0
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eff
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ncy
compression ratio
Engine efficiency versus compression ratio
400 K
500 K
Produce professional graphs sized appropriately
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Fig. 5. Engine efficiency vs. compression ratio. The graph plots
two temperature cases, showing that a higher temperature provides a
slight increase in efficiency at elevated compression ratios.
0
0.1
0.2
0.3
0.4
0.5
0.6
0 2 4 6 8 10 12
eff
icie
ncy
compression ratio
Engine efficiency versus compression ratio
400 K
500 K
outer border
gridlines
legend outside
bad data rangegray axes and missing full box
tic marks on outside
text too small
data points and error bars not shown (if applies)
title
standard width is 3.25 inches
Produce professional graphs sized appropriately
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0.0
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0 1 2 3 4 5 6 7 8 9 10 11
effi
cien
cy
compression ratio
500 K
400 K
Fig. 5. Engine efficiency vs. compression ratio. A higher
temperature provides a slight increase in efficiency at elevated
compression ratios.
Produce professional graphs sized appropriately
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Do me a favor
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