Computer-Aided Molecular Modeling of Materials Instructor: Yun Hee Jang ([email protected], MSE 302, 2323) TA: Eunhwan Jung ([email protected], MSE 301, 2364) Web: http://mse.gist.ac.kr/~modeling/lecture.html Reference: - D. Frenkel & B. Smit, Understanding molecular simulations, 2nd ed. (2002) - M. P. Allen & D. J. Tildesley, Computer simulation of liquids (1986) - A. R. Leach, Molecular modeling: principles and applications, 2nd ed. (2001) - and more Grading: - Homework: reading + 0.5-page summary - Exam or Term report: Mid-term & Final - Hands-on computer labs (report & presentation)
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Reference:- D. Frenkel & B. Smit, Understanding molecular simulations, 2nd ed. (2002)- M. P. Allen & D. J. Tildesley, Computer simulation of liquids (1986)- A. R. Leach, Molecular modeling: principles and applications, 2nd ed. (2001)- and more
- Grand Canonical (GCMC) or Kinetic (KMC) - Flue gas diffusion & Selective CO2 capture
Example of multi-scale molecular modeling:CO2 capture project
solvent (PzH2)
PzH2+-CO2
-
PzH-CO2H
PzH2 (regener)PzH2
+CO2-
PzH3+
PzHCO2-
+CO2
+PzH2
+PzH2
PzH3+
Pz(CO2)22-
PzH3+-CO2
PzH+-2CO2-+CO2
+CO2
Piperazine
PzH3+
HCO3-
HN NH
10.6 kcal/mol(MEA)
7.8O
HN
C
7
Step 1: Quantum:Reaction
Quantum simulation Example No. 2: Pd 촉매 반응 , UV/vis spectrum 재현 , 유기태양전지 효율 저하 설명
-50
-40
-30
-20
-10
0
10
Rel
ativ
e fr
ee e
nerg
y (k
cal/m
ol)
TS1 I1 TS2 I2 TS3 I3Pd+ 22BI
08.9
-32.4
-18.4
-34.7-29.1
-51.3
-26.5
Pd+ +2BI
gone!
PCE 3.1%
PCE 0.4%
-3.26
-5.22
-1.96
EX23.26 EX1
1.96
-2.12
-5.11
EX12.99
NN NN PdPd
H
H
NN
Pd
H
+NN
Pd+
NN NN PdPd
H
H
NN
Pd
H
+NN
Pd+
200 400 600 800 10000.0
0.5
1.0
1.5
2.0
2.5
Osc
illa
tor
stre
ng
th (f)
SS
NS
N
n1
What quantum/classical molecular modeling can bring to you: Examples.Reduction-oxidation potential, acibity/basicity (pKa), UV-vis spectrum, density profile, etc.
J. Phys. Chem. B (2006)
J. Phys. Chem. A (2009, 2001), J. Phys. Chem. B (2003),Chem. Res. Toxicol. (2003, 2002, 2000), Chem. Lett. (2007)
cm-1J. Phys. Chem. B (2011), J. Am. Chem. Soc. (2005, 2005, 2005)
Step 2: Classical: 2-species (AMP and PZ) distribution in water
Which one (among AMP and PZ) is less soluble in water?Which one is preferentially positioned at the gas-liquid interface?Which one will meet gaseous CO2 first? Hopefully PZ to capture CO2 faster, but is it really like that?Let’s see with the MD simulation on a model of their mixture solution!
제일원리 다단계 분자모델링
► 물질구조 분자수준 이해 ► 선험적 특성 예측 ► 신물질 설계 ► 물질특성 향상
2. 고전역학 분자동력학 모사 ( 컴퓨터 구축 102~107 개 원자계의 뉴턴방정식 풀기 )
- 전자 무시 , ball ( 원자 ) & spring ( 결합 ) 모델로 분자 /물질 표현 ( 힘장 )- Cheap ► 대규모 시스템에 적용 , 시간 /온도에 따른 구조 /형상 변화 모사
1. 양자역학 전자구조 계산 ( 컴퓨터 구축 101~103 개 원자계 슈레딩거방정식 풀기 )
- 정확 , 경험적 패러미터 불필요 , 제일원리계산 , but expensive ► 소규모 시스템
MULTISCALE
MODELING
MDatomistic molecular
QMelectronic structure
KMCcharge- transpor
t
CGMDcoarse- grained
FF
snapshotCG-FF
nanoscalemorphology
transport parameter
understandingnew designprediction
testvalidation
EXPERIMENTsynthesis
fabricationcharacterizati
on
First-principles multi-scale molecular modeling
I. 2013 Spring: Elements of Quantum Mechanics (QM) - Birth of quantum mechanics, its postulates & simple examples
Particle in a box (translation) Harmonic oscillator (vibration) Particle on a ring or a sphere (rotation)
II. 2013 Fall: Quantum Chemistry - Quantum-mechanical description of chemical systems
III. 2014 Spring: Classical Molecular Simulations of materials - Large-scale simulation of chemical systems (or any collection of particles)
Monte Carlo (MC) & Molecular Dynamics (MD)
IV. 2014 Fall: Molecular Modeling of Materials (Project-oriented class) - Application of a combination of the above methods to understand structures, electronic structures, properties, and functions of various materials
Lecture series I-IV: Molecular Modeling of Materials
P
T
A typical experiment in a real (not virtual) space
1. Some material is put in a container at fixed T & P.
2. The material is in a thermal fluctuation, producing lots of different configurations (a set of microscopic states) for a given amount of time. It is the Mother Nature who generates all the microstates.
3. An apparatus is plugged to measure an observable (a macroscopic quantity) as an average over all the microstates produced from thermal fluctuation.
P
T
How do we mimic the Mother Nature in a virtual space to realize lots of microstates, all of which correspond to
a given macroscopic state?
How do we mimic the apparatus in a virtual space to obtain a macroscopic quantity (or property or
observable) as an average over all the microstates?
P
T
microscopic states (microstates)or microscopic configurationsunder external
constraints (N or , V or P, T or E,
etc.) Ensemble (micro-canonical,
canonical, grand canonical, etc.)
Average over a collection
of microstates
Macroscopic quantities (properties, observables)• thermodynamic – or N, E or T, P or V, Cv, Cp, H, S,
G, etc.• structural – pair correlation function g(r), etc.• dynamical – diffusion, etc.
These are what are measured in true experiments.
they’re generated naturally from thermal fluctuation
In a real-space experiment
In a virtual-space simulation
How do we mimic the Mother Nature in a virtual space to realize lots of microstates, all of which correspond to
a given macroscopic state? By MC & MD methods!
it is us who needs to generate them by QM/MC/MD methods.