from classroom to collaboration: crossing computational and classic chemistry
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From Classroom to Collaboration:Crossing Computational and Classic ChemistryJohn Harkless, Howard University Department of Chemistry
For HPC & Cyberinfrastructure Campus Bridging Workshop
June 22, 2009
Computational Chemistry:Standard Model
Co-located, individual research groups
PI-driven focus on specific topics
Considerable investment in algorithm & methods development
Computational Chemistry:Standard Model
PI-driven research themes attract experimental collaborations
Focus is often on applications as proof of algorithmic concept
Sustainability requires consistent access to computational personnel
Computational Chemistry:Collaborative Model
Lesser focus on methods development in favor of broader application of methods
Limited number of purely computational collaborators
Increased reliance on creative coalition building and offsite resource acquisition
Computational Chemistry:Collaborative Model
Limited computational code development
Greater use of pre-existing codes
Dependence on broader training and development of novice/amateur user base
Overview
Developing potential collaborations
Overarching research themes
Applications and research results
Developing Collaborators:Coursework
Computational Methods in Chemistry
Uses technology classroom
Focus on computational project design
Students propose modeling-based solutions to pre-existing experimental problems
Developing Collaborators:Advertising
Development of computational science primers
Presentations of various classes of results
Modeling and simulations service
Human Costs in Computational Chemistry
Research Themes:Human Costs in Computational Chemistry
Quantum chemists excel at estimating scaling and costs of algorithms
There is minimal effort in determining the costs and complexity in training users
Commercial codes may obscure meaning to promote ease of use
Research Themes:Human Costs in Computational Chemistry
“How long before a student becomes useful?”
Simplification of high-end techniques without sacrificing quality of result
Investigation of the limits of procedural calculation versus targeted design of calculations
Pros:
Widespread use of techniques
Relative ease of use
Always gets a number as output
Cons:
Often promotes misconceptions
Usually no error estimation
Always gets a number as output
Black Box Computing:Human Costs in Computational Chemistry
Pros:
End results are well-analyzed
Consistently superior results
Cons:
Expensive (human, not CPU) cost
Not for everyone
State-of-the Art Computing:Human Costs in Computational Chemistry
QMC calculations have a significant degree of “art”, due to the lack of strict restriction on trial function form.
How much trial function “artistry” is necessary to retain accuracy for “difficult” systems?
This leads us to “Golden Box” computing.
“Golden Box” Computing:Human Costs in Computational Chemistry
“Golden Box” Computinguses generalized forms of high level techniques.
Accuracy
Exp
ertis
e
Black Box
State ofthe Art
Golden Box
Quality With Less ComplexityHuman Costs in Computational Chemistry
Investigation of benefits and liabilities of procedurally generated wavefunctions.
Application of general, simple rules for wavefunction optimization and correlation.
Basic wavefunction forms include Hartree-Fock and CISD, with CASSCF at the highest level.
Qualitative Electronic Structure
Research Themes:Qualitative Electronic Structure
Three practical timescales for service-oriented computational chemistry:
“Over coffee, over lunch, or overnight.” -Anne M. Chaka
Aiding chemical intuition without full, explicit quantum mechanical treatment
β-ketoimines Qualitative Electronic Structure
Have been used as precursors for the formation of metal oxides
Metal oxides can have interesting optical properties
Easier, more optimal means of creating metal oxides desired
Potential ligands for catalytic processes
Trends in different side chains?Qualitative Electronic Structure
Properties of interest include
Polarization of molecule
Charge distribution over molecule
Steric effects ( qualitative)
Classification of structuresQualitative Electronic Structure
13 structures into 4 groups
Linear alkyls ( 3 - 6 C’s)
Nonlinear 20 alkyls (3 - 5 C’s)
Cyclic - cyclopentyl
Cyclic - conjugated
Linear Alkyl GroupsQualitative Electronic Structure
Nonlinear 20 AlkylsQualitative Electronic Structure
CyclopentylQualitative Electronic Structure
Dipole moment 6.0550
Charge on O , N sites similar to other structures (-0.533, 0.294)
Larger side chain likely to be less favorable sterically
Quantitative Electronic Structure
Deviation from experiment, eV
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Li 2P Be 3P Be 1P B 4P C 1D C 1S N 2D N 2P O 1D O 1S F 4P
B3LYP MP2 CCSD CCSD(T) DMC
Research Themes:Quantitative Electronic Structure
Explicit quantum mechanical treatment of systems with “difficult” electronic features:
Unpaired spins - high/low spin, open shells
Electronic excited states
Metallic and/or multi-reference character
Tetrasulfur (S4)Quantitative Electronic Structure
Tetrasulfur (S4) is of interest to researchers in the atmospheric and interstellar sciences, and exists in a double-well potential.
The global minimum (C2v) and saddle point (D2h) structures are the defining points of the potential.
Theoretical approaches produce significant qualitative differences for the cis-planar (C2v) and rectangular (D2h) structures.
Estimation of two dissociation pathways and total atomization requires description of open and closed shell species.
Estimates of electronic excitations of S4 and daughter species adds to the overall picture.
Tetrasulfur (S4)Quantitative Electronic Structure
Tetrasulfur (S4)Quantitative Electronic Structure
3
S + S3 (3B2)
4
S + S3 (1A1)
5
4 S (3P)
2
2 S2 (3Σg)
1
S4 (1Ag), D2h
S4 (1A1), C2v S4 (1A1), C2v
S4 has a bound LUMO, necessitating multireference trial functions.
Asymmetric dissociation requires equivalent multireference treatment of correlation.
Procedural wavefunction design appears to improve DMC significantly more than VMC.
Tetrasulfur (S4)Quantitative Electronic Structure
Dr. J. Francisco (Tetrasulfur)
Dr. J. Matthews (β-ketoimines)
Dr. K. Scott (Drug Design)
Dr. S. Smith (CLDC)
CheTaH Group- Dr. W. Hercules, Dr. A. Gibson, Mr. F. Fayton, Mr. G. Taylor
AcknowledgementsHumans in Computational Chemistry