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