Selected papers from Journal of Computational Chemistry

February, 2013 – July, 2013

Copyright © 2013 Wiley Periodicals, Inc., A Wiley Company

Edited By: Charles L. Brooks III, Masahiro Ehara, GernotFrenking, and Peter R. Schreiner

Impact Factor: 4.583

ISI Journal Citation Reports © Ranking: 2011: 26/154 (Chemistry Multidisciplinary)

Tibor, again.

• Water-oxidation catalyst Crabtree et al.• Huge => only DFT for reaction exploration• DFT (&HF) methods known to have little

basis set dependence (converge at small BS)

• Not true for transition metal compoundsEg. 6-31G** underestimates reaction barrier badly

• Reaction

– reactant complex(RC)

– transition state(TS)

– troduct complex(PC)

• Basis set

– Slater type orbital (STO)

– Gaussian type orbital (GTO)

• Methods

– CCSD on a simpler model compound to have a reference

– Various DFT methods: with Grimme’s dispersion correction (-D3) and without

• Codes

– for GTOs:Turbomole, NWChem

– For STOs:ADF2012

• Basis set functions: radial*angular

– radial:spherical harmonics

• Slater type orbitals (STO)

– H-atom like orbitals

– No radial nodes

– Exponential decay at long range

– Kato’s cusp condition at short range

– Computationally difficult->overlap int.

• Gaussian type orbitals(GTO)

– Easier to calculate

– problem: no cusp=> we need more of

them to approximate reality

• notation: Ir/other atoms basis

• near-limit basis sets for reference

• QZ4P/TZ2P

• Def2-QZVPP

• at least triple zeta required for both Ir and other atoms to reach

convergence in activation energy

• DZ/6-31G** badly underestimates

reaction barrier

• G03 and Turbomole results are not matching, at small basis set the results are very sensitive to small changes

Simplified model molecule

• in ordet to be able to do a higher level reference calculations

• validating:

• B3LYP-D3 full geom optimization

• good match of RC, intermediate and TS structures

• energy reference: single point CCSD,

frozen core at HF level at the DFT optimized geometries

B3LYP: faster convergence with STO’s

B3LYP:TZP/TZP and larger are reliable !

Dispersion correction has little effect on relative energies in gas-phase (but improves on absolute)

B3LYP, M06-L, M06,TPSSh is okay, M06-2X is bad

• Absorption of small and macromolecules on surfaces is very important

• Commonly used force-fields are parameterized not forinterfacial systems, but for bulk liquid and solid phases

• Dual-scale modelling approach1.Quantum chemical calculations

2. force field parameterisation and classical MD

– Surface site specific absorption energies

– Extensive sampling of near-surface conformations:

• Not done in the past

• Distance, position, orientation of the molecule sampling=> reliable FF

• Phase-space exploration and fitting strategies to obtain representative and reliable force field are recommended.

Dual-scale modeling approach

• HF, post-HF, DFT, DFT-based finite electronic temperature MD

• Geometries used for param.

– distance dependent conformations

– site-dependent conformations

– Single conformation

• Energy is highly distance, site and orientation dependent!!

Force field

• chemisorption: electron density overlap, charge transfer

Morse: good for aromatic molecules on metals

• Physisorption: LJ6-12:hard repulsive

Buckingham: soft repulsive

• Electrostatic: important in metal-oxide systems,

dominated by Coulomb: charges on surface atoms

• Polarizability can be important

• QC calculation is expensive, we need small, but represen-tative sampling to reconstruct the adsorption landscape

• Vertical, lateral scans

• which scan is more important depends on the system and aim.

• balanced mixture of low- and high-energy conformations are needed

• distance dependence of the adsorption energy, and the molecule’s position and orientation with respect to the surface are also important.

• local optimization methods require educated guess

• Global search recommended: genetic algorithms

• start with small number of reference points

• iterative, self-consistent fitting: do until classical MD will predict the same phase space as the reference points used for fitting

• gradual increase the number of reference points

• classical MD accelerates phase-space exploration: can overcome barriers, can suggest new local minima that cannot be quickly found by QC. They should be validated by QC.

• ReaxFF force field

– Bond, lone-pair, over and under-coordination of atoms with respect to their valency, valence angle (3body), torsion, penalty energy to stabilize 3body system with center atoms with 2 double bonds, conjugated double bonds( 1=2-3=4) 3 and 4 body conjugation terms,H-bond.

• vdW interaction=distance corrected Morse

– Dij:interaction E, rvdw::vdW radius

+empirical parameters

– f13: Shielding to reduce 1-3 repulsion

• Shielded Coulomb interaction, polarization

• Bond order (always between 0-3) is calculated between any two atoms: distance and bond dependent

• All bonded interactions will be scaled with bond order=> bond breaking and bond forming is described naturally.

• All nonbonded interactions are switched off in a smooth mannerat the cutoff distance (seventh order polynomial)

• ReaxFF: ~100 parameters/atom: • many of them with no physical meaning: no educated guess can be

made on initial values=> local search is bad• Training set: bond lengths, angles, torsion angles, potential energy

changes, heat of formations, etc

• The σ values used to scale different types of errors.• Monte Carlo Metropolis (MMC) – Simulated annealing: GLOBAL

– random changes in parameters with probabilistic acceptance (Pa)– starting at high temperature and slowly reducing it

• Comparing with parabolic search algorithm: LOCAL– One parameter optimized at a time by taking 3 values of it and fitting a

parabola to the errors.– Parabola min position is estimated, or if it is ∩ than the lowest of three

Training set

• DFT: ADF code PW91-GGA functional

• MgSO4.xH2O x=0,1,..,6 DFT structures.

• Binding energy curves of H2O on MgSO4

(100) surface

• Various H+ transfer pathways

• Experimental equation of state(T=0K,E=E(V)) x=4,7

Comparison of a the two algorithms

• Five random starting force fields

• MMC-SA is usually much-much better

• Acceptance probability can be adjustedwith scaling the param. perturbations

• Slow rate of cooling, appropriateperturbation scaling with low acceptanceprobability is better.

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