** Reference to paper:

J. Chem. Theory Comput., 2016, 12 (6), pp 2583–2597

** DOI:

10.1021/acs.jctc.6b00160

** Title:

Diffusion Monte Carlo for Accurate Dissociation Energies of 3d Transition Metal Containing Molecules

** Authors:

Katharina Doblhoff-Dier, Jörg Meyer, Philip E. Hoggan, Geert-Jan Kroes, and Lucas K. Wagner

** Contact e-mail:

k.doblhoff-dier@umail.leidenuniv.nl

** Abstract:

Transition metals and transition metal compounds are important to catalysis, photochemistry, and many superconducting systems. We study the performance of diffusion Monte Carlo (DMC) applied to transition metal containing dimers (TMCDs) using single-determinant Slater–Jastrow trial wavefunctions and investigate the possible influence of the locality and pseudopotential errors. We find that the locality approximation can introduce nonsystematic errors of up to several tens of kilocalories per mole in the absolute energy of Cu and CuH if Ar or Mg core pseudopotentials (PPs) are used for the 3d transition metal atoms. Even for energy differences such as binding energies, errors due to the locality approximation can be problematic if chemical accuracy is sought. The use of the Ne core PPs developed by Burkatzki et al. (J. Chem. Phys. 2008, 129, 164115), the use of linear energy minimization rather than unreweighted variance minimization for the optimization of the Jastrow function, and the use of large Jastrow parametrizations reduce the locality errors. In the second section of this article, we study the general performance of DMC for 3d TMCDs using a database of binding energies of 20 TMCDs, for which comparatively accurate experimental data is available. Comparing our DMC results to these data for our results that compare best with experiment, we find a mean unsigned error (MUE) of 4.5 kcal/mol. This compares well with the achievable accuracy in CCSDT(2)Q (MUE = 4.6 kcal/mol) and the best all-electron DFT results (MUE = 4.5 kcal/mol) for the same set of systems (Truhlar et al. J. Chem. Theory Comput. 2015, 11, 2036–2052). The mean errors in DMC depend less on the exchange-correlation functionals used to generate the trial wavefunction than the corresponding mean errors in the underlying DFT calculations. Furthermore, the QMC results obtained for each molecule individually vary less with the functionals used. These observations are relevant for systems such as molecules interacting with transition metal surfaces where the DFT functionals performing best for molecules (hybrids) do not yield improvements in DFT. Overall, the results presented in this article yield important guidelines for both the assessment of the achievable accuracy with DMC and the design of DMC calculations for systems including transition metal atoms.

** Manuscript:
Available for download from 
http://pubsdc3.acs.org/articlesonrequest/AOR-mgdx4U3jMHxK6KNXHFG4
this links allows 50 downloads within the first 12 months after publication and unlimited download thereafter (so please do not use this link unless you cannot get access via your institution). The preprint file cannot be made available prior to these 12 months due to copyright reasons.

** Description per file:

** Folder manuscript only available group internally:

** Folder databasesAndAnalysis
** File jobDB.db
   Database used for the run generation and for storing the results of the main results
** File jobDB.db
   Database used for the run generation and for storing the results of the calculations used in part 2 of the supplementary material (generating trial wavefunctions using gaussian)
** 3dMLBE20_JTCT11-2036_2015.db  
   Database containing the experimental values and spin-orbit couplings taken from JCTC 11, 2036-2052 (2015) used in the analysis
** File makePlots_plotsPaper2.ipynb  
   ipython notebook file (version 4.0.6 and is running on: Python 2.7.8) generating the plots and the data analysis used in the manuscript
** File makePlots_TiCl-CrCl-MnS.ipynb  
   ipython notebook file (version 4.0.6 and is running on: Python 2.7.8) generating the plots and the data analysis used in the first part of the supplementary information
** File makePlotsV2_01.ipynb
   ipython notebook file (version 4.0.6 and is running on: Python 2.7.8) generating the plots and the data analysis used in the second part of the supplementary information

** Folder generateRunsFromDB
   These files are only available university internally at the moment.