DOI: 10.5061/dryad.hx3ffbgn3
title: Data for: Thermodynamics of the gas-phase dimerization of formic acid: fully anharmonic finite temperature calculations at the CCSD(T) and many DFT levels

description: We provide data files needed to reproduce the MLPT calculations presented
 in work of Dávid Vrška, Michal Pitoňák and Tomáš Bučko:
 "Thermodynamics of the gas-phase dimerization of formic acid: Fully
 anharmonic finite temperature calculations at the CCSD(T) and many DFT
 levels" or to perform such calculation for any other electronic
 structure method not considered in mentioned work. In particular, the
 structural data are available in the standard xyz file format
 containing the atomic labels and atomic coordinates in Angstroms (Å),
 and the energies are provided as data files in a two-column
 format, where the items in the first column represent the
 identification numbers of each configuration and those in the second
 column are the corresponding energies in electronvolts (eV).
description: Ab initio density functional theory (DFT) simulations were
 employed using the periodic DFT software package VASP [1-3]. Coupled
 Clusters (CCSD(T)) [4] calculations were performed using the program ORCA
 (version 5.0.4) [5,6].  Ab initio molecular dynamics
 (AIMD) calculations with a time step of 0.5 fs were performed at the
 PBE+D2 level. The simulation temperature of 300 K was maintained by
 employing Andersen thermostat [7], whereby the mass of hydrogen atoms was
 increased to 3 amu. The total length of all AIMD trajectories was 100 ps
 out of which the equilibration period identified via Mann Kendall test [8]
 (up to 15 ps) was discarded.  For determining energy at
 the target level of theory (CCSD(T) and various DFAs) we employed Machine
 Learning Perturbation Theory (MLPT) aproach using the data obtained at
 computationally less expensive method (PBE+D2). We used the training set
 with 100 training points obtained as 100 single point calculations at the
 target level. Within MLPT, the kernel ridge regression [9] as implemented
 in scikit-learn library [10,11] with the REMatch kernel [12] and the SOAP
 (smooth overlap of atomic positions) descriptors [13] as implemented in
 DScribe library [14] are used. In the cases of a poor
 overlap, corresponding to cases with index I<sub>w</sub>
 &lt; 0.05, see Herzog et al. [15] proposed to perform machine learning
 Monte Carlo (MLMC) resampling. References:<br>[1]
 G. Kresse and J. Hafner, Phys. Rev. B 48, 13115 (1993)<br>[2] G.
 Kresse and J. Hafner, J. Phys. Condens. Matter 6, 8245 (1994)<br>[3]
 G. Kresse and J. Hafner, Phys. Rev. B 47, 558 (1993)<br>[4]
 K. Raghavachari, G. W. Trucks, J. A. Pople, and M. Head-Gordon, Chem.
 Phys. Lett. 157, 479 (1989)<br>[5] F. Neese, Wiley Interdiscip. Rev.
 Comput. Mol. Sci. 2, 73 (2011)<br>[6] F. Neese, Wiley Interdiscip.
 Rev. Comput. Mol. Sci. 12 (2022), 10.1002/wcms.1606<br>[7] H. C.
 Andersen, J. Chem. Phys. 72, 2384 (1980)<br>[8] S. K. Schiferl and
 D. C. Wallace, J. Chem. Phys. 83, 5203 (1985)<br>[9] M. Rupp, Int.
 J. Quantum Chem. 115, 1058 (2015)<br>[10] L. Himanen, M. O. Jäger,
 E. V. Morooka, F. F. Canova, Y. S. Ranawat, D. Z. Gao, P. Rinke, and A. S.
 Foster, Comput. Phys. Commun. 247, 106949 (2020)<br>[11]
 F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel,
 M. Blondel, A. Müller, J. Nothman, G. Louppe, P. Prettenhofer, R. Weiss,
 V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M.
 Perrot, and E. Duchesnay, J. Mach. Learn. Res. (2012),
 10.48550/ARXIV.1201.0490<br>[12] S. De, A. P. Bartók, G. Csányi, and
 M. Ceriotti, Phys. Chem. Chem. Phys. 18, 13754 (2016)<br>[13] A. P.
 Bartók, R. Kondor, and G. Csányi, Phys. Rev. B 87, 184115
 (2013)<br>[14] L. Himanen, M. O. Jäger, E. V. Morooka, F. Federici
 Canova, Y. S. Ranawat, D. Z. Gao, P. Rinke, and A. S. Foster, Comput.
 Phys. Commun. 247, 106949 (2020)<br>[15] . Herzog, M. C. da Silva,
 B. Casier, M. Badawi, F. Pascale, T. Bučko, S. Lebègue, and D. Rocca, J.
 Chem. Theory Comput. 18, 1382 (2022)
description: # Data for: Thermodynamics of the gas-phase dimerization of formic acid:
 Fully anharmonic finite temperature calculations at the CCSD(T) and many
 DFT levels ## file formats * **.xyz files** contain the atomic labels and
 atomic coordinates in Å, in the standard xyz format. * **.dat files**
 consist of two columns: the items in the first column represent the
 identification numbers of each configuration and in the second column are
 the corresponding energies in eV. ## structures * **AIMD_M.xyz** cartesian
 coordinates from PBE+D2 AIMD for monomer * **AIMD_D.xyz** cartesian
 coordinates from PBE+D2 AIMD dynamics for dimer * **training_M.xyz**
 cartesian coordinates used for MLPT training for monomer *
 **training_D.xyz** cartesian coordinates used for MLPT training for dimer
 * **MC_HSE06.xyz** cartesian coordinates from PBE+D2/HSE06 MLMC *
 **MC_vdW-DF.xyz** cartesian coordinates from PBE+D2/vdW-DF MLMC *
 **MC_CCSDT.xyz** cartesian coordinates from PBE+D2/CCSD(T) MLMC ##
 energies ### AIMD * **AIMD_M_PBE.dat** PBE energies from PBE+D2 AIMD for
 monomer * **AIMD_M_PBE+D2.dat** PBE+D2 energies from PBE+D2 AIMD for
 monomer * **AIMD_D_PBE.dat** PBE energies from PBE+D2 AIMD for dimer *
 **AIMD_D_PBE+D2.dat** PBE+D2 energies from PBE+D2 AIMD for dimer ### MLPT
 training * **training_D_PBE.dat** PBE energies used for training of MLPT
 for dimer * **training_D_PBE+D3.dat** PBE+D3(0) energies used for training
 of MLPT for dimer * **training_D_PBE+D3BJ.dat** PBE+D3BJ energies used for
 training of MLPT for dimer * **training_D_PBE+MBD.dat** PBE+MBD energies
 used for training of MLPT for dimer * **training_D_PBE+HI.dat** PBE+TS/HI
 energies used for training of MLPT for dimer * **training_D_SCAN.dat**
 SCAN energies used for training of MLPT for dimer *
 **training_D_SCAN+D3.dat** SCAN+D3(0) energies used for training of MLPT
 for dimer * **training_D_SCAN+D3BJ.dat** SCAN+D3BJ energies used for
 training of MLPT for dimer * **training_D_SCAN+MBD.dat** SCAN+MBD energies
 used for training of MLPT for dimer * **training_D_SCAN+RVV10.dat**
 SCAN+RVV10 energies used for training of MLPT for dimer *
 **training_D_HSE06.dat** HSE06 energies used for training of MLPT for
 dimer * **training_D_HSE06+D3.dat** HSE06+D3(0) energies used for training
 of MLPT for dimer * **training_D_HSE06+D3BJ.dat** HSE06+D3BJ energies used
 for training of MLPT for dimer * **training_D_HSE06+MBD.dat** HSE06+MBD
 energies used for training of MLPT for dimer * **training_D_vdW-DF.dat**
 vdW-DF energies used for training of MLPT for dimer *
 **training_D_vdW-DF-cx.dat** vdW-DF-cx energies used for training of MLPT
 for dimer * **training_D_vdW-DF2-B86R.dat** vdW-DF2-B86R energies used for
 training of MLPT for dimer * **training_D_optB86b-vdW.dat** optB86b-vdW
 energies used for training of MLPT for dimer * **training_D_CCSDT.dat**
 CCSD(T) energies used for training of MLPT for dimer *
 **training_M_PBE.dat** PBE energies used for training of MLPT for monomer
 * **training_M_PBE+D3.dat** PBE+D3(0) energies used for training of MLPT
 for monomer * **training_M_PBE+D3BJ.dat** PBE+D3BJ energies used for
 training of MLPT for monomer * **training_M_PBE+MBD.dat** PBE+MBD energies
 used for training of MLPT for monomer * **training_M_PBE+HI.dat**
 PBE+TS/HI energies used for training of MLPT for monomer *
 **training_M_SCAN.dat** SCAN energies used for training of MLPT for
 monomer * **training_M_SCAN+D3.dat** SCAN+D3(0) energies used for training
 of MLPT for monomer * **training_M_SCAN+D3BJ.dat** SCAN+D3BJ energies used
 for training of MLPT for monomer * **training_M_SCAN+MBD.dat** SCAN+MBD
 energies used for training of MLPT for monomer *
 **training_M_SCAN+RVV10.dat** SCAN+RVV10 energies used for training of
 MLPT for monomer * **training_M_HSE06.dat** HSE06 energies used for
 training of MLPT for monomer * **training_M_HSE06+D3.dat** HSE06+D3(0)
 energies used for training of MLPT for monomer *
 **training_M_HSE06+D3BJ.dat** HSE06+D3BJ energies used for training of
 MLPT for monomer * **training_M_HSE06+MBD.dat** HSE06+MBD energies used
 for training of MLPT for monomer * **training_M_vdW-DF.dat** vdW-DF
 energies used for training of MLPT for monomer *
 **training_M_vdW-DF-cx.dat** vdW-DF-cx energies used for training of MLPT
 for monomer * **training_M_vdW-DF2-B86R.dat** vdW-DF2-B86R energies used
 for training of MLPT for monomer * **training_M_optB86b-vdW.dat**
 optB86b-vdW energies used for training of MLPT for monomer *
 **training_M_CCSDT.dat** CCSD(T) energies used for training of MLPT for
 monomer ## MLPT predictions * **predicted_D_PBE.dat** PBE energies from
 MLPT prediction for dimer * **predicted_D_PBE+D3.dat** PBE+D3(0) energies
 from MLPT prediction for dimer * **predicted_D_PBE+D3BJ.dat** PBE+D3BJ
 energies from MLPT prediction for dimer * **predicted_D_PBE+MBD.dat**
 PBE+MBD energies from MLPT prediction for dimer *
 **predicted_D_PBE+HI.dat** PBE+TS/HI energies from MLPT prediction for
 dimer * **predicted_D_SCAN.dat** SCAN energies from MLPT prediction for
 dimer * **predicted_D_SCAN+D3.dat** SCAN+D3(0) energies from MLPT
 prediction for dimer * **predicted_D_SCAN+D3BJ.dat** SCAN+D3BJ energies
 from MLPT prediction for dimer * **predicted_D_SCAN+MBD.dat** SCAN+MBD
 energies from MLPT prediction for dimer * **predicted_D_SCAN+RVV10.dat**
 SCAN+RVV10 energies from MLPT prediction for dimer *
 **predicted_D_HSE06.dat** HSE06 energies from MLPT prediction for dimer *
 **predicted_D_HSE06+D3.dat** HSE06+D3(0) energies from MLPT prediction for
 dimer * **predicted_D_HSE06+D3BJ.dat** HSE06+D3BJ energies from MLPT
 prediction for dimer * **predicted_D_HSE06+MBD.dat** HSE06+MBD energies
 from MLPT prediction for dimer * **predicted_D_vdW-DF.dat** vdW-DF
 energies from MLPT prediction for dimer * **predicted_D_vdW-DF-cx.dat**
 vdW-DF-cx energies from MLPT prediction for dimer *
 **predicted_D_vdW-DF2-B86R.dat** vdW-DF2-B86R energies from MLPT
 prediction for dimer * **predicted_D_optB86b-vdW.dat** optB86b-vdW
 energies from MLPT prediction for dimer * **predicted_D_CCSDT.dat**
 CCSD(T) energies from MLPT prediction for dimer * **predicted_M_PBE.dat**
 PBE energies from MLPT prediction for monomer * **predicted_M_PBE+D3.dat**
 PBE+D3(0) energies from MLPT prediction for monomer *
 **predicted_M_PBE+D3BJ.dat** PBE+D3BJ energies from MLPT prediction for
 monomer * **predicted_M_PBE+MBD.dat** PBE+MBD energies from MLPT
 prediction for monomer * **predicted_M_PBE+HI.dat** PBE+TS/HI energies
 from MLPT prediction for monomer * **predicted_M_SCAN.dat** SCAN energies
 from MLPT prediction for monomer * **predicted_M_SCAN+D3.dat** SCAN+D3(0)
 energies from MLPT prediction for monomer * **predicted_M_SCAN+D3BJ.dat**
 SCAN+D3BJ energies from MLPT prediction for monomer *
 **predicted_M_SCAN+MBD.dat** SCAN+MBD energies from MLPT prediction for
 monomer * **predicted_M_SCAN+RVV10.dat** SCAN+RVV10 energies from MLPT
 prediction for monomer * **predicted_M_HSE06.dat** HSE06 energies from
 MLPT prediction for monomer * **predicted_M_HSE06+D3.dat** HSE06+D3(0)
 energies from MLPT prediction for monomer * **predicted_M_HSE06+D3BJ.dat**
 HSE06+D3BJ energies from MLPT prediction for monomer *
 **predicted_M_HSE06+MBD.dat** HSE06+MBD energies from MLPT prediction for
 monomer * **predicted_M_vdW-DF.dat** vdW-DF energies from MLPT prediction
 for monomer * **predicted_M_vdW-DF-cx.dat** vdW-DF-cx energies from MLPT
 prediction for monomer * **predicted_M_vdW-DF2-B86R.dat** vdW-DF2-B86R
 energies from MLPT prediction for monomer *
 **predicted_M_optB86b-vdW.dat** optB86b-vdW energies from MLPT prediction
 for monomer * **predicted_M_CCSDT.dat** CCSD(T) energies from MLPT
 prediction for monomer ## MLMC * **MC_HSE06.dat** HSE06 energies from
 HSE06 MLMC * **MC_vdW-DF.dat** vdW-DF energies from vdW-DF MLMC *
 **MC_CCSDT.dat** CCSD(T) energies from CCSD(T) MLMC