scholarly journals Quantum Dynamics of Water from Møller-Plesset Perturbation Theory via a Neural Network Potential

Author(s):  
Jinggang Lan ◽  
David Wilkins ◽  
Vladimir Rybkin ◽  
Marcella Iannuzzi ◽  
Juerg Hutter

We report the static and dynamical properties of liquid water at the level of second-order Møller-Plesset per- perturbation theory (MP2) with classical and quantum nuclear dynamics using a neural network potential. We examined the temperature-dependent radial distribution functions, diffusion, and vibrational dynamics. MP2 theory predicts over-structured liquid water as well as a lower diffusion coefficient at ambient conditions compared to experiments, which may be attributed to the incomplete basis set. A better agreement with experimental structural properties and the diffusion constant are observed at an elevated temperature of 340 K from our simulations. Although the high-level electronic structure calculations are expensive, training a neural network potential requires only a few thousand frames. The approach is promising as it involves modest human effort and is straightforwardly extensible to other simple liquids.

2021 ◽  
Author(s):  
Jinggang Lan ◽  
David Wilkins ◽  
Vladimir Rybkin ◽  
Marcella Iannuzzi ◽  
Juerg Hutter

We report the static and dynamical properties of liquid water at second-order Møller-Plesset perturbation theory level (MP2) with classical and quantum dynamics simulations using a neural network potential. We examined the temperature-dependent radial distribution function, diffusion and vibrational dynamics. MP2 theory predicts an over-structured liquid water at ambient conditions, which may be attributed to the incomplete basis set. The excellent agreement with experimental structural properties as well as the diffusion constant is observed at an elevated temperature of 340K.


2013 ◽  
Vol 111 (9-11) ◽  
pp. 1178-1189
Author(s):  
Kameron R. Jorgensen ◽  
Vinay V. Ramasesh ◽  
Sonja Hannibal ◽  
Nathan J. DeYonker ◽  
Angela K. Wilson

2010 ◽  
Vol 09 (01) ◽  
pp. 341-352
Author(s):  
GUIQIU ZHANG ◽  
HONGMEI GAO ◽  
DEZHAN CHEN

We present an ab initio investigation on the chiral discrimination of 2-methylol oxirane (M-olOx)· · · ethanol (EtOH) complexes, for the sake of comparison with previous report on propylene oxide (PO)· · · EtOH complexes. Second-order Møller–Plesset perturbation theory (MP2) with the 6-311++G(d,p) basis set was used to elucidate the diastereomeric interactions between ethanol ( EtOH ), a transient chiral alcohol, and the chiral molecule 2-methylol oxirane (R). Six complexes of M-olOx· · · EtOH have been identified and their structures as well as their calculated stability ordering have been determined. The six complexes were defined in a similar way as for PO· · · EtOH . The primary O–H· · · O hydrogen bonds are predicted to be important contributions to chiral discrimination in M-olOx· · · EtOH . The three syn structures, with ethanol and the methylol group on the same side of the oxirane ring, are energetically favored over the three anti structures. The larger chirodiastaltic energy between synG- and synG+ is 0.52 kJ mol-1. The largest diastereofacial energy between synG- and antiG- is 13.90 kJ mol-1. The obtained results are compared with previously reported results on the PO· · · EtOH complexes and the mechanisms of chiral discrimination in PO· · · EtOH and M-olOx· · · EtOH are discussed. The harmonic frequencies, IR intensities, rotational constants, and dipole moments for the M-olOx· · · EtOH complexes are also presented. Such a theoretical study should be valuable to further spectroscopic investigations on M-olOx· · · EtOH complexes.


2019 ◽  
Author(s):  
Brian Nguyen ◽  
Guo P Chen ◽  
Matthew M. Agee ◽  
Asbjörn M. Burow ◽  
Matthew Tang ◽  
...  

Prompted by recent reports of large errors in noncovalent interaction (NI) energies obtained from many-body perturbation theory (MBPT), we compare the performance of second-order Møller–Plesset MBPT (MP2), spin-scaled MP2, dispersion-corrected semilocal density functional approximations (DFA), and the post-Kohn–Sham random phase approximation (RPA) for predicting binding energies of supramolecular complexes contained in the S66, L7, and S30L benchmarks. All binding energies are extrapolated to the basis set limit, corrected for basis set superposition errors, and compared to reference results of the domain-based local pair-natural orbital coupled-cluster (DLPNO-CCSD(T)) or better quality. Our results confirm that MP2 severely overestimates binding energies of large complexes, producing relative errors of over 100% for several benchmark compounds. RPA relative errors consistently range between 5-10%, significantly less than reported previously using smaller basis sets, whereas spin-scaled MP2 methods show limitations similar to MP2, albeit less pronounced, and empirically dispersion-corrected DFAs perform almost as well as RPA. Regression analysis reveals a systematic increase of relative MP2 binding energy errors with the system size at a rate of approximately 1‰ per valence electron, whereas the RPA and dispersion-corrected DFA relative errors are virtually independent of the system size. These observations are corroborated by a comparison of computed rotational constants of organic molecules to gas-phase spectroscopy data contained in the ROT34 benchmark. To analyze these results, an asymptotic adiabatic connection symmetry-adapted perturbation theory (AC-SAPT) is developed which uses monomers at full coupling whose ground-state density is constrained to the ground-state density of the complex. Using the fluctuation–dissipation theorem, we obtain a nonperturbative “screened second-order” expression for the dispersion energy in terms of monomer quantities which is exact for non-overlapping subsystems and free of induction terms; a first-order RPA-like approximation to the Hartree, exchange, and correlation kernel recovers the macroscopic Lifshitz limit. The AC-SAPT expansion of the interaction energy is obtained from Taylor expansion of the coupling strength integrand. Explicit expressions for the convergence radius of the AC-SAPT series are derived within RPA and MBPT and numerically evaluated. Whereas the AC-SAPT expansion is always convergent for nondegenerate monomers when RPA is used, it is found to spuriously diverge for second-order MBPT, except for the smallest and least polarizable monomers. The divergence of the AC-SAPT series within MBPT is numerically confirmed within RPA; prior numerical results on the convergence of the SAPT expansion for MBPT methods are revisited and support this conclusion once sufficiently high orders are included. The cause of the failure of MBPT methods for NIs of large systems is missing or incomplete “electrodynamic” screening of the Coulomb interaction due to induced particle–hole pairs between electrons in different monomers, leaving the effective interaction too strong for AC-SAPT to converge. Hence, MBPT cannot be considered reliable for quantitative predictions of NIs, even in moderately polarizable molecules with a few tens of atoms. The failure to accurately account for electrodynamic polarization makes MBPT qualitatively unsuitable for applications such as NIs of nanostructures, macromolecules, and soft materials; more robust non-perturbative approaches such as RPA or coupled cluster methods should be used instead whenever possible.<br>


Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1909
Author(s):  
Konstantin Osintsev ◽  
Sergei Aliukov ◽  
Yuri Prikhodko

A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average sizes, which are usually grouped and expressed as a fraction of the total mass of the fuel. The authors suggest taking into account the sequential nature of the entry into the chemical reactions of combustion of particles of different masses. In addition, for the application of the developed methodology, it is necessary to divide the furnace volume into zones and sections. In particular, the initial section of the torch, the zone of intense burning and the zone of afterburning. In this case, taking into account all the thermophysical characteristics of the torch, it is possible to make a thermal balance of the zone of intense burning. Then determines the rate of expiration of the fuel-air mixture, the time of combustion of particles of different masses and the temperature at the end of the zone of intensive combustion. The temperature of the torch, the speed of flame propagation, and the degree of particle burnout must be controlled. The authors propose an algorithm for controlling the thermophysical properties of the torch based on neural network algorithms. The system collects data for a certain time, transmits the information to the server. The data is processed and a forecast is made using neural network algorithms regarding the combustion modes. This allows to increase the reliability and efficiency of the combustion process. The authors present experimental data and compare them with the data of the analytical calculation. In addition, data for certain modes are given, taking into account the system’s operation based on neural network algorithms.


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