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13

I am afraid I can't be of much help regarding the QM part of your simulation, but I can give some thoughts on the MM part. In short: you should use the cutoff that was used to validate your force field. While a proper ab initio method is expected to improve with a longer cutoff, this is not necessarily true for force fields. The reason for that is that force ...


11

Very interesting question! The LJ potential is quite benign in that it has a $r^{-6}$ decay, which is pretty rapid. However, MD codes typically go a bit farther than a straightaway truncation at $r_{\rm max}$, but instead also apply an analytical correction for the region $[r_{\rm max},\infty)$. The analytical correction for the cut-off can be justified by ...


11

pDynamo pDynamo is a program developed by Martin Field, and is a CHARMM/ORCA interface. There is a wiki. Here is the first line from the abstract: The pDynamo program has been developed for the simulation of molecular systems using hybrid quantum chemical (QC) and molecular mechanical (MM) potentials. There is also a PyMol plugin named EasyHybrid. Though I ...


8

I figured out the correct formatting. The section defining the QM needs to have the MORead option, the %moinp section, and the %scf section. Example: !B3LYP 6-31G* Slowconv MOREAD *xyz 0 3 ... * %moinp "01.ethylene.gbw" %scf rotate {7, 8, 90, 0, 0} end end


7

The Monte Carlo Sign Problem For classical systems, Monte Carlo works extremely well. Quantum Monte Carlo is very powerful, but there are many interesting systems that suffer from the sign problem, which makes Monte Carlo exponentially hard. Rather than discuss this in detail here, see the discussion on the sign problem on the physics SE.


7

To tether a set of atoms (index or fragment) in pDynamo, the below lines are needed (energy model can vary): tethers = pM.SoftConstraintContainer () system.DefineSoftConstraints ( tethers ) reference = Clone ( system.coordinates3 ) tetherEnergyModel = SoftConstraintEnergyModelHarmonic ( 0.1 , 500.0 ) NAME1 = "IronOH" sele1 = ...


7

Not really sure about benchmarks for solid state TS geometries. A couple of scattered piece of references are: (i) This paper by Ceder's group has several related TS barriers: aip.scitation.org/doi/pdf/10.1063/1.4960790 (ii) Then there is Henkelman's paper for bulk unit cell TS calculations: aip.scitation.org/doi/abs/10.1063/1.3684549. Maybe you are already ...


6

TeraChem is commercial, but the demo mode will handle calculations taking up to ten minutes and that is free. Ten minutes for a single point energy and gradient gets you pretty far with TeraChem (unlike other CPU codes).


5

TeraChem QM/MM is not computationally more intensive than regular QM. In mechanical embedding the MM region is essentially free because it's just simple harmonic oscillators. In electrostatic embedding the MM region polarizes the charges of the QM region via the nuclear potential. As long as the external charge doesn't cause any SCF convergence issue the ...


5

In pdynamo 3.0.9 A vector3 can be defined by importing from Geometry module. For example, center = Vector3.Null ( ) It looks like a vector3 is just 3 element vector. Not really sure but here is what I found in pScientific/Symmetry/PointGroupFinder.py file You will need to get the center of mass however by looping over each atom in the geometry e.g. $...


3

The Q-Chem manual edition 5.2 is much better at describing QM/MM However, to answer your main question there are no angles and torsions across a link atom. The link atom is a fictitious hydrogen that is added when you break the QM and MM region at a bond. This is done to maintain the proper valency/avoid dangling bonds in the QM region. The MM region is ...


3

I found the most consistent way to localize orbitals. Use the orca_loc extension. You must create and input file where define the localization method (PM, Boys, ...), and the orbitals you want to localize. To run it: orca_loc name.inp -i


2

Machine Learning There are several examples about how Machine Learning is been used in Quantum Chemistry. Here I bring two of them that are directly related with QM/MM calculations. L. Böselt, M. Thürlemann, and S. Riniker. Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems, (2021) DOI: 10.1021/acs.jctc.0c01112, PDF. ...


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