Question
Asked 20 August 2015

Are their any clear indications that the SCF will not converge for a SPE calculation in Gaussian?

Of 54 atoms in my structure (a solute and a water molecule) in an implicit solvent (water), using Gaussian 09, I performed a high level DFT geometry optimisation: wb97xd/6-311++g(2df,2p). Using the optimised structure I am trying to perform an MP2/aug-cc-pVTZ single point energy calculation. It is taking a very long time to complete (16 cores on 1 node and I'm still waiting after 3 days). I have included scf=(qc,maxcycle=1024).
My question; given the output below does it look as though the calculation will ever complete? Or, is it 'circling the drain'?
I would also like to know what "QCLLim is confused" means.
Many thanks
Anthony
Iteration    1 A*A^-1 deviation from unit magnitude is 2.78D-15 for    295.
Iteration    1 A*A^-1 deviation from orthogonality  is 3.09D-15 for   2663   2446.
Iteration    1 A^-1*A deviation from unit magnitude is 2.66D-15 for   1526.
Iteration    1 A^-1*A deviation from orthogonality  is 5.12D-08 for   2296   2294.
Iteration    2 A*A^-1 deviation from unit magnitude is 2.55D-15 for    280.
Iteration    2 A*A^-1 deviation from orthogonality  is 2.16D-15 for   3291     32.
Iteration    2 A^-1*A deviation from unit magnitude is 6.66D-16 for    236.
Iteration    2 A^-1*A deviation from orthogonality  is 2.68D-16 for   2588    970.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
     Minimum is close to point  3 DX=  3.60D-02 DF= -1.61D-04 DXR=  4.30D-02 DFR=  1.85D-03 which will be used.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
     Accept linear search using points  1 and  2.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
     Accept linear search using points  1 and  2.
LinEq1:  Iter=  0 NonCon=     1 RMS=1.79D-05 Max=1.27D-03 NDo=     1
AX will form     1 AO Fock derivatives at one time.
LinEq1:  Iter=  1 NonCon=     1 RMS=5.25D-06 Max=3.44D-04 NDo=     1
LinEq1:  Iter=  2 NonCon=     1 RMS=1.79D-06 Max=8.20D-05 NDo=     1
LinEq1:  Iter=  3 NonCon=     1 RMS=3.51D-07 Max=2.27D-05 NDo=     1
LinEq1:  Iter=  4 NonCon=     1 RMS=1.63D-07 Max=9.52D-06 NDo=     1
LinEq1:  Iter=  5 NonCon=     1 RMS=4.58D-08 Max=2.70D-06 NDo=     1
LinEq1:  Iter=  6 NonCon=     1 RMS=2.06D-08 Max=1.19D-06 NDo=     1
LinEq1:  Iter=  7 NonCon=     0 RMS=1.04D-08 Max=2.30D-07 NDo=     1
Linear equations converged to 5.524D-08 5.524D-07 after     7 iterations.
     Accept linear search using points  1 and  2.
     Minimum is close to point  2 DX= -1.07D-03 DF= -1.41D-10 DXR=  1.07D-03 DFR=  1.14D-06 which will be used.
LinEq1:  Iter=  0 NonCon=     1 RMS=4.41D-08 Max=2.39D-06 NDo=     1
LinEq1:  Iter=  1 NonCon=     1 RMS=2.65D-08 Max=2.19D-06 NDo=     1
LinEq1:  Iter=  2 NonCon=     1 RMS=1.14D-08 Max=2.49D-07 NDo=     1
LinEq1:  Iter=  3 NonCon=     1 RMS=6.58D-09 Max=2.22D-07 NDo=     1
LinEq1:  Iter=  4 NonCon=     1 RMS=4.14D-09 Max=1.91D-07 NDo=     1
LinEq1:  Iter=  5 NonCon=     1 RMS=1.50D-09 Max=5.61D-08 NDo=     1
LinEq1:  Iter=  6 NonCon=     1 RMS=1.08D-09 Max=2.73D-08 NDo=     1
LinEq1:  Iter=  7 NonCon=     1 RMS=1.14D-09 Max=1.38D-08 NDo=     1
LinEq1:  Iter=  8 NonCon=     1 RMS=1.18D-09 Max=2.65D-08 NDo=     1
LinEq1:  Iter=  9 NonCon=     1 RMS=1.26D-09 Max=3.05D-08 NDo=     1
LinEq1:  Iter= 10 NonCon=     1 RMS=1.18D-09 Max=1.61D-08 NDo=     1
LinEq1:  Iter= 11 NonCon=     1 RMS=6.90D-10 Max=1.29D-08 NDo=     1
LinEq1:  Iter= 12 NonCon=     1 RMS=5.19D-10 Max=8.95D-09 NDo=     1
LinEq1:  Iter= 13 NonCon=     1 RMS=5.84D-10 Max=8.23D-09 NDo=     1
LinEq1:  Iter= 14 NonCon=     1 RMS=4.89D-10 Max=5.58D-09 NDo=     1
LinEq1:  Iter= 15 NonCon=     1 RMS=5.05D-10 Max=6.42D-09 NDo=     1
LinEq1:  Iter= 16 NonCon=     1 RMS=3.51D-10 Max=3.42D-09 NDo=     1
LinEq1:  Iter= 17 NonCon=     0 RMS=2.44D-10 Max=2.83D-09 NDo=     1
Linear equations converged to 3.476D-10 3.476D-09 after    17 iterations.
LinEq1:  Iter=  0 NonCon=     1 RMS=1.88D-08 Max=2.20D-07 NDo=     1
LinEq1:  Iter=  1 NonCon=     1 RMS=5.39D-09 Max=8.12D-08 NDo=     1
LinEq1:  Iter=  2 NonCon=     1 RMS=4.73D-09 Max=6.98D-08 NDo=     1
LinEq1:  Iter=  3 NonCon=     1 RMS=2.82D-09 Max=3.38D-08 NDo=     1
LinEq1:  Iter=  4 NonCon=     1 RMS=1.95D-09 Max=2.85D-08 NDo=     1
LinEq1:  Iter=  5 NonCon=     1 RMS=1.80D-09 Max=2.01D-08 NDo=     1
LinEq1:  Iter=  6 NonCon=     1 RMS=1.08D-09 Max=1.82D-08 NDo=     1
LinEq1:  Iter=  7 NonCon=     1 RMS=6.16D-10 Max=7.13D-09 NDo=     1
LinEq1:  Iter=  8 NonCon=     1 RMS=5.44D-10 Max=6.63D-09 NDo=     1
LinEq1:  Iter=  9 NonCon=     1 RMS=6.08D-10 Max=7.66D-09 NDo=     1
LinEq1:  Iter= 10 NonCon=     1 RMS=5.10D-10 Max=7.21D-09 NDo=     1
LinEq1:  Iter= 11 NonCon=     1 RMS=3.96D-10 Max=4.01D-09 NDo=     1
LinEq1:  Iter= 12 NonCon=     1 RMS=2.71D-10 Max=3.59D-09 NDo=     1
LinEq1:  Iter= 13 NonCon=     1 RMS=2.04D-10 Max=2.27D-09 NDo=     1
LinEq1:  Iter= 14 NonCon=     1 RMS=2.36D-10 Max=2.26D-09 NDo=     1
LinEq1:  Iter= 15 NonCon=     1 RMS=2.44D-10 Max=2.76D-09 NDo=     1
LinEq1:  Iter= 16 NonCon=     1 RMS=2.08D-10 Max=2.57D-09 NDo=     1
LinEq1:  Iter= 17 NonCon=     0 RMS=1.96D-10 Max=2.10D-09 NDo=     1
Linear equations converged to 2.220D-10 2.220D-09 after    17 iterations.
QCLLim is confused:  Bigger=T Turned=T
NLin=  3 IMin12=  1  2 I12=  0  2 IX=  1 XLMin= 0.000D+00 XLMax= 0.000D+00
X = 0.000D+00 1.000D+00 2.000D+00
DE= 0.000D+00 5.184D-11 8.322D-10
LinEq1:  Iter=  0 NonCon=     1 RMS=1.15D-08 Max=1.38D-07 NDo=     1
LinEq1:  Iter=  1 NonCon=     1 RMS=6.34D-09 Max=1.01D-07 NDo=     1
LinEq1:  Iter=  2 NonCon=     1 RMS=4.57D-09 Max=7.15D-08 NDo=     1
LinEq1:  Iter=  3 NonCon=     1 RMS=2.12D-09 Max=3.09D-08 NDo=     1
LinEq1:  Iter=  4 NonCon=     1 RMS=2.10D-09 Max=2.33D-08 NDo=     1
LinEq1:  Iter=  5 NonCon=     1 RMS=1.28D-09 Max=1.45D-08 NDo=     1
LinEq1:  Iter=  6 NonCon=     1 RMS=7.91D-10 Max=1.05D-08 NDo=     1
LinEq1:  Iter=  7 NonCon=     1 RMS=5.56D-10 Max=6.68D-09 NDo=     1
LinEq1:  Iter=  8 NonCon=     1 RMS=4.93D-10 Max=7.23D-09 NDo=     1
LinEq1:  Iter=  9 NonCon=     1 RMS=6.15D-10 Max=8.48D-09 NDo=     1
LinEq1:  Iter= 10 NonCon=     1 RMS=7.11D-10 Max=8.51D-09 NDo=     1
LinEq1:  Iter= 11 NonCon=     1 RMS=5.40D-10 Max=7.07D-09 NDo=     1
LinEq1:  Iter= 12 NonCon=     1 RMS=4.21D-10 Max=5.07D-09 NDo=     1
LinEq1:  Iter= 13 NonCon=     1 RMS=3.13D-10 Max=3.44D-09 NDo=     1
LinEq1:  Iter= 14 NonCon=     1 RMS=2.42D-10 Max=2.47D-09 NDo=     1
LinEq1:  Iter= 15 NonCon=     1 RMS=2.01D-10 Max=2.45D-09 NDo=     1
LinEq1:  Iter= 16 NonCon=     1 RMS=1.85D-10 Max=2.36D-09 NDo=     1
LinEq1:  Iter= 17 NonCon=     1 RMS=1.64D-10 Max=1.52D-09 NDo=     1
LinEq1:  Iter= 18 NonCon=     0 RMS=1.09D-10 Max=1.34D-09 NDo=     1
Linear equations converged to 1.381D-10 1.381D-09 after    18 iterations.
Restarting incremental Fock formation.
LinEq1:  Iter=  0 NonCon=     1 RMS=6.06D-08 Max=6.90D-07 NDo=     1
LinEq1:  Iter=  1 NonCon=     1 RMS=1.27D-08 Max=1.37D-07 NDo=     1
LinEq1:  Iter=  2 NonCon=     1 RMS=4.40D-09 Max=6.68D-08 NDo=     1
LinEq1:  Iter=  3 NonCon=     1 RMS=2.64D-09 Max=3.13D-08 NDo=     1
LinEq1:  Iter=  4 NonCon=     1 RMS=1.95D-09 Max=2.42D-08 NDo=     1
LinEq1:  Iter=  5 NonCon=     1 RMS=1.71D-09 Max=2.54D-08 NDo=     1
LinEq1:  Iter=  6 NonCon=     1 RMS=1.21D-09 Max=1.66D-08 NDo=     1
LinEq1:  Iter=  7 NonCon=     1 RMS=7.63D-10 Max=1.13D-08 NDo=     1
LinEq1:  Iter=  8 NonCon=     0 RMS=5.20D-10 Max=6.37D-09 NDo=     1
Linear equations converged to 7.330D-10 7.330D-09 after     8 iterations.
Search did not lower the energy significantly.
No lower point found -- try reversing direction.
Restarting incremental Fock formation.
Search did not lower the energy significantly.
No lower point found -- switch to scaled steepest descent.
Restarting incremental Fock formation.
     Minimum is close to point 10 DX=  0.00D+00 DF=  0.00D+00 DXR=  0.00D+00 DFR=  0.00D+00 which will be used.
LinEq1:  Iter=  0 NonCon=     1 RMS=7.08D-08 Max=7.87D-07 NDo=     1
LinEq1:  Iter=  1 NonCon=     1 RMS=1.34D-08 Max=1.47D-07 NDo=     1
LinEq1:  Iter=  2 NonCon=     1 RMS=4.89D-09 Max=6.60D-08 NDo=     1
LinEq1:  Iter=  3 NonCon=     1 RMS=2.35D-09 Max=2.87D-08 NDo=     1
LinEq1:  Iter=  4 NonCon=     1 RMS=1.74D-09 Max=2.46D-08 NDo=     1
LinEq1:  Iter=  5 NonCon=     1 RMS=1.27D-09 Max=1.70D-08 NDo=     1
LinEq1:  Iter=  6 NonCon=     1 RMS=8.90D-10 Max=1.12D-08 NDo=     1
LinEq1:  Iter=  7 NonCon=     1 RMS=7.69D-10 Max=1.18D-08 NDo=     1
LinEq1:  Iter=  8 NonCon=     1 RMS=8.62D-10 Max=1.07D-08 NDo=     1
LinEq1:  Iter=  9 NonCon=     1 RMS=1.01D-09 Max=1.30D-08 NDo=     1
LinEq1:  Iter= 10 NonCon=     0 RMS=7.41D-10 Max=8.17D-09 NDo=     1
Linear equations converged to 8.552D-10 8.552D-09 after    10 iterations.
Restarting incremental Fock formation.
Search did not lower the energy significantly.
No lower point found -- try reversing direction.

Most recent answer

Prasanta Bandyopadhyay
The University of Manchester
Chamikara Herath , As stated earlier, This is not an error.
1 Recommendation

Popular answers (1)

Daniel Smykowski
Wrocław University of Science and Technology
It seems that your system oscillates. Have you tried to change the scf convergence criteria as well as the algorithm, e.g. switching to qc/xqc? Using ultrafine grid may also be a potential solution. 
4 Recommendations

All Answers (11)

Daniel Smykowski
Wrocław University of Science and Technology
A maxcycle=1024 is pointless for such system, 100-150 should be enough to reach the convergence. Did the geometry optimization end up with success? Or it has just reached the max number of atomic steps? Please provide your input file. 
1 Recommendation
Anthony Nash
University of Oxford
The geometry optimisation worked perfectly fine. 
What is the rationale behind 1024 cycles being pointless for such a system?
Daniel Smykowski
Wrocław University of Science and Technology
It seems that your system oscillates. Have you tried to change the scf convergence criteria as well as the algorithm, e.g. switching to qc/xqc? Using ultrafine grid may also be a potential solution. 
4 Recommendations
Anthony Nash
University of Oxford
Thanks for that suggestion. I'm currently trying qc, I've yet to try xqc or an ultrafine grid (it is on fine, by default). I'll try those. 
Bartosz Trzaskowski
University of Warsaw
"What is the rationale behind 1024 cycles being pointless for such a system?"
The optimizing algorithm in gaussian is really efficient, so (as Daniel suggested) going above 150 (well, perhaps 200) steps rarely makes sense. If you need that many steps it means that either:
- you started very far from the stationary point, which is a waste of computational power and time, or
- your system is oscilating between two geometries, and it won't optimize even in a hige number of steps. So it's better to let it run for a smaller number of steps and then take a look at geometry and/or try different keywords/methods, then let it run for a long time and waste computational powern and time.
2 Recommendations
Anthony Nash
University of Oxford
Hi Bartosz, 
Thanks for that answer. The structure was initially optimised on an ultrafine grid using wb97xd/6-311++G(2df,2p) (Freq found a minimum). I am now trying a single point energy calculation using MP2/aug-cc-pVTZ on an ultrafine grid. Unfortunately, just using the default SCF algorithm and the QC algorithm the system is oscillating, I'm hoping that xqc will make the difference. 
Bartosz Trzaskowski
University of Warsaw
xqc may help. Another idea is to:
- first, change the geometry of the oscilating structure a bit, so it's further from the minimum (e.g. by making one bond longer than usual) and then:
- try a combination o different algorithms (opt=RFO, opt=EF) and different coordinate system (fopt=z-matrix, opt=cartesian - this uses a different optimization approach even if you don't change the geometry specification).  From my experience the latter option is particualrly useful in diffucult cases.
1 Recommendation
Anand M. Verma
Motilal Nehru National Institute of Technology
Hi Anthony
Since ab initio methods take much time as you know already it deals with numerous more variable than what DFT deals with. So I think you need not to worry about it unless DFT is taking less than an hours to complete the same job. I think DFT must've also taken around more than 3 hours. 
I've also done one QST2 optimization with one DFT functional and MP2. DFT took 15-16 hours whereas MP2 took 6 days for the same job.
And I don't agree with 1024 maxcycles. You should've taken 200 cycles then depending upon the optimization trajectory you could've changed your geometry.
Anthony Nash
University of Oxford
Hi A. M. Verma,
Thanks for that information. It was very interesting to note the time difference between the QST2 optimisation using DFT and MP2.
With regards to max cycles I've actually removed that entry because after careful study it turns out the default is 512 if SCF=QC is included (which it is in my case).
Chamikara Herath
Ninewells Hospitals PVT Ltd
Hello I am Chamikara,
I am running a DFT calculation for a Metal-Aminoacid fragment of a protein. my calculation is still running but log file continuously print this.
Restarting incremental Fock formation.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
and this is my input file
%NProcShared=8
%Chk=complex_charged_new_02.chk
#n b3lyp/6-311g(d,p) pop=mk Opt scf=qc
Title
2 1
is this an error or can I let the calculation run and check ?? please advice me
1 Recommendation
Prasanta Bandyopadhyay
The University of Manchester
Chamikara Herath , As stated earlier, This is not an error.
1 Recommendation

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How can I circumvent Bend failed for angle errors in Gaussian?
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  • Anthony NashAnthony Nash
Hi all,
I have a compound with a particular functional group surrounded by water molecules. I am using Gaussian G09 to optimise, first with HF/6-31+g** and then I will progressively move higher per stable structure. Unfortunately, of the twenty different model arrangements (4 functional groups I am interested in and 8, 10, 12, 14, 16) water molecules neighbouring) only three of the twenty ran successfully. The others suffered from the following error (atom numbers will have been different obviously):
GradGradGradGradGradGradGradGradGradGradGradGradGradGradGradGradGradGrad
Berny optimization.
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Tors failed for dihedral    51 -    39 -    70 -    68
Tors failed for dihedral    69 -    68 -    70 -    39
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