Question
Asked 3 January 2021

What to do with this Gaussian Convergence error?

Hi! I'm kind of a beginner with computational chemistry and I\m trying to study the interaction between copper ion and a graphitic carbon nitride quantum dot. the calculation converges with HF but when I try using DFT the software keeps showing me these messages
EnCoef did 100 forward-backward iterations gaussian
EnCoef did100 forward-backward iterations gaussian
EnCoef did 80 forward-backward iterations gaussian
rare condition: small coef for last iteration: -0.433d-15
and repeats
I tried different functionals with different basis sets but didn't work for me
any help?

Most recent answer

Mohammed Nooraldeen Al-Qattan
Universiti Sains Malaysia
zaheer mahmod, thank you

All Answers (9)

Md. Masud Parvez
Bangabandhu Sheikh Mujibur Rahman Aviation and Aerospace University Lalmonirhat; Bangladesh
Melek Hajji, very appropriate answer, thank you very much for your potential answer, also I am benefited from this answer.
1 Recommendation
Zaheer Masood
University of Oklahoma
Your input structure shows that you use fragments. In this case, generate a guess for wavefunction and use that guess to optimize your structure. Due to these fragments, I think your calculations are long. In the Gaussian manual, example 9.7 on page No 447 is helpful. (Exploring chemistry with electronic structure Methods, 3rd Ed.)
2 Recommendations
Kamal ES Nassar
University of South Florida
Dear Zaheer
by generating guess, do you mean to carry out the calculation with a low level of theory then use this guess in the DFT? or there is another way?
1 Recommendation
Zaheer Masood
University of Oklahoma
Use same basis set. see attached procedure
2 Recommendations
D. B. Dhangar
BSSPM'S ACS COLLEGE SONGIR,DHULE
Convergence errors occur due to the difference between a fully covereged solution to finite number of gird points&sol. thathas not totally sure
Convergence , if the convergence test is dissolved prematurely than errors arise.
Kan Xiaonan
Qingdao University of Science and Technology
Have u solved this problem? I did similar calculation by G16 with fragments containing Ti, "EnCoef did 100 forward-backward iterations" just repeated.
Alexis Otero-Calvis
Universidade Internacional do Cuanza
The answer of Melek Hajji is the best solution. Did you try to do it?
Mohammed Nooraldeen Al-Qattan
Universiti Sains Malaysia
zaheer mahmod, thank you

Similar questions and discussions

Are their any clear indications that the SCF will not converge for a SPE calculation in Gaussian?
Question
11 answers
  • Anthony NashAnthony Nash
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.
How can I circumvent Bend failed for angle errors in Gaussian?
Question
15 answers
  • 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.
Using GEDIIS/GDIIS optimizer.
Bend failed for angle    39 -    70 -    68
Tors failed for dihedral    15 -    39 -    70 -    68
Tors failed for dihedral    51 -    39 -    70 -    68
Tors failed for dihedral    69 -    68 -    70 -    39
FormBX had a problem.
Error termination via Lnk1e in /usr/local/g09_d01/l103.exe at Tue Mar 24 10:12:24 2015.
When I investigate this further, the first failure (Bend on the angle 39, 70, 68) is an angle between the oxygen and hydrogen of a water, followed by the hydrogen of a hydroxyl group. It turns out to be exactly 180 degrees. I understand the optimisation algorithm in gaussian struggles with inline angle. 
Surely, restarting with a different arrangement to the water molecules isn't the best or only solution? Any tips or suggestions are very grateful.
Thanks
Anthony

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