Я рассчитываю молекулу в программе Gaussian
Ввожу следующие параметры:
Код: Выделить всё
%mem=14GB
%nprocshared=16
Will use up to 16 processors via shared memory.
%rwf=CuNiFevidrano10Big.rwf
%NoSave
%chk=/home/dan/opt/vichislit/CuNiFevidrano10Big.chk
# opt=(maxcycle=200) freq mp6/sto-3g nosymm scf=(maxcycle=100)
Код: Выделить всё
Symmetry not used in FoFCou.
Requested convergence on RMS density matrix=1.00D-08 within 128 cycles.
Requested convergence on MAX density matrix=1.00D-06.
Requested convergence on energy=1.00D-06.
No special actions if energy rises.
Integral accuracy reduced to 1.0D-05 until final iterations.
Problem detected with inexpensive integrals.
Switching to full accuracy and repeating last cycle.
Restarting incremental Fock formation.
Restarting incremental Fock formation.
Restarting incremental Fock formation.
Restarting incremental Fock formation.
Restarting incremental Fock formation.
>>>>>>>>>> Convergence criterion not met.
SCF Done: E(UPBE-PBE) = -7605.20808848 A.U. after 129 cycles
NFock=128 Conv=0.21D-03 -V/T= 2.0110
<Sx>= 0.0000 <Sy>= 0.0000 <Sz>= 0.5000 <S**2>= 2.7320 S= 1.2268
<L.S>= 0.00000000000
Annihilation of the first spin contaminant:
S**2 before annihilation 2.7320, after 4.8926
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Minimum is close to point 2 DX= 3.19D-02 DF= -1.53D-01 DXR= 9.61D-02 DFR= 9.33D-03 which will be used.
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.
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.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Minimum is close to point 2 DX= 1.88D-02 DF= -1.87D-03 DXR= 5.89D-02 DFR= 3.48D-03 which will be used.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Minimum is close to point 3 DX= 3.37D-03 DF= -3.96D-04 DXR= 3.26D-02 DFR= 1.06D-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.
Restarting incremental Fock formation.
Accept linear search using points 2 and 3.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
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 2 DX= 4.69D-03 DF= -1.68D-04 DXR= 4.48D-02 DFR= 2.01D-03 which will be used.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
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 2 and 3.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Restarting incremental Fock formation.
Minimum is close to point 2 DX= -2.59D-03 DF= -3.80D-05 DXR= 2.66D-02 DFR= 7.08D-04 which will be used.
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 2 DX= -4.97D-03 DF= -1.23D-04 DXR= 5.24D-02 DFR= 2.75D-03 which will be used.
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 2 DX= 2.00D-03 DF= -1.48D-05 DXR= 1.96D-02 DFR= 3.84D-04 which will be used.
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 2 DX= -3.44D-03 DF= -4.29D-05 DXR= 3.56D-02 DFR= 1.27D-03 which will be used.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Restarting incremental Fock formation.
Minimum is close to point 2 DX= 3.59D-03 DF= -3.56D-05 DXR= 3.47D-02 DFR= 1.20D-03 which will be used.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Accept linear search using points 2 and 3.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
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 2 and 3.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Restarting incremental Fock formation.
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 2 and 3.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Accept linear search using points 2 and 3.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Minimum is close to point 3 DX= 1.08D-02 DF= -4.10D-05 DXR= 5.13D-02 DFR= 2.58D-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.
Restarting incremental Fock formation.
Accept linear search using points 5 and 6.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
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.
Restarting incremental Fock formation.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Minimum is close to point 4 DX= 8.46D-02 DF= -6.11D-05 DXR= 9.56D-02 DFR= 9.51D-03 which will be used.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
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.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Accept linear search using points 5 and 6.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Restarting incremental Fock formation.
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.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
Accept linear search using points 4 and 5.
Gradient too large for Newton-Raphson -- use scaled steepest descent instead.
В нете я только нашёл то, что в данном случае реккомендуется использовать xqc. Но его я уже использую.
Вычисления идут уже 4-ый день.
Прошу порекомендовать ещё более быстрый и менее точный способ оптимизации структуры.
И кстати хотя бы немного, но структура изменилась, а значит оптимизировалась.