added the possibility to have a gradient based convergence in casscf

devel/casscf/EZFIO.cfg

@@ -10,12 +10,6 @@ doc: Calculated |FCI| energy + |PT2|
 interface: ezfio
 size: (determinants.n_states)
 
-[cisd_guess]
-type: logical
-doc: If true, the CASSCF starts with a CISD wave function
-interface: ezfio,provider,ocaml
-default: True 
-
 [state_following_casscf]
 type: logical                                                                                                                                
 doc: If |true|, the CASSCF will try to follow the guess CI vector and orbitals
@@ -48,3 +42,10 @@ type: logical
 doc: If true, the two-rdm are computed with a fast algo
 interface: ezfio,provider,ocaml
 default: True 
+
+[criterion_casscf]
+type: character*(32)
+doc: choice of the criterion for the convergence of the casscf: can be  energy  or  gradients  
+interface: ezfio, provider, ocaml
+default: energy
+

devel/casscf/casscf.irp.f

@@ -41,12 +41,19 @@ subroutine run
     call write_double(6,norm_grad_vec2_tab(3), '   Active-virtual gradients = ')
     call write_double(6,energy_improvement, 'Predicted energy improvement = ')
 
+    if(criterion_casscf == "energy")then
      converged = dabs(energy_improvement) < thresh_scf
+    else if (criterion_casscf == "gradients")then
+     converged = norm_grad_vec2 < thresh_scf
+    else 
+     converged = dabs(energy_improvement) < thresh_scf
+    endif
     pt2_max = dabs(energy_improvement / pt2_relative_error)
 
     mo_coef = NewOrbs
     mo_occ  = occnum  
     call save_mos
+    if(.not.converged)then
      iteration += 1
      N_det = max(N_det/2 ,N_states)
      psi_det = psi_det_sorted
@@ -58,6 +65,7 @@ subroutine run
       state_following_casscf = state_following_casscf_save 
       touch state_following_casscf
      endif
+    endif
 
   enddo
 

devel/casscf/neworbs.irp.f

@@ -54,9 +54,9 @@ END_PROVIDER
   ! Eigenvectors/eigenvalues of the single-excitation matrix
   END_DOC
   if(nMonoEx+1.gt.n_det_max_full)then
-!   if(bavard)then
+   if(bavard)then
     print*,'Using the Davidson algorithm to diagonalize the SXmatrix'
-!   endif
+   endif
    double precision, allocatable :: u_in(:,:),energies(:)
    allocate(u_in(nMonoEx+1,n_states_diag),energies(n_guess_sx_mat))
    call davidson_diag_sx_mat(n_guess_sx_mat, u_in, energies)
@@ -70,9 +70,9 @@ END_PROVIDER
     enddo
    enddo
   else
-!   if(bavard)then
+   if(bavard)then
     print*,'Diagonalize the SXmatrix with Jacobi'
-!   endif
+   endif
    call lapack_diag(SXeigenval,SXeigenvec,SXmatrix,nMonoEx+1,nMonoEx+1)
   endif
   if (bavard) then
@@ -84,9 +84,9 @@ END_PROVIDER
      write(6,*) ' 4 - ',SXeigenval(4),SXeigenvec(1,4)
      write(6,*) ' 5 - ',SXeigenval(5),SXeigenvec(1,5)
     endif
-  endif
     write(6,*)
     write(6,*) ' SXdiag : lowest eigenvalue = ',SXeigenval(1)
+  endif
 END_PROVIDER
 
  BEGIN_PROVIDER [real*8, energy_improvement]