SAPT(DFT) MO disk I/O optimization & Exchange-Dispersion scaling scheme update

doc/sphinxman/source/bibliography.rst

@@ -524,6 +524,14 @@ Bibliography
    A. Hesselmann and T. Korona,
    *J. Chem. Phys.* **141**, 094107 (2014).
 
+.. [Podeszwa:2006:400]
+   R. Podeszwa, R. Bukowski, and K. Szalewicz,
+   *J. Chem. Theory Comput.* **2**, 400 (2006). 
+
+.. [Xie:2022:024801]
+   Y. Xie, D. G. A. Smith, and C. D. Sherrill,
+   *J. Chem. Phys.*, **157**, 024801 (2022)
+
 .. [Hylleraas:1930:209]
    E. Hylleraas
    *Z. Phys.* **65**, 209 (1930).
@@ -613,4 +621,3 @@ Bibliography
    "Two new unitary-invariant and size-consistent perturbation theoretical approaches to the electron correlation energy",
    R. F. Fink
    *Chem. Phys. Lett.*, **428**, 461 (2006)
-

doc/sphinxman/source/sapt.rst

@@ -300,8 +300,8 @@ the molecule as given, and the molecule after one electron has been removed
 (e.g., the energy difference between a neutral molecule and its cation).
 
 The values of GRAC shifts should be given in Hartree. For the example above,
-the GRAC shift value of both molecules are calculated to be 0.1307, and the 
-user would specify them using the following keywords::
+the GRAC shift value of both molecules are calculated to be 0.1307 (using experimental
+IP from CCCBDB), and the user would specify them using the following keywords::
 
 
     set globals {
@@ -530,12 +530,20 @@ term needs to be computed with orbital response for good accuracy,
 and it is recommended to enable |sapt__sapt_dft_do_hybrid| (set to
 ``True`` by default). The coupled exchange-dispersion energy is usually
 estimated by scaling from the uncoupled value either by a fitted fixed
-value as suggested by [Hesselmann:2014:094107]_, or by the ratio of
-coupled and uncoupled dispersion energy. This can be controlled by
-keyword |sapt__sapt_dft_exch_disp_scale_scheme|, with ``FIXED`` being
-the former approach, ``DISP`` being the latter and ``NONE`` for not
-scaling and use the uncoupled exchange-dispersion energy directly.
-
+value (suggested initially by [Hesselmann:2014:094107]_ for a local Hartree--Fock (LHF) formulation and then revised
+by [Xie:2022:024801]_ for non-LHF) or
+by the ratio of
+coupled and uncoupled dispersion energy (suggested by [Podeszwa:2006:400]_ ). 
+This can be controlled by keyword |sapt__sapt_dft_exch_disp_scale_scheme|, 
+with ``FIXED`` using the Hesselmann/Xie approach (|PSIfours| default prior 
+to Nov 2022), ``DISP`` using the Podeszwa approach (|PSIfours| default after Nov 2022),
+or ``NONE`` for not scaling and using the uncoupled exchange-dispersion 
+energy directly.
+
+.. warning:: Since Nov 2022, the defaults of options |sapt__sapt_dft_exch_disp_scale_scheme| and |sapt__sapt_dft_exch_disp_fixed_scale|
+             have been changed. Before, the former defaulted to ``FIXED`` with Hesselmann value of 0.686 for the latter. Now, the former defaults to ``DISP`` and should you instead select ``FIXED``, the default for the latter is the Xie value of 0.770. This might cause
+             an older version of |PSIfour| to produce a different value of
+             exchange-dispersion energy from the latest version.
 
 Basic Keywords for SAPT(DFT) 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~

psi4/driver/procrouting/proc_util.py

@@ -211,7 +211,7 @@ def print_ci_results(ciwfn, rname, scf_e, ci_e, print_opdm_no=False):
 
 def prepare_sapt_molecule(sapt_dimer: core.Molecule, sapt_basis: str) -> Tuple[core.Molecule, core.Molecule, core.Molecule]:
     """
-    Prepares a dimer molecule for a SAPT computations. Returns the dimer, monomerA, and monomerB.
+    Prepares a dimer molecule for a SAPT computation. Returns the dimer, monomerA, and monomerB.
     """
 
     # Shifting to C1 so we need to copy the active molecule

psi4/driver/procrouting/sapt/sapt_mp2_terms.py

@@ -96,15 +96,18 @@ def df_fdds_dispersion(primary, auxiliary, cache, is_hybrid, x_alpha, leg_points
         core.print_out("   (P|Fxc|Q) Thresh: % 8.3e\n" % rho_thresh)
 
     # Build object
+    core.timer_on("Form FDDS object")
     df_matrix_keys = ["Cocc_A", "Cvir_A", "Cocc_B", "Cvir_B"]
     fdds_matrix_cache = {key: cache[key] for key in df_matrix_keys}
 
     df_vector_keys = ["eps_occ_A", "eps_vir_A", "eps_occ_B", "eps_vir_B"]
     fdds_vector_cache = {key: cache[key] for key in df_vector_keys}
 
     fdds_obj = core.FDDS_Dispersion(primary, auxiliary, fdds_matrix_cache, fdds_vector_cache, is_hybrid)
+    core.timer_off("Form FDDS object")
 
     # Aux Densities
+    core.timer_on("Form xc kernel")
     D = fdds_obj.project_densities([cache["D_A"], cache["D_B"]])
 
     # Temps
@@ -125,11 +128,13 @@ def df_fdds_dispersion(primary, auxiliary, cache, is_hybrid, x_alpha, leg_points
 
     # Nuke the densities
     del D
+    core.timer_off("Form xc kernel")
 
     metric = fdds_obj.metric().clone().to_array()
     metric_inv = fdds_obj.metric_inv().clone().to_array()
 
     # Integrate
+    core.timer_on("Time Integration")
     core.print_out("\n   => Time Integration <= \n\n")
 
     val_pack = ("Omega", "Weight", "Disp20,u", "Disp20", "time [s]")
@@ -235,6 +240,7 @@ def df_fdds_dispersion(primary, auxiliary, cache, is_hybrid, x_alpha, leg_points
 
     Disp20_uc = -1.0 / (2.0 * np.pi) * total_uc
     Disp20_c = -1.0 / (2.0 * np.pi) * total_c
+    core.timer_off("Time Integration")
 
     core.print_out("\n")
     core.print_out(print_sapt_var("Disp20,u", Disp20_uc, short=True) + "\n")