Psithon: Structuring an Input File

To allow arbitrarily complex computations to be performed, PSI4 was built upon the Python interpreter. However, to make the input syntax simpler, some pre-processing of the input file is performed before it is interpreted, resulting in Python syntax that is customized for PSI, termed Psithon. In this section we will describe the essential features of the Psithon language. PSI4 is distributed with an extensive test suite, described in section Test Suite and Sample Inputs; the input files for these test cases can be found in the samples subdirectory of the top-level PSI4 source directory and should serve as useful examples.

Physical Constants

For convenience, the Python interpreter will execute the contents of the ~/.psi4rc file in the current user’s home area (if present) before performing any tasks in the input file. This allows frequently used python variables to be automatically defined in all input files. For example, if we repeatedly make use of the universal gravitational constant, the following line could be placed in the ~/.psi4rc file

UGC = 6.67384E-11  # m^3 / kg^-1 s^-2

which would make the variable UGC available in all PSI4 input files. For convenience, the physical constants used within the PSI4 code (which are obtained from NIST CODATA 2014 are also automatically loaded as Psithon variables (before ~/.psi4rc is loaded, so that the user’s ~/.psi4rc values can override the builtins (in the input file, not in the C++ code).

The physical constants used within PSI4, which are automatically made available within all PSI4 input files are in Physical Constants.

In Psithon input files, prepend physical constants with psi_ to prevent clashes with user-defined variables (e.g., psi_h). In PsiAPI mode, access as, e.g., psi4.constants.h.

Memory Specification

By default, PSI4 assumes that 500 MiB of memory are available. While this is enough for many computations, many of the algorithms will perform better if more is available. To specify memory, the memory keyword should be used. The following lines are all equivalent methods for specifying that 2 GB of RAM is available to PSI4:

# all equivalent

memory 2 GB

memory 2000 MB

memory 2000000 kB

Please note that memory can be specified both in IEC binary units (1 KiB = 1024 bytes) and SI units (1 kB = 1000 bytes). PSI4 recognizes and obeys both of them correctly. The units are not case sensitive (Kb and KB are equivalent to kB).

By default, PSI4 performs a “sanity check” when parsing Psithon input files, enforcing a minimum memory requirement of 250 MiB. While it is generally not recomennded to do so, expert users can bypass this check by directly setting the number of bytes availble to PSI4:

# setting available memory to 2 MB
set_memory_bytes(2000000)

Please note that this memory setting only governs the maximal memory usage of the major data structures, and actual total memory usage is slightly higher. This is usually a negligible amount, except when setting tiny memory allowances.

One convenient way to override the PSI4 default memory is to place a memory command in the ~/.psi4rc file (Sec. ~/.psi4rc File). For example, the following makes the default memory 2 GB.

set_memory(2000000000)

However, unless you’re assured of having only one job running on a node at a time (and all nodes on the filesystem with ~/.psi4rc have similar memory capacities), it is advised to set memory in the input file on a per-calculation basis.

That same command can be used for PsiAPI mode:

psi4.set_memory(int(5e8))

PSIthonPsiAPI

set_memory(2000000000)

Note

For parallel jobs, the memory keyword represents the total memory available to the job, not the memory per thread.

Molecule and Geometry Specification

• Molecule and Geometry Specification
  • Coordinates
  • Molecule Keywords
  • Multiple Molecules
  • Ghost Atoms
  • Isotopic Substitution
  • PubChem Database
  • Symmetry
  • Non-Covalently Bonded Molecule Fragments
  • Advanced Python

To add EFP fragments to a molecule, see Molecule Specification.

Job Control Keywords

PSI4 comprises a number of C++ modules that each perform specific tasks and are callable directly from the Python front end. Each module recognizes specific keywords in the input file which control its function. These keywords are detailed in Appendix Keywords by Module. The keywords can be made global, or scoped to apply to certain specific modules. The following examples demonstrate some of the ways that global keywords can be specified:

# all equivalent

set globals basis cc-pVDZ

set basis cc-pVDZ

set globals basis = cc-pVDZ

set basis = cc-pVDZ

set globals{
  basis cc-pVDZ
}

set {
  basis cc-pVDZ
}

set {
  basis = cc-pVDZ
}

Note the lack of quotes around cc-pVDZ, even though it is a string. The Psithon preprocessor automatically wraps any string values in set commands in strings. The last three examples provide a more convenient way for specifying multiple keywords:

set {
  basis = cc-pVDZ
  print = 1
  reference = rhf
}

For arguments that require an array input, standard Python list syntax should be used, viz.:

set {
  docc = [3, 0, 1, 1]
}

List/matrix inputs may span multiple lines, as long as the opening [ is on the same line as the name of the keyword.

Any of the above keyword specifications can be scoped to individual modules, by adding the name of the module after the set keyword. Omitting the module name, or using the name global or globals will result in the keyword being applied to all modules. For example, in the following input

molecule{
  o
  h 1 roh
  h 1 roh 2 ahoh

  roh = 0.957
  ahoh = 104.5
}

set basis cc-pVDZ
set ccenergy print 3
set scf print 1
energy('ccsd')

the basis set is set to cc-pVDZ throughout, the SCF code will have a print level of 1 and the ccenergy code, which performs coupled cluster computations, will use a print level of 3. In this example a full CCSD computation is performed by running the SCF code first, then the coupled cluster modules; the energy() Python helper function ensures that this is performed correctly. Note that the Python interpreter executes commands in the order they appear in the input file, so if the last four commands in the above example were to read

set basis cc-pVDZ
energy('ccsd')
set ccenergy print 3
set scf print 1

the commands that set the print level would be ineffective, as they would be processed after the CCSD computation completes.

In PsiAPI mode, one can use the command set_options() like below for general and module-specific options. Note that these values should be of correct type, strings for strings, floats for floats like convergences. The function ~psi4.core.clean_options that reinitializes all options may also be useful to separate calculations in a PsiAPI session.

psi4.set_options({
    'scf_type': 'pk',
    'e_convergence': 1.e-5,
    'soscf': True,
    'optking__geom_maxiter': 50
})

Basis Sets

• Basis Sets
  • Built-In Basis Sets
  • Mixing Basis Sets
  • Decontracted Basis Sets
  • User-Defined Basis Sets
  • Inputting effective core potentials (ECPs)

PSI Variables

To harness the power of Python, PSI4 makes the most pertinent results of each computation available to the Python interpreter for post-processing. To demonstrate, we can embellish the previous example of H₂ and H atom:

molecule h2 {
  H
  H 1 0.9
}

set basis cc-pvdz
set reference rhf
h2_energy = energy('scf')

molecule h {
  H
}

set basis cc-pvdz
set reference uhf
h_energy = energy('scf')

D_e = psi_hartree2kcalmol * (2*h_energy - h2_energy)
print "De=%f" % D_e

The energy() function returns the final result of the computation, the requested total energy in Hartrees, which we assign to a Python variable. The two energies are then converted to a dissociation energy and printed to the output file using standard Python notation.

Generally, there are multiple quantities of interest. Appendix PSI Variables by Module lists PSI variables variables set by each module, and PSI Variables by Alpha defines them. These can be accessed through the get_variable() function. For example, after performing a density fitted MP2 computation, both the spin component scaled energy and the unscaled MP2 energy are made available:

e_mp2 = get_variable('MP2 TOTAL ENERGY')
e_scs_mp2 = get_variable('SCS-MP2 TOTAL ENERGY')

Each module and the Python driver set PSI variables over the course of a calculation. The values for all can be printed in the output file with the input file command print_variables(). Note that PSI variables are cleared at the start of each energy(), etc. in an input file by clean_variables(). So if you run in a single input file a STO-3G FCI followed by a aug-cc-pVQZ SCF followed by a print_variables() command, the last will include SCF TOTAL ENERGY but not FCI TOTAL ENERGY. The entire dictionary of PSI variables can be obtained through get_variables().

Return Values

Most of the usual user computation functions (i.e., energy(), optimize(), and frequency()) return simply the current total energy. Consult the descriptions of other functions in Psithon Functions: Invoking a Calculation for what quantities they return and for what data structures they make available for post-processing. Many users need only deal with the simple return form for the computation functions.

# E is total energy float
# G is gradient array
# H is hessian array
# wfn is class instance with many computational details

# simple returns
E = energy(...)
E = optimize(...)
E = frequency(...)
G = gradient(...)  # used by optimize()
H = hessian(...)  # used by frequency()

For more elaborate post-processing of computations, adding return_wfn=True keyword argument additionally returns Wavefunction.

# power user returns
E, wfn = energy(..., return_wfn=True)
E, wfn = optimize(..., return_wfn=True)
E, wfn = frequency(..., return_wfn=True)
G, wfn = gradient(..., return_wfn=True)  # used by optimize()
H, wfn = hessian(..., return_wfn=True)  # used by frequency()

# print gradient array and its rms
wfn.gradient.print_out()
print wfn.gradient().rms()

# format output for other programs
molden(wfn, 'mycalc.molden')

# access array in another format
np.array(wfn.hessian())

Loops

Python provides many control structures, any of which can be used within PSI4 input files. For example, to loop over three basis sets, the following code can be used:

basis_sets = ["cc-pVDZ", "cc-pVTZ", "cc-pVQZ"]
for basis_set in basis_sets:
    set basis = $basis_set
    energy('scf')

The declaration of basis_sets is completely standard Python, as is the next line, which iterates over the list. However, because the Psithon preprocessor wraps strings in quotes by default, we have to tell it that basis_set is a Python variable, not a string, by prefixing it with a dollar sign.

The geometry specification supports delayed initialization of variable, which permits potential energy scans. As an example, we can scan both the angle and bond length in water:

molecule h2o{
  O
  H 1 R
  H 1 R 2 A
}

Rvals = [0.9, 1.0, 1.1]
Avals = range(102, 106, 2)

set basis cc-pvdz
set scf e_convergence=11
for R in Rvals:
    h2o.R = R
    for A in Avals:
        h2o.A = A
        energy('scf')

The declarations of Rvals and Avals are both completely standard Python syntax. Having named our molecule h2o we can then set the values of R and A within the loops. Note that we do not need the dollar sign to access the Python variable in this example; that is required only when using Python variables with the set keyword.

Cartesian geometries, because of details of the geometry update process, need to be specified within the loop(s) along with their basis set when geometry scans are performed. See scf4 for analogous Z-matrix and Cartiesian scans.

Tables of Results

The Psithon function psi4.driver.p4util.Table has been removed, as the Python ecosystem provides many more flexible alternatives. An example tabulating a potential energy surface scan for water with Pandas is shown below:

molecule h2o {
  O
  H 1 R
  H 1 R 2 A
}

Rvals=[0.9,1.0,1.1]
Avals=range(100,103,2)

rows = []
table = []

set basis cc-pvdz

for R in Rvals:
    h2o.R = R
    for A in Avals:
        h2o.A = A
        energy('mp2')
        escf = variable('SCF TOTAL ENERGY')
        edfmp2 = variable('MP2 TOTAL ENERGY')
        escsmp2 = variable('SCS-MP2 TOTAL ENERGY')
        rows.append((R, A))
        table.append([escf, escsmp2, edfmp2])

import pandas as pd
df = pd.DataFrame(table, columns = ["E(SCF)", "E(SCS)", "E(DFMP2)"], index=rows)
print(df)

#                E(SCF)     E(SCS)   E(DFMP2)
# (0.9, 100) -76.020680 -76.217006 -76.221189
# (0.9, 102) -76.021305 -76.217439 -76.221605
# (1.0, 100) -76.021264 -76.224987 -76.228727
# (1.0, 102) -76.021460 -76.224946 -76.228668
# (1.1, 100) -75.990195 -76.201891 -76.205087
# (1.1, 102) -75.990085 -76.201498 -76.204676

Python Wrappers

The Python foundations of the PSI4 driver and Psithon syntax permit many commonly performed post-processing procedures to be integrated into the PSI4 suite.

As seen in the neon dimer example from the tutorial section, the nbody() wrapper provides automatic computation of counterpoise-corrected interaction energies between two molecules. For example,

energy('mp2', bsse_type='cp')

will compute the counterpoise-corrected density-fitted MP2 interaction energy between two molecules.

PSI4 also provides the cbs() wrapper, which automatically computes a complete-basis-set extrapolation (and automatically sets up the computations with different basis sets required to do the extrapolation). For example,:

# all equivalent

energy('mp2', corl_basis='cc-pv[dt]z', corl_scheme=corl_xtpl_helgaker_2)

energy('mp2/cc-pv[dt]z')

will compute a 2-point Helgaker extrapolation of the correlation energy using the cc-pVDZ and cc-pVTZ basis sets (with method MP2) and add this extrapolated correlation energy to the Hartree–Fock energy in the largest basis (cc-pVTZ). cbs() can be configured behind-the-scenes with explicit arguments, as in the first example, or the convenience syntax of the equivalent second example can be used.

Another very useful and powerful feature of PSI4 is the ability to compute results on entire databases of molecules at a time, as provided by the database() wrapper. For example,:

database('mp2', 'S22', cp=1, benchmark='S22B')

will perform DF-MP2 counterpoise-corrected interaction energies (cp=1) on all members of Hobza’s S22 database set of van der Waals dimers, and then compare the results against the S22B benchmark energies. Built-in databases include S22, A24, HTBH, HBC6, HSG, S22by5, S66, JSCH, NCB31, S66by8, and NBC10, among others.

These wrapper functions are discussed separately in Psithon Functions: Invoking a Calculation. Note that the options documented for Python functions are placed as arguments in the command that calls the function, not in the set {...} block or with any other set command.