There are three quantities that need to be carefully described in order to understand the significance of numerical values generated by MOPAC: Accuracy, reproducibility and precision. First, some definitions:

**Accuracy**: A comparison of the results obtained by
calculation with the values of reference data. This is usually expressed
in terms of Average Unsigned Errors for various quantities, See
accuracy for PM7 for examples and
details.

**Reproducibility**: A method such as PM7 consists of a set of
approximations and a set of parameters. Such methods can be used in
programs such as MOPAC, that can be run on different operating systems, e.g.,
Windows, Linux, and MAC, that run in various architectures, i.e., 32 or 64
bit, using variable numbers of processors (threads), typically 1 to 12 threads.
Results obtained from running the same system using different programs,
OS, architectures, and number of threads, should be the same. In addition,
unless a method is very deliberately changed, results obtained at one time
should be reproducible at any later date.

**Precision**: The number of significant figures in a printed
quantity.

The recent semiempirical methods (PM6 and PM7) in MOPAC are only good for modeling chemical systems, i.e., for predicting the geometries of various species and their heats of formation. For non-chemical properties, such as ionization potentials and dipole moments, or electronics in general, these methods are of low accuracy.

In principle, the results of calculations run using a specific method on a well-defined system should be independent of all variables such as software, operating system, architecture, etc. In practice, this is normally does not happen - there are small differences. Despite the fact that all versions of MOPAC use the same software, and all versions are compiled using the same compiler (the Intel Fortran compiler), there are small differences in the results. These differences can be attributed to a small number of specific factors:

(A) Finite convergence criteria are used. In the SCF calculation, the
optimization of the wavefunction is terminated when a pre-set criterion is
satisfied. In geometry optimization, the process is stopped when the
gradients fall below a pre-set limit. These criteria were selected to minimize
run-times while not significantly compromising the results. If better
convergence is needed, the criteria can be tightened, e.g., by adding `
RELSCF=0.1`
(for the SCF criterion) or `GNORM=0.5` (for geometries), or, in general, by adding
`PRECISE`.

(B) Although the same software is used on all platforms, differences in the operating systems can alter the math used in subtle ways. All arithmetic is carried out to 16 digit precision, but the last digit can have an uncertainty of ±1. This uncertainty can become greater as more and more math is carried out, and in some cases can result in a degradation from 16 digit precision to five or even fewer digits. In most cases, this loss of numerical precision can, perhaps surprisingly, be corrected by tightening up the convergence criteria. With the default criteria, the path from the starting function (electronic or geometric) to the function that satisfies the criteria can differ from one machine to another. But if the criteria are tightened up, the final functions will also be more tightly defined, and differences in the paths taken to reach the optimum point will become less and less important.

(C) Even on one machine, if the same job is run several times, small
differences can be detected. This effect occurs only on multi-processor machines,
and only when multi-thread calculations (the default) are run. In this
case, the differences originate in the changing sequence of operations that can
occur when multi-processing is done. This type of error can be eliminated
by using only one thread, using `THREADS=1`, or
by limiting processes to one core.

(D) There are two very different methods in MOPAC for solving the
self-consistent equations: (1) Conventional SCF methods, that use matrix
algebra, and scale as the cube of the size of the system. (1) A Localized
Molecular Orbital method, MOZYME, that uses Lewis structures to general LMOs.
SCF calculations run using `MOZYME` scale as the size of system to the power 1.4,
i.e., almost linear scaling. MOZYME was written to allow protein systems
(protein plus water plus hetero groups, e.g., substrates) to be run in a
reasonable time. Currently, MOZYME is limited to RHF systems.
Results calculated using conventional methods and using MOZYME should be the
same. In practice, because of different criteria, there are small
differences. These differences can be made arbitrarily small by tightening
criteria (under keyword control) but if that is done, the compute effort would
increase to the point that MOZYME would take the same time as conventional.
In most cases where MOZYME gives a significantly different result from
conventional, and the MOZYME heat of formation is greater than the conventional,
MOZYME has converged on an electronic excited state. In the remaining
cases, the conventional SCF has converged on an electronic excited state/

In this context, precision is the number of significant digits in a quantity
that is printed. The internal precision of the calculation is not readily
accessible to the user, and is therefore essentially irrelevant. In most
cases, the precision used in the output and related files is within two or three
digits of the internal precision of the calculation. If higher or lower
precision is needed, use `AUX` to
output
results to the required precision.

Semiempirical methods have limited accuracy, so although the AUE for
ΔH_{f} might be 4 or 5 kcal/mol, the
precision of the results will typically be 10^{-6} kcal/mol. For
most work, the precision of ΔH_{f}
should be truncated to 0.1 kcal/mol.