CCL:G: Interaction of QC software components via text files

 What you describe can be achieved by using the Atomic Simulation
 Environment (ASE). The peer-reviewed article for ASE can be found here
 and main
 webpage found here <>;. ASE is a Python-based
 code meant to manipulate, run, and analyse quantum-chemical calculations.
 ASE has a built-in set of optimization routines (described here
 <>;), and
 quantum-chemical program of your choice can be used just to calculate the
 energies and forces required for ASE to update the atomic positions. You
 then do not have to rely on the optimizers built into Gaussian, and it
 operates using the exact same workflow you proposed. ASE does support
 Gaussian as a calculator (as listed here
 but it is currently lacking documentation and would probably require some
 modifications to suit all your needs.
 In principle though, this would be an excellent choice, and I would
 personally recommend giving ASE a try before building something on your own
 > from scratch. Since ASE is written in Python, it would be fairly trivial to
 set up your script to run the DFT calculation (using ASE-based optimizers
 and a Gaussian "calculator") and then feed whatever features you'd
 into a Python-based neural network, such as one built with scikit-learn.
 On Wed, Sep 26, 2018 at 2:13 PM Grigoriy Zhurko reg_zhurko/ <owner-chemistry#,> wrote:
 > Sent to CCL by: Grigoriy Zhurko []
 >   I suppose it is well-known that the geometry optimization in Gaussian is
 > sometimes accompanied with serious problems, and the users have to invent
 > their own ways of solving them. Besides that, the energy of the final
 > (optimized) geometry computed with Gaussian is often slightly higher than
 > the energy of the penultimate (previous) step. This is usually not a big
 > problem, but this is slightly annoying (unless you use my program
 > Chemcraft) ).
 >   My question is, whether it is possible to implement a third-party
 > algorithm of geometry optimization, which implies invoking the Gaussian by
 > a third-party program. I mean that the third-party program generates a
 > Gaussian input file (.gjf) with single point and gradient computation, then
 > Gaussian processes this job and generates the output file, then the program
 > reads and parses this output file, then it predicts the coordinates of the
 > next step and runs Gaussian again, etc. until the energy minimum is
 >   If such an approach is possible, I will probably try to implement the
 > following things:
 >  - geometry optimization and frequencies computation with CCSD(T)/CBS, or
 > even a composite method like FPD;
 >  - performing several computations with different DFT functionals at same
 > geometry, and passing their results into a neural network to obtain a very
 > accurate energy.
 >   So, is this possible to implement such interaction of a third-party
 > program with Gaussian (preferably the Windows version), or other QC codes?
 > Grigoriy Zhuko>