I wanted to do TS search upon the growth of a Cu2O crystal.
Can anyone suggest another technique or an appropriate way to use DFT calculation for Transition state calculation for the formation Cu2O or CuO crystal
A TS search requires an initial and final structure. For Cu2O and CuO, your final structure is well-defined but your initial structure is not. What would you start from? Vacuum? A Cu atom? An O atom or O2 molecule? Also, I'm not aware of any method that can be used to do variable-composition TS searches. It's an interesting idea but just that unfortunately.
Another option would be to do DFT molecular dynamics (MD) in the grand canonical ensemble (https://en.wikipedia.org/wiki/Grand_canonical_ensemble). However, there are two problems associated with this. First, like variable-composition TS searches, I'm not aware of any software package that offers this functionality. Second, DFT MD likely cannot access the relatively long timescales over which crystal growth takes place (i.e. μs to days). This is another interesting idea but computationally infeasible at the moment.
If you are clinging to DFT as your method of choice, you could try looking for/generating cluster expansion models for Cu2O or CuO (see equation 1 in this paper by my PI, https://iopscience.iop.org/article/10.1088/1361-651X/aa7e0c/pdf). Additionally, if you have a good intuition of the types of growth steps possible, you could calculate the barriers for those steps using DFT and perform kinetic Monte Carlo simulations (see this paper by my former PI for inspiration, https://www.sas.upenn.edu/rappegroup/research/Papers/Kim11p076102.pdf).
Now, if you aren't beholden to DFT, then you might be able to access the μs timescale using classical molecular dynamics (this is a GREAT code, https://lammps.sandia.gov). To access timescales longer than μs, you could also experiment with metadynamics (https://www.plumed.org, which can be used with a number of codes, including DFT ones).
As a final note, I recently published a method for automatically predicting surface phase diagrams using DFT-powered grand canonical Monte Carlo simulations (Article Automatic Prediction of Surface Phase Diagrams Using Ab Init...
). While this doesn't exactly solve your problem, we are developing functionality that will allow the study of nanoparticle growth. One caveat of Monte Carlo methods in general is that they don't provide information about time evolution but rather configurational (e.g. structure and composition) evolution. As such, you can learn something about the stable intermediates in the growth process but not necessarily about the temporal features of growth.
I know this answer is long but I hope that it gives you some fresh ideas for achieving your goals. Best of luck and feel free to ask any follow-up questions that you may have.