the experiments and theory have to go hand in hand to solve materials design
problem in a reliable and efficient way.
One of recent examples of the synergistic effort of the experiments and
theory is machine learning. People are trying to solve materials design problems
using machine learning. However, the reliability of machine learning for
different fields is different. For example, machine learning in astronomy is reasonably
reliable because the experimental data is provided only by a very few
telescopes which are very well tested. On the other hand, machine learning
in materials design suffers with a lot of ambiguity, for example, variations in
pseudopotentials, different electronic structure codes, plethora of unreliable
experimental data etc. Therefore, I personally feel that the dream of solving
materials design problem with machine learning is very far from realizable in
the near future and we still have to resort to more fundamental science than
blindly using computers.