4 ms·
Regarding the last point: try applying libraries like Quantum espresso [1] or CP2K [2] to real world problems and apply machine learning with the solutions they
by abcxx99 7y ago
Regarding the last point: try applying libraries like Quantum espresso [1] or CP2K [2] to real world problems and apply machine learning with the solutions they provide to a given problem. There's a tremendous amount of academic research being done in this direction but try and take these libraries and make them useful for real world applications.
[1] https://github.com/QEF/q-e https://github.com/QEF/q-e
[2] https://github.com/cp2k/cp2k https://github.com/cp2k/cp2k
- mariushn 7y agoThanks! Interesting, I'll look into both
- mxcrossb 7y agoAs someone working in this field, I disagree. The state of these codes is that if you put garbage in, you get garbage out. Without a strong theoretical background, you’re generating junk. Of course, if he’s willing to spend several years developing his knowledge of the field, there is for sure room to help. If not in your suggested machine learning area, instead perhaps in improving the quality and usability of the code. But instead I would recommend he start from something like the NOMAD database, where the calculations have already been run by more knowledgeable people. Then he can focus on the analysis side.
- abcxx99 7y agoOf course you need to develop domain knowledge. But there's tremendous opportunity lying in these libraries, you just have to spend the time looking for it.