Ask HN: What can I do to accelerate scientific research?

There are a lot of citizen science initiatives you can participate in including folding at home and so on. Your best bet is going to be to find a field you love and find experts in the field to work with and learn from - theres definitely a lot of lack fo software talent in some areas that you'd be able to make a dent into but outside of citizen science initiatives, you need to start with understanding the problems that need to be solved which can be much more technical and difficult to understand than they ultimately could be to solve. Good luck!

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

[2] https://github.com/cp2k/cp2k

Thanks! Interesting, I'll look into both

If you want to help software related AI research, start compiling massive high quality training data sets and giving it away for free. Easier said than done though.

No immediate financial return? I hope you can accept no financial return, period. In general the easiest way for an individual to accelerate general research is through generous funding. But even then it’s not like a slider in a game where you provide more funding and things get done faster. There’s diminishing returns after a point. Not that I’m trying to discourage you, but I hope you’re thinking about it the right way before you waste a lot of time and money.

I suggest talking to actual researchers and asking them what they really need, and give them that. Basically the same as a startup going out and talking to customers. The only research probably being done around here is largely software related, and probably not changing the world much in ways that actually matter.

> indexing all open research with free unlimited access, similar to arxiv-sanity.com but better

This space is pretty crowded, in my opinion.

I don’t know much about biology, but I can tell you that in materials, it’s all about data. The materials design problem and predicting new materials comes down to knowing properties of other materials. A lot of progress has been made by using datasets generated by quantum mechanical calculations by the Materials Project, OQMD, AFLOW and NOMAD, but materials design is tricky because what we want to predict are the outliers that we haven’t seen yet: materials with the highest strength, etc..

There’s value to be created for materials researchers by curating experimental data in a digital, usable form, since so much is locked up in papers, but you really need domain expertise for this and there’s another problem that the experiments are so sparse and have so many features (chemistry, microstructure, thermal history, etc) that people have really only been successful when focusing on particular classes of materials.